diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index eadc67c7..e69de29b 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -1 +0,0 @@
-*       @vim-usds @travis-newby @sampowers-usds @mattbowen-usds
diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml
index d7d800b3..31f44ad3 100644
--- a/.github/workflows/codeql-analysis.yml
+++ b/.github/workflows/codeql-analysis.yml
@@ -45,7 +45,7 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v1
+      uses: github/codeql-action/init@v2
       with:
         languages: ${{ matrix.language }}
         # If you wish to specify custom queries, you can do so here or in a config file.
@@ -56,7 +56,7 @@ jobs:
     # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
     # If this step fails, then you should remove it and run the build manually (see below)
     - name: Autobuild
-      uses: github/codeql-action/autobuild@v1
+      uses: github/codeql-action/autobuild@v2
 
     # ℹ️ Command-line programs to run using the OS shell.
     # 📚 https://git.io/JvXDl
@@ -70,4 +70,4 @@ jobs:
     #   make release
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v1
+      uses: github/codeql-action/analyze@v2
diff --git a/.github/workflows/data-checks.yml b/.github/workflows/data-checks.yml
index 280a6e2a..ac111d2a 100644
--- a/.github/workflows/data-checks.yml
+++ b/.github/workflows/data-checks.yml
@@ -2,9 +2,6 @@
 name: Data Checks
 on:
   pull_request:
-    branches:
-      - main
-      - "**/release/**"
     paths:
       - "data/**"
 jobs:
@@ -16,11 +13,11 @@ jobs:
     strategy:
       matrix:
         # checks all of the versions allowed in pyproject.toml
-        python-version: [3.8, 3.9]
+        python-version: [3.10.15]
     steps:
       # installs Python
       # one execution of the tests per version listed above
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v4
         with:
@@ -31,7 +28,7 @@ jobs:
         uses: hmarr/debug-action@v2
       - name: Load cached Poetry installation
         id: cached-poetry-dependencies
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: ~/.cache/pypoetry/virtualenvs
           key: env-${{ runner.os }}-${{ matrix.python-version }}-${{ hashFiles('**/poetry.lock') }}-${{ hashFiles('.github/workflows/data-checks.yml') }}
diff --git a/.github/workflows/deploy_backend_main.yml b/.github/workflows/deploy_backend_main.yml
new file mode 100644
index 00000000..1a420687
--- /dev/null
+++ b/.github/workflows/deploy_backend_main.yml
@@ -0,0 +1,132 @@
+name: Deploy Backend Main
+on:
+  push:
+    branches: [main]
+    paths:
+      - "data/**"
+env:
+  CENSUS_API_KEY: ${{ secrets.CENSUS_API_KEY }}
+  J40_VERSION_LABEL_STRING: ${{ vars.SCORE_VERSION }}
+
+jobs:
+  generate-score-tiles:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: data/data-pipeline
+    strategy:
+      matrix:
+        python-version: ['3.10']
+    environment: Staging
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v4
+      - name: Print variables to help debug
+        uses: hmarr/debug-action@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Load cached Poetry installation
+        id: cached-poetry-dependencies
+        uses: actions/cache@v4
+        with:
+          path: ~/.cache/pypoetry/virtualenvs
+          key: env-${{ runner.os }}-${{ matrix.python-version }}-${{ hashFiles('**/poetry.lock') }}-${{ hashFiles('.github/workflows/deploy_backend_main.yml') }}
+      - name: Install poetry
+        uses: snok/install-poetry@v1
+      - name: Print Poetry settings
+        run: poetry show -v
+      - name: Install dependencies
+        run: poetry add s4cmd && poetry install
+        if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
+      - name: Configure AWS Credentials
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          aws-access-key-id: ${{ secrets.DATA_DEV_AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.DATA_DEV_AWS_SECRET_ACCESS_KEY }}
+          aws-region: us-east-1
+      - name: Install GDAL/ogr2ogr
+        run: |
+          sudo apt-get update
+          sudo apt-get -y install gdal-bin
+          ogrinfo --version
+      - name: Get Census Data
+        run: |
+          poetry run python3 data_pipeline/application.py pull-census-data -s aws
+      - name: Generate Score
+        run: |
+          poetry run python3 data_pipeline/application.py score-full-run
+      - name: Generate Score Post
+        run: |
+          poetry run python3 data_pipeline/application.py generate-score-post -s aws
+      - name: Confirm we generated the version of the score we think we did
+        if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' || env.J40_VERSION_LABEL_STRING == 'test' }}
+        run: |
+          grep "Identified as disadvantaged due to tribal overlap" data_pipeline/data/score/downloadable/* > /dev/null
+      - name: Confirm we generated the version of the score we think we did
+        if: ${{ env.J40_VERSION_LABEL_STRING == '2.0' || env.J40_VERSION_LABEL_STRING == 'beta' }}
+        run: |
+          grep -v "Identified as disadvantaged due to tribal overlap" data_pipeline/data/score/downloadable/* > /dev/null
+      - name: Generate Score Geo
+        run: |
+          poetry run python3 data_pipeline/application.py geo-score
+      - name: Run smoketest for 1.0
+        if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' }}
+        run: |
+          poetry run pytest data_pipeline/ -m smoketest
+      - name: Deploy Score to Geoplatform AWS
+        run: |
+          poetry run s4cmd put ./data_pipeline/data/score/csv/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/csv --sync-check --recursive --force
+          poetry run s4cmd put ./data_pipeline/files/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --sync-check --recursive --force
+          poetry run s4cmd put ./data_pipeline/data/score/downloadable/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/downloadable --sync-check --recursive --force
+      - name: Deploy 1.0 score post
+        if: ${{ env.J40_VERSION_LABEL_STRING == '1.0' }}
+        run: |
+          curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-shapefile-codebook.zip" -s -f -I -o /dev/null && \
+          curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-communities.xlsx" -s -f -I -o /dev/null && \
+          curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/1.0-communities.csv" -s -f -I -o /dev/null && \
+          curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/cejst-technical-support-document.pdf" -s -f -I -o /dev/null && \
+          curl "${{secrets.DATA_URL}}/data-versions/1.0/data/score/downloadable/draft-communities-list.pdf" -s -f -I -o /dev/null
+      - name: Deploy 2.0 score post
+        if: ${{ env.J40_VERSION_LABEL_STRING == '2.0' }}
+        run: |
+          curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-data-documentation.zip" -s -f -I -o /dev/null && \
+          curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-shapefile-codebook.zip" -s -f -I -o /dev/null
+          curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-communities.xlsx" -s -f -I -o /dev/null && \
+          curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/2.0-communities.csv" -s -f -I -o /dev/null && \
+          curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/cejst-technical-support-document.pdf" -s -f -I -o /dev/null && \
+          curl "${{secrets.DATA_URL}}/data-versions/2.0/data/score/downloadable/draft-communities-list.pdf" -s -f -I -o /dev/null
+      - name: Set timezone for tippecanoe
+        uses: szenius/set-timezone@v2.0
+        with:
+          timezoneLinux: "America/Los_Angeles"
+      - name: Get tippecanoe
+        run: |
+          sudo apt-get install -y software-properties-common libsqlite3-dev zlib1g-dev
+          sudo apt-add-repository -y ppa:git-core/ppa
+          sudo mkdir -p /tmp/tippecanoe-src
+          sudo git clone https://github.com/mapbox/tippecanoe.git /tmp/tippecanoe-src
+      - name: Make tippecanoe
+        working-directory: /tmp/tippecanoe-src
+        run: |
+          sudo /usr/bin/bash -c make
+          mkdir -p /usr/local/bin
+          cp tippecanoe /usr/local/bin/tippecanoe
+          tippecanoe -v
+      - name: Generate Tiles
+        run: |
+          poetry run python3 data_pipeline/application.py generate-map-tiles
+      - name: Deploy Map to Geoplatform AWS
+        run: |
+          poetry run s4cmd put ./data_pipeline/data/score/geojson/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/geojson --sync-check --recursive --force --delete-removed --num-threads=250
+          poetry run s4cmd put ./data_pipeline/data/score/shapefile/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/shapefile --sync-check --recursive --force --delete-removed
+          poetry run s4cmd put ./data_pipeline/data/score/tiles/ s3://${{secrets.S3_DATA_BUCKET}}/data-versions/${{env.J40_VERSION_LABEL_STRING}}/data/score/tiles --sync-check --recursive --force --delete-removed --num-threads=250
+      - name: Invalidate cache on AWS CDN
+        uses: chetan/invalidate-cloudfront-action@master
+        env:
+          DISTRIBUTION: ${{secrets.DATA_CDN_ID}}
+          PATHS: "/*"
+          AWS_REGION: "us-east-1"
+          AWS_ACCESS_KEY_ID: ${{ secrets.CLIENT_DEV_AWS_ACCESS_KEY_ID }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets.CLIENT_DEV_AWS_SECRET_ACCESS_KEY }}
diff --git a/.github/workflows/deploy_frontend_main.yml b/.github/workflows/deploy_frontend_main.yml
new file mode 100644
index 00000000..662edeaa
--- /dev/null
+++ b/.github/workflows/deploy_frontend_main.yml
@@ -0,0 +1,85 @@
+name: Deploy Frontend Main
+on:
+  push:
+    branches: [main]
+    paths:
+      - "client/**/*"
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    environment: Staging
+    defaults:
+      run:
+        working-directory: client
+    strategy:
+      matrix:
+        node-version: [14.x]
+    steps:
+      - uses: actions/checkout@v4
+      - name: Use Node.js ${{ matrix.node-version }}
+        uses: actions/setup-node@v2
+        with:
+          node-version: ${{ matrix.node-version }}
+      - name: Set DESTINATION_FOLDER for main
+        run: |
+          echo "DESTINATION_FOLDER=main" >> $GITHUB_ENV
+      - name: Install
+        run: npm ci
+      - name: Build
+        run: npm run build --if-present
+        env:
+          # See the client readme for more info on environment variables:
+          # https://github.com/usds/justice40-tool/blob/main/client/README.md
+          DATA_SOURCE: cdn
+          # TODO: Update main URL when either is back up
+          SITE_URL: "${{ secrets.SITE_URL }}"
+          MAPBOX_STYLES_READ_TOKEN: "${{ secrets.MAPBOX_STYLES_READ_TOKEN }}"
+      - name: Get directory contents
+        run: ls -la public
+      - name: Lint
+        run: npm run lint
+        # Disabling for now due to jsonlint - TODO: put this back
+      # - name: License Check
+      #   run: npm run licenses
+      - name: Test
+        run: npm test
+      # - name: Check for security vulnerabilities
+      #   run: npm audit --production
+      - name: Upload Artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: J40Static
+          # Upload-artifact does not support the default working directory
+          # See more: https://github.com/actions/upload-artifact/issues/87
+          path: ./client/public
+  deploy:
+    runs-on: ubuntu-latest
+    needs: build
+    environment: Staging
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v4
+      - name: Download Artifacts
+        uses: actions/download-artifact@v4
+        with:
+          name: J40Static
+          path: ./public
+      - name: Set DESTINATION_FOLDER for main
+        run: |
+          echo "DESTINATION_FOLDER=main" >> $GITHUB_ENV
+      - name: Configure AWS Credentials
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          aws-access-key-id: ${{ secrets.CLIENT_DEV_AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.CLIENT_DEV_AWS_SECRET_ACCESS_KEY }}
+          aws-region: us-east-1
+      - name: Deploy to Geoplatform AWS
+        run: aws s3 sync ./public/ s3://${{secrets.S3_WEBSITE_BUCKET}}/justice40-tool/${{env.DESTINATION_FOLDER}} --delete
+      - name: Invalidate cache on AWS CDNs
+        uses: chetan/invalidate-cloudfront-action@master
+        env:
+          DISTRIBUTION: ${{secrets.WEB_CDN_ID}}
+          PATHS: "/*"
+          AWS_REGION: "us-east-1"
+          AWS_ACCESS_KEY_ID: ${{ secrets.CLIENT_DEV_AWS_ACCESS_KEY_ID }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets.CLIENT_DEV_AWS_SECRET_ACCESS_KEY }}
\ No newline at end of file
diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index cceff8ee..d95b1488 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -17,9 +17,9 @@ jobs:
       working-directory: ./client
     steps:
       - name: Checkout
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
       - name: Cypress nightly tests 🌃
-        uses: cypress-io/github-action@v2
+        uses: cypress-io/github-action@v6
         with:
           working-directory: ${{env.working-directory}}
           start: npm start
diff --git a/.github/workflows/markdown-link-check.yml b/.github/workflows/markdown-link-check.yml
index 780dd59c..dfe4d024 100644
--- a/.github/workflows/markdown-link-check.yml
+++ b/.github/workflows/markdown-link-check.yml
@@ -1,15 +1,16 @@
-name: Check Markdown links
+name: Check Markdown Links
 on:
   pull_request:
-    # The branches below must be a subset of the branches above
     branches: [ main ]
-    # Run only if the pull request changes a markdown file:
-    # https://docs.github.com/en/actions/learn-github-actions/workflow-syntax-for-github-actions#onpushpull_requestpaths
     paths:
       - '**.md'
 jobs:
-  markdown-link-check:
-    runs-on: ubuntu-latest
+  check-links:
+    name: runner / linkspector
+    runs-on: ubuntu-22.04
     steps:
-    - uses: actions/checkout@v2
-    - uses: gaurav-nelson/github-action-markdown-link-check@v1
+      - uses: actions/checkout@v4
+      - name: Run linkspector
+        uses: umbrelladocs/action-linkspector@v1
+        with:  
+          fail_on_error: true
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 36c2ca61..07d1cb50 100644
--- a/.gitignore
+++ b/.gitignore
@@ -150,3 +150,6 @@ node_modules
 .python-version
 .DS_Store
 temp_dir
+
+# asdf tooling
+.tool-versions
diff --git a/client/.env.development b/client/.env.development
index 4f232c01..a9594395 100644
--- a/client/.env.development
+++ b/client/.env.development
@@ -3,14 +3,14 @@
 
 # Feature Tiles env variables:
 # The TILES_BASE_URL will be determined by the DATA_SOURCE env variable
-GATSBY_CDN_TILES_BASE_URL=https://static-data-screeningtool.geoplatform.gov
+GATSBY_CDN_TILES_BASE_URL=https://dig0wsohit6js.cloudfront.net
 GATSBY_LOCAL_TILES_BASE_URL=http://localhost:5000/data/data-pipeline
 
 GATSBY_DATA_PIPELINE_SCORE_PATH_LOCAL=data_pipeline/data/score
 GATSBY_DATA_PIPELINE_TRIBAL_PATH=data-pipeline/data/tribal
 
 GATSBY_BETA_SCORE_PATH = data-versions/beta/data/score
-GATSBY_1_0_SCORE_PATH = data-versions/1.0/data/score
+GATSBY_2_0_SCORE_PATH = data-versions/2.0/data/score
 
 GATSBY_FILE_DL_PATH_BETA_COMMUNITIES_LIST_XLS=downloadable/beta-communities.xlsx
 GATSBY_FILE_DL_PATH_BETA_COMMUNITIES_LIST_CSV=downloadable/beta-communities.csv
@@ -19,17 +19,17 @@ GATSBY_FILE_DL_PATH_BETA_DATA_DOC=downloadable/beta-data-documentation.zip
 GATSBY_FILE_DL_PATH_BETA_TRAINING_SLIDES_PPT=downloadable/technical-training-slides.pptx
 
 
-GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_XLS=downloadable/1.0-communities.xlsx
-GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_CSV=downloadable/1.0-communities.csv
-GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_PDF=downloadable/1.0-communities-list.pdf
-GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_ES_PDF=downloadable/1.0-communities-list-es.pdf
-GATSBY_FILE_DL_PATH_1_0_SHAPE_FILE_ZIP=downloadable/1.0-shapefile-codebook.zip
-GATSBY_FILE_DL_PATH_1_0_INSTRUCT_PDF=downloadable/CEQ-CEJST-Instructions.pdf
-GATSBY_FILE_DL_PATH_1_0_INSTRUCT_ES_PDF=downloadable/CEQ-CEJST-Instructions-es.pdf
-GATSBY_FILE_DL_PATH_1_0_COMP_CHART_PDF=downloadable/total-comparison-chart.pdf
-GATSBY_FILE_DL_PATH_1_0_TSD_PDF=downloadable/1.0-cejst-technical-support-document.pdf
-GATSBY_FILE_DL_PATH_1_0_TOOL_COMP_PDF=downloadable/1.0-climate-and-environmental-burden-tool-comparison.pdf
-GATSBY_FILE_DL_PATH_1_0_M_23_09_SIGNED_PDF=downloadable/M-23-09_Signed_CEQ_CPO_es.pdf
+GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_XLS=downloadable/2.0-communities.xlsx
+GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_CSV=downloadable/2.0-communities.csv
+GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_PDF=downloadable/2.0-communities-list.pdf
+GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_ES_PDF=downloadable/2.0-communities-list-es.pdf
+GATSBY_FILE_DL_PATH_2_0_SHAPE_FILE_ZIP=downloadable/2.0-shapefile-codebook.zip
+GATSBY_FILE_DL_PATH_2_0_INSTRUCT_PDF=downloadable/CEQ-CEJST-Instructions.pdf
+GATSBY_FILE_DL_PATH_2_0_INSTRUCT_ES_PDF=downloadable/CEQ-CEJST-Instructions-es.pdf
+GATSBY_FILE_DL_PATH_2_0_COMP_CHART_PDF=downloadable/total-comparison-chart.pdf
+GATSBY_FILE_DL_PATH_2_0_TSD_PDF=downloadable/2.0-cejst-technical-support-document.pdf
+GATSBY_FILE_DL_PATH_2_0_TOOL_COMP_PDF=downloadable/2.0-climate-and-environmental-burden-tool-comparison.pdf
+GATSBY_FILE_DL_PATH_2_0_M_23_09_SIGNED_PDF=downloadable/M-23-09_Signed_CEQ_CPO_es.pdf
 
 GATSBY_FILE_DL_PATH_TSD_ES_PDF=downloadable/cejst-technical-support-document-es.pdf
 GATSBY_FILE_DL_PATH_HOW_TO_COMMUNITIES_PDF=downloadable/draft-communities-list.pdf
diff --git a/client/.env.production b/client/.env.production
index 540744cb..a925c4a2 100644
--- a/client/.env.production
+++ b/client/.env.production
@@ -3,12 +3,12 @@
 
 # Feature Tiles env variables:
 # The TILES_BASE_URL will always point to the CDN
-GATSBY_CDN_TILES_BASE_URL=https://static-data-screeningtool.geoplatform.gov
+GATSBY_CDN_TILES_BASE_URL=https://dig0wsohit6js.cloudfront.net
 
 GATSBY_DATA_PIPELINE_TRIBAL_PATH=data-pipeline/data/tribal
 
 GATSBY_BETA_SCORE_PATH = data-versions/beta/data/score
-GATSBY_1_0_SCORE_PATH = data-versions/1.0/data/score
+GATSBY_2_0_SCORE_PATH = data-versions/2.0/data/score
 
 GATSBY_FILE_DL_PATH_BETA_COMMUNITIES_LIST_XLS=downloadable/beta-communities.xlsx
 GATSBY_FILE_DL_PATH_BETA_COMMUNITIES_LIST_CSV=downloadable/beta-communities.csv
@@ -16,17 +16,17 @@ GATSBY_FILE_DL_PATH_BETA_SHAPE_FILE_ZIP=downloadable/beta-shapefile-codebook.zip
 GATSBY_FILE_DL_PATH_BETA_DATA_DOC=downloadable/beta-data-documentation.zip
 GATSBY_FILE_DL_PATH_BETA_TRAINING_SLIDES_PPT=downloadable/technical-training-slides.pptx
 
-GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_XLS=downloadable/1.0-communities.xlsx
-GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_CSV=downloadable/1.0-communities.csv
-GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_PDF=downloadable/1.0-communities-list.pdf
-GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_ES_PDF=downloadable/1.0-communities-list-es.pdf
-GATSBY_FILE_DL_PATH_1_0_SHAPE_FILE_ZIP=downloadable/1.0-shapefile-codebook.zip
-GATSBY_FILE_DL_PATH_1_0_INSTRUCT_PDF=downloadable/CEQ-CEJST-Instructions.pdf
-GATSBY_FILE_DL_PATH_1_0_INSTRUCT_ES_PDF=downloadable/CEQ-CEJST-Instructions-es.pdf
-GATSBY_FILE_DL_PATH_1_0_COMP_CHART_PDF=downloadable/total-comparison-chart.pdf
-GATSBY_FILE_DL_PATH_1_0_TSD_PDF=downloadable/1.0-cejst-technical-support-document.pdf
-GATSBY_FILE_DL_PATH_1_0_TOOL_COMP_PDF=downloadable/1.0-climate-and-environmental-burden-tool-comparison.pdf
-GATSBY_FILE_DL_PATH_1_0_M_23_09_SIGNED_PDF=downloadable/M-23-09_Signed_CEQ_CPO_es.pdf
+GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_XLS=downloadable/2.0-communities.xlsx
+GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_CSV=downloadable/2.0-communities.csv
+GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_PDF=downloadable/2.0-communities-list.pdf
+GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_ES_PDF=downloadable/2.0-communities-list-es.pdf
+GATSBY_FILE_DL_PATH_2_0_SHAPE_FILE_ZIP=downloadable/2.0-shapefile-codebook.zip
+GATSBY_FILE_DL_PATH_2_0_INSTRUCT_PDF=downloadable/CEQ-CEJST-Instructions.pdf
+GATSBY_FILE_DL_PATH_2_0_INSTRUCT_ES_PDF=downloadable/CEQ-CEJST-Instructions-es.pdf
+GATSBY_FILE_DL_PATH_2_0_COMP_CHART_PDF=downloadable/total-comparison-chart.pdf
+GATSBY_FILE_DL_PATH_2_0_TSD_PDF=downloadable/2.0-cejst-technical-support-document.pdf
+GATSBY_FILE_DL_PATH_2_0_TOOL_COMP_PDF=downloadable/2.0-climate-and-environmental-burden-tool-comparison.pdf
+GATSBY_FILE_DL_PATH_2_0_M_23_09_SIGNED_PDF=downloadable/M-23-09_Signed_CEQ_CPO_es.pdf
 
 GATSBY_FILE_DL_PATH_TSD_ES_PDF=downloadable/cejst-technical-support-document-es.pdf
 GATSBY_FILE_DL_PATH_HOW_TO_COMMUNITIES_PDF=downloadable/draft-communities-list.pdf
diff --git a/client/README.md b/client/README.md
index 48b8d7e3..da2079b3 100644
--- a/client/README.md
+++ b/client/README.md
@@ -214,6 +214,7 @@ When developing, to use a flag:
 6. Set breakpoints in VS code!
 
 
+
 ## Package Versions
 
 The following attemps to explain why certain packages versions have been chosen and what their current limitations are
diff --git a/client/package-lock.json b/client/package-lock.json
index 72420b06..98143395 100644
--- a/client/package-lock.json
+++ b/client/package-lock.json
@@ -3024,7 +3024,7 @@
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       "dev": true
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@@ -4881,7 +4881,7 @@
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@@ -4939,7 +4939,7 @@
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@@ -5169,7 +5169,7 @@
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@@ -5355,7 +5355,7 @@
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@@ -5448,7 +5448,7 @@
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@@ -5613,7 +5613,7 @@
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       "dev": true
     },
     "undeclared-identifiers": {
@@ -20735,7 +20740,7 @@
     "unixify": {
       "version": "1.0.0",
       "resolved": "https://registry.npmjs.org/unixify/-/unixify-1.0.0.tgz",
-      "integrity": "sha1-OmQcjC/7zk2mg6XHDwOkYpQMIJA=",
+      "integrity": "sha512-6bc58dPYhCMHHuwxldQxO3RRNZ4eCogZ/st++0+fcC1nr0jiGUtAdBJ2qzmLQWSxbtz42pWt4QQMiZ9HvZf5cg==",
       "dev": true,
       "requires": {
         "normalize-path": "^2.1.1"
@@ -20744,7 +20749,7 @@
         "normalize-path": {
           "version": "2.1.1",
           "resolved": "https://registry.npmjs.org/normalize-path/-/normalize-path-2.1.1.tgz",
-          "integrity": "sha1-GrKLVW4Zg2Oowab35vogE3/mrtk=",
+          "integrity": "sha512-3pKJwH184Xo/lnH6oyP1q2pMd7HcypqqmRs91/6/i2CGtWwIKGCkOOMTm/zXbgTEWHw1uNpNi/igc3ePOYHb6w==",
           "dev": true,
           "requires": {
             "remove-trailing-separator": "^1.0.1"
@@ -20755,7 +20760,7 @@
     "unpipe": {
       "version": "1.0.0",
       "resolved": "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz",
-      "integrity": "sha1-sr9O6FFKrmFltIF4KdIbLvSZBOw=",
+      "integrity": "sha512-pjy2bYhSsufwWlKwPc+l3cN7+wuJlK6uz0YdJEOlQDbl6jo/YlPi4mb8agUkVC8BF7V8NuzeyPNqRksA3hztKQ==",
       "dev": true
     },
     "unset-value": {
@@ -20868,7 +20873,7 @@
     "upper-case": {
       "version": "1.1.3",
       "resolved": "https://registry.npmjs.org/upper-case/-/upper-case-1.1.3.tgz",
-      "integrity": "sha1-9rRQHC7EzdJrp4vnIilh3ndiFZg=",
+      "integrity": "sha512-WRbjgmYzgXkCV7zNVpy5YgrHgbBv126rMALQQMrmzOVC4GM2waQ9x7xtm8VU+1yF2kWyPzI9zbZ48n4vSxwfSA==",
       "dev": true
     },
     "uri-js": {
@@ -20945,7 +20950,7 @@
     "url-parse-lax": {
       "version": "3.0.0",
       "resolved": "https://registry.npmjs.org/url-parse-lax/-/url-parse-lax-3.0.0.tgz",
-      "integrity": "sha1-FrXK/Afb42dsGxmZF3gj1lA6yww=",
+      "integrity": "sha512-NjFKA0DidqPa5ciFcSrXnAltTtzz84ogy+NebPvfEgAck0+TNg4UJ4IN+fB7zRZfbgUf0syOo9MDxFkDSMuFaQ==",
       "dev": true,
       "requires": {
         "prepend-http": "^2.0.0"
@@ -20981,7 +20986,7 @@
         "inherits": {
           "version": "2.0.3",
           "resolved": "https://registry.npmjs.org/inherits/-/inherits-2.0.3.tgz",
-          "integrity": "sha1-Yzwsg+PaQqUC9SRmAiSA9CCCYd4=",
+          "integrity": "sha512-x00IRNXNy63jwGkJmzPigoySHbaqpNuzKbBOmzK+g2OdZpQ9w+sxCN+VSB3ja7IAge2OP2qpfxTjeNcyjmW1uw==",
           "dev": true
         }
       }
@@ -20989,7 +20994,7 @@
     "util-arity": {
       "version": "1.1.0",
       "resolved": "https://registry.npmjs.org/util-arity/-/util-arity-1.1.0.tgz",
-      "integrity": "sha1-WdAa8f2z/t4KxOYysKtfbOl8kzA=",
+      "integrity": "sha512-kkyIsXKwemfSy8ZEoaIz06ApApnWsk5hQO0vLjZS6UkBiGiW++Jsyb8vSBoc0WKlffGoGs5yYy/j5pp8zckrFA==",
       "dev": true
     },
     "util-deprecate": {
@@ -21006,13 +21011,13 @@
     "utila": {
       "version": "0.4.0",
       "resolved": "https://registry.npmjs.org/utila/-/utila-0.4.0.tgz",
-      "integrity": "sha1-ihagXURWV6Oupe7MWxKk+lN5dyw=",
+      "integrity": "sha512-Z0DbgELS9/L/75wZbro8xAnT50pBVFQZ+hUEueGDU5FN51YSCYM+jdxsfCiHjwNP/4LCDD0i/graKpeBnOXKRA==",
       "dev": true
     },
     "utils-merge": {
       "version": "1.0.1",
       "resolved": "https://registry.npmjs.org/utils-merge/-/utils-merge-1.0.1.tgz",
-      "integrity": "sha1-n5VxD1CiZ5R7LMwSR0HBAoQn5xM=",
+      "integrity": "sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA==",
       "dev": true
     },
     "uuid": {
@@ -21096,7 +21101,7 @@
     "vary": {
       "version": "1.1.2",
       "resolved": "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz",
-      "integrity": "sha1-IpnwLG3tMNSllhsLn3RSShj2NPw=",
+      "integrity": "sha512-BNGbWLfd0eUPabhkXUVm0j8uuvREyTh5ovRa/dyow/BqAbZJyC+5fU+IzQOzmAKzYqYRAISoRhdQr3eIZ/PXqg==",
       "dev": true
     },
     "verror": {
@@ -21873,7 +21878,7 @@
     "yauzl": {
       "version": "2.10.0",
       "resolved": "https://registry.npmjs.org/yauzl/-/yauzl-2.10.0.tgz",
-      "integrity": "sha1-x+sXyT4RLLEIb6bY5R+wZnt5pfk=",
+      "integrity": "sha512-p4a9I6X6nu6IhoGmBqAcbJy1mlC4j27vEPZX9F4L4/vZT3Lyq1VkFHw/V/PUcB9Buo+DG3iHkT0x3Qya58zc3g==",
       "dev": true,
       "requires": {
         "buffer-crc32": "~0.2.3",
diff --git a/client/package.json b/client/package.json
index fefac8fd..aebfd24f 100644
--- a/client/package.json
+++ b/client/package.json
@@ -68,6 +68,7 @@
     "husky": "^7.0.4",
     "identity-obj-proxy": "^3.0.0",
     "jest": "^27.5.1",
+    "jest-environment-jsdom": "^27.5.1",
     "license-checker": "^25.0.1",
     "prettier": "^2.6.0",
     "react-test-renderer": "^17.0.2",
@@ -84,6 +85,7 @@
     "gatsby-plugin-env-variables": "^2.2.0",
     "gatsby-plugin-robots-txt": "^1.7.0",
     "gatsby-plugin-sitemap": "^4.10.0",
+    "js-search": "^2.0.1",
     "mapbox-gl": "^1.13.2",
     "maplibre-gl": "^1.14.0",
     "query-string": "^7.1.3",
diff --git a/client/src/components/AreaDetail/AreaDetail.tsx b/client/src/components/AreaDetail/AreaDetail.tsx
index 4e480dd8..2f3656b8 100644
--- a/client/src/components/AreaDetail/AreaDetail.tsx
+++ b/client/src/components/AreaDetail/AreaDetail.tsx
@@ -1146,6 +1146,12 @@ const AreaDetail = ({properties}: IAreaDetailProps) => {
             isAdjacencyLowIncome={
               properties[constants.ADJACENCY_LOW_INCOME_EXCEEDS_THRESH]
             }
+            isIslandLowIncome={
+              properties[constants.IS_FEDERAL_POVERTY_LEVEL_200] &&
+              constants.TILES_ISLAND_AREA_FIPS_CODES.some((code) => {
+                return properties[constants.GEOID_PROPERTY].startsWith(code);
+              })
+            }
             tribalCountAK={
               properties[constants.TRIBAL_AREAS_COUNT_AK] >= 1 ?
                 properties[constants.TRIBAL_AREAS_COUNT_AK] :
diff --git a/client/src/components/J40Map.tsx b/client/src/components/J40Map.tsx
index 675428f3..adc249b9 100644
--- a/client/src/components/J40Map.tsx
+++ b/client/src/components/J40Map.tsx
@@ -130,7 +130,7 @@ const J40Map = ({location}: IJ40Interface) => {
   const onClick = (event: MapEvent | React.MouseEvent<HTMLButtonElement>) => {
     // Stop all propagation / bubbling / capturing
     event.preventDefault();
-    event.stopPropagation();
+    (event as React.MouseEvent<HTMLButtonElement>).stopPropagation?.();
 
     // Check if the click is for territories. Given the territories component's design, it can be
     // guaranteed that each territory control will have an id. We use this ID to determine
@@ -167,8 +167,9 @@ const J40Map = ({location}: IJ40Interface) => {
         default:
           break;
       }
-    } else {
-      // This else clause will fire when the ID is null or empty. This is the case where the map is clicked
+    } else if (event.target && (event.target as HTMLElement).nodeName == 'DIV' ) {
+      // This else clause will fire when the user clicks on the map and will ignore other controls
+      // such as the search box and buttons.
 
       // @ts-ignore
       const feature = event.features && event.features[0];
diff --git a/client/src/components/MapLegend/tests/__snapshots__/mapLegend.test.tsx.snap b/client/src/components/MapLegend/tests/__snapshots__/mapLegend.test.tsx.snap
index f772b0dd..a464fdc3 100644
--- a/client/src/components/MapLegend/tests/__snapshots__/mapLegend.test.tsx.snap
+++ b/client/src/components/MapLegend/tests/__snapshots__/mapLegend.test.tsx.snap
@@ -18,8 +18,8 @@ exports[`rendering of the MapLegend checks if snapshots have changed 1`] = `
           class="secondary"
         >
           
-      Communities identified as disadvantaged by the map are those that are marginalized, underserved, 
-      and overburdened by pollution. These communities are at or above the thresholds in one or 
+      Communities identified as disadvantaged by the map are those that are marginalized, underserved,
+      and overburdened by pollution. These communities are at or above the thresholds in one or
       more of eight categories of criteria.
     
         </p>
diff --git a/client/src/components/MapSearch/MapSearch.tsx b/client/src/components/MapSearch/MapSearch.tsx
index 4fc4e808..681332ea 100644
--- a/client/src/components/MapSearch/MapSearch.tsx
+++ b/client/src/components/MapSearch/MapSearch.tsx
@@ -4,6 +4,8 @@ import {LngLatBoundsLike} from 'maplibre-gl';
 import {useIntl} from 'gatsby-plugin-intl';
 import {Search} from '@trussworks/react-uswds';
 import {useWindowSize} from 'react-use';
+import * as JsSearch from 'js-search';
+import * as constants from '../../data/constants';
 
 import MapSearchMessage from '../MapSearchMessage';
 
@@ -14,6 +16,16 @@ interface IMapSearch {
   goToPlace(bounds: LngLatBoundsLike):void;
 }
 
+interface ISearchResult {
+  addresstype: string;
+  lat: string;
+  lon: string;
+  boundingbox: string[];
+  type: string;
+  // eslint-disable-next-line camelcase
+  place_rank: number;
+}
+
 const MapSearch = ({goToPlace}:IMapSearch) => {
   // State to hold if the search results are empty or not:
   const [isSearchResultsNull, setIsSearchResultsNull] = useState(false);
@@ -30,44 +42,118 @@ const MapSearch = ({goToPlace}:IMapSearch) => {
    */
   const {width, height} = useWindowSize();
   const [placeholderText, setPlaceholderText]= useState(EXPLORE_COPY.MAP.SEARCH_PLACEHOLDER);
+  const [tractSearch, setTractSearch] = useState<JsSearch | null>(null);
+
+  /**
+   * Gets the tract search data and loads in the state.
+   */
+  const getTractSearchData = async () => {
+    const searchDataUrl = `${constants.TILE_BASE_URL}/${constants.MAP_TRACT_SEARCH_PATH}`;
+    fetch(searchDataUrl)
+        .then((response) => {
+          if (response.ok) {
+            return response.json();
+          } else {
+            throw new Error(`${response.statusText} error with status code of ${response.status}`);
+          }
+        })
+        .then((data) => {
+          // We use JsSearch to make it easy to load and quick to search.
+          const search = new JsSearch.Search('GEOID10');
+          search.indexStrategy = new JsSearch.ExactWordIndexStrategy();
+          search.addIndex('GEOID10');
+          search.addDocuments(data);
+          setTractSearch(search);
+        })
+        .catch((error) =>
+          console.error('Unable to read search tract table:', error));
+  };
 
   useEffect( () => {
    width > height ? setPlaceholderText(EXPLORE_COPY.MAP.SEARCH_PLACEHOLDER): setPlaceholderText(EXPLORE_COPY.MAP.SEARCH_PLACEHOLDER_MOBILE);
   }, [width]);
 
+  useEffect(()=>{
+    getTractSearchData();
+  }, []);
+
+  /**
+   * Searchs for a given Census tract ID.
+   * @param {string} tract the 11 digit tract ID as a string
+   * @return {Array} an array of one search result, or null if no result found
+   */
+  const searchForTract = (tract: string): [ISearchResult] | [] => {
+    // We create a bounding box just to get the tract in the view box.
+    // The size is not important.
+    const BOUNDING_BOX_SIZE_DD = 0.2;
+    if (tractSearch) {
+      // Convert 10 digit tracts to 11.
+      const searchTerm = tract.length == 10 ? '0' + tract : tract;
+      const result = tractSearch.search(searchTerm);
+      if (result.length > 0) {
+        const lat = Number(result[0].INTPTLAT10);
+        const lon = Number(result[0].INTPTLON10);
+        return [{
+          addresstype: 'tract',
+          boundingbox: [
+            (lat - (BOUNDING_BOX_SIZE_DD / 2)).toString(),
+            (lat + (BOUNDING_BOX_SIZE_DD / 2)).toString(),
+            (lon - (BOUNDING_BOX_SIZE_DD / 2)).toString(),
+            (lon + (BOUNDING_BOX_SIZE_DD / 2)).toString(),
+          ],
+          lat: result[0].INTPTLAT10,
+          lon: result[0].INTPTLON10,
+          type: 'tract',
+          place_rank: 1,
+        }];
+      }
+    }
+    return [];
+  };
+
   /*
     onSearchHandler will
      1. extract the search term from the input field
-     2. fetch data from the API and return the results as JSON and results to US only
-     3. if data is valid, destructure the boundingBox values from the search results
-     4. pan the map to that location
+     2. Determine if the search term is a Census Tract or not.
+     3. If it is a Census Tract, it will search the tract table for a bounding box.
+     4. If it is NOT a Census Tract, it will fetch data from the API and return the
+        results as JSON and results to US only. If data is valid, destructure the
+        boundingBox values from the search results.
+     4. Pan the map to that location
   */
   const onSearchHandler = async (event: React.FormEvent<HTMLFormElement>) => {
     event.preventDefault();
+    event.stopPropagation();
 
     const searchTerm = (event.currentTarget.elements.namedItem('search') as HTMLInputElement).value;
+    let searchResults = null;
 
-    const searchResults = await fetch(
-        `https://nominatim.openstreetmap.org/search?q=${searchTerm}&format=json&countrycodes=us`,
-        {
-          mode: 'cors',
-        })
-        .then((response) => {
-          if (!response.ok) {
-            throw new Error('Network response was not OK');
-          }
-          return response.json();
-        })
-        .catch((error) => {
-          console.error('There has been a problem with your fetch operation:', error);
-        });
-
+    // If the search term a Census tract
+    const isTract = /^\d{10,11}$/.test(searchTerm);
+    if (isTract) {
+      setIsSearchResultsNull(false);
+      searchResults = searchForTract(searchTerm);
+    } else {
+      searchResults = await fetch(
+          `https://nominatim.openstreetmap.org/search?q=${searchTerm}&format=json&countrycodes=us`,
+          {
+            mode: 'cors',
+          })
+          .then((response) => {
+            if (!response.ok) {
+              throw new Error('Network response was not OK');
+            }
+            return response.json();
+          })
+          .catch((error) => {
+            console.error('There has been a problem with your fetch operation:', error);
+          });
+      console.log('Nominatum search results: ', searchResults);
+    }
 
     // If results are valid, set isSearchResultsNull to false and pan map to location:
     if (searchResults && searchResults.length > 0) {
       setIsSearchResultsNull(false);
-      console.log('Nominatum search results: ', searchResults);
-
       const [latMin, latMax, longMin, longMax] = searchResults[0].boundingbox;
       goToPlace([[Number(longMin), Number(latMin)], [Number(longMax), Number(latMax)]]);
     } else {
diff --git a/client/src/components/MapSearch/__snapshots__/MapSearch.test.tsx.snap b/client/src/components/MapSearch/__snapshots__/MapSearch.test.tsx.snap
index 9d1c383c..74a2597d 100644
--- a/client/src/components/MapSearch/__snapshots__/MapSearch.test.tsx.snap
+++ b/client/src/components/MapSearch/__snapshots__/MapSearch.test.tsx.snap
@@ -23,7 +23,7 @@ exports[`rendering of the MapSearch checks if component renders 1`] = `
         data-testid="textInput"
         id="search-field"
         name="search"
-        placeholder="Search for an address, city, state or ZIP"
+        placeholder="Search for an address, city, state, ZIP or Census Tract"
         type="search"
       />
       <button
diff --git a/client/src/components/MapTractLayers/MapTractLayers.tsx b/client/src/components/MapTractLayers/MapTractLayers.tsx
index 7970d4d1..57a765c2 100644
--- a/client/src/components/MapTractLayers/MapTractLayers.tsx
+++ b/client/src/components/MapTractLayers/MapTractLayers.tsx
@@ -24,7 +24,7 @@ interface IMapTractLayers {
 export const featureURLForTilesetName = (tilesetName: string): string => {
   const flags = useFlags();
 
-  const pipelineStagingBaseURL = `https://justice40-data.s3.amazonaws.com/data-pipeline-staging`;
+  const pipelineStagingBaseURL = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-pipeline-staging`;
   const XYZ_SUFFIX = '{z}/{x}/{y}.pbf';
 
   if ('stage_hash' in flags) {
@@ -38,13 +38,8 @@ export const featureURLForTilesetName = (tilesetName: string): string => {
   } else {
     // The feature tile base URL and path can either point locally or the CDN.
     // This is selected based on the DATA_SOURCE env variable.
-    const featureTileBaseURL = process.env.DATA_SOURCE === 'local' ?
-        process.env.GATSBY_LOCAL_TILES_BASE_URL :
-        process.env.GATSBY_CDN_TILES_BASE_URL;
-
-    const featureTilePath = process.env.DATA_SOURCE === 'local' ?
-      process.env.GATSBY_DATA_PIPELINE_SCORE_PATH_LOCAL :
-      process.env.GATSBY_1_0_SCORE_PATH;
+    const featureTileBaseURL = constants.TILE_BASE_URL;
+    const featureTilePath = constants.TILE_PATH;
 
     return [
       featureTileBaseURL,
diff --git a/client/src/components/PrioritizationCopy/PrioritizationCopy.tsx b/client/src/components/PrioritizationCopy/PrioritizationCopy.tsx
index c2b22f6e..6769ebc3 100644
--- a/client/src/components/PrioritizationCopy/PrioritizationCopy.tsx
+++ b/client/src/components/PrioritizationCopy/PrioritizationCopy.tsx
@@ -9,6 +9,7 @@ interface IPrioritizationCopy {
   totalBurdensPrioritized: number
   isAdjacencyThreshMet: boolean,
   isAdjacencyLowIncome: boolean,
+  isIslandLowIncome: boolean,
   tribalCountAK: number | null,
   tribalCountUS: null, // when this signal is supported add number type
   percentTractTribal: number | null
@@ -26,6 +27,7 @@ interface IPrioritizationCopy {
  * @param {number} totalBurdensPrioritized
  * @param {boolean} isAdjacencyThreshMet
  * @param {boolean} isAdjacencyLowIncome
+ * @param {boolean} isIslandLowIncome
  * @param {number | null} tribalCountAK
  * @param {number | null} tribalCountUS
  * @param {number | null} percentTractTribal
@@ -36,6 +38,7 @@ const PrioritizationCopy =
      totalBurdensPrioritized,
      isAdjacencyThreshMet,
      isAdjacencyLowIncome,
+     isIslandLowIncome,
      tribalCountAK,
      tribalCountUS,
      percentTractTribal,
@@ -48,6 +51,9 @@ const PrioritizationCopy =
        if (isAdjacencyThreshMet && isAdjacencyLowIncome) {
          prioCopyRendered = EXPLORE_COPY.PRIORITIZATION_COPY.PRIO_SURR_LI;
          // if 1-2
+       } else if (isIslandLowIncome) {
+         prioCopyRendered = EXPLORE_COPY.PRIORITIZATION_COPY.PRIO_ISLAND_LI;
+         // if 1-3
        } else if (isAdjacencyThreshMet && !isAdjacencyLowIncome) {
          // if 1-2-1
          if ( tribalCountAK === null && tribalCountUS === null) {
diff --git a/client/src/components/ReleaseUpdate/ReleaseUpdate.tsx b/client/src/components/ReleaseUpdate/ReleaseUpdate.tsx
index 57b4f3e4..fc509e81 100644
--- a/client/src/components/ReleaseUpdate/ReleaseUpdate.tsx
+++ b/client/src/components/ReleaseUpdate/ReleaseUpdate.tsx
@@ -77,195 +77,195 @@ const ReleaseUpdate = ({}: IReleaseUpdateProps) => {
         <div>
 
           <div className={styles.releaseHeader}>
-            {DOWNLOAD_COPY.RELEASE_1_0.UPDATE_1}
+            {DOWNLOAD_COPY.RELEASE_2_0.UPDATE_1}
           </div>
 
           <div className={styles.releaseSectionTitle}>
-            {DOWNLOAD_COPY.RELEASE_1_0.SECTION1}
+            {DOWNLOAD_COPY.RELEASE_2_0.SECTION1}
           </div>
 
           <div>
             <ul>
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B1}</li>
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B1}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2}</li>
 
               <ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_1}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_1}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_1_1}</li>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_1_2}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_1_1}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_1_2}</li>
                 </ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_2}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_2}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_1_3}</li>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_2_2}</li>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_2_1}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_1_3}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_2_2}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_2_1}</li>
                 </ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_3}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_3}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_3_1}</li>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_3_2}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_3_1}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_3_2}</li>
                 </ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_4}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_4}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_4_1}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_4_1}</li>
                 </ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_5}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_5}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B2_5_1}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B2_5_1}</li>
                 </ul>
               </ul>
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B3}</li>
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B4}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B3}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B4}</li>
               <ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B4_1}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B4_2}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B4_3}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B4_1}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B4_2}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B4_3}</li>
               </ul>
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B5}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B5}</li>
               <ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B5_1}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B5_2}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B5_3}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B5_1}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B5_2}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B5_3}</li>
               </ul>
 
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6}</li>
               <ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_1}</li>
-                  {/* <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_1}</li>
+                  {/* <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2}</li>
                   <ul>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1}</li>
                     <ul>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1_1}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1_1}</li>
                     </ul>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2}</li>
                     <ul>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_1}</li>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_2}</li>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_3}</li>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_4}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_1}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_2}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_3}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_4}</li>
                     </ul>
                   </ul> */}
                 </ul>
 
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_2}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_2}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_2_1}</li>
-                  {/* <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_2_1}</li>
+                  {/* <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2}</li>
                   <ul>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1}</li>
                     <ul>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1_1}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1_1}</li>
                     </ul>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2}</li>
                     <ul>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_1}</li>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_2}</li>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_3}</li>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_4}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_1}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_2}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_3}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_4}</li>
                     </ul>
                   </ul> */}
                 </ul>
 
-                {/* <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_3}</li>
+                {/* <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_3}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2}</li>
                   <ul>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1}</li>
                     <ul>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1_1}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1_1}</li>
                     </ul>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2}</li>
                     <ul>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_2}</li>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_4}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_2}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_4}</li>
                     </ul>
                   </ul>
                 </ul>
 
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_4}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_4}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2}</li>
                   <ul>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1}</li>
                     <ul>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1_1}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1_1}</li>
                     </ul>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2}</li>
                     <ul>
-                      <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_4}</li>
+                      <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_4}</li>
                     </ul>
                   </ul>
                 </ul> */}
 
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5_1}</li>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5_2}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_1}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_2}</li>
                   <ul>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5_2_1}</li>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5_2_2}</li>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5_2_3}</li>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_5_2_4}</li>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_1}</li>
-                    <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2_2}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_2_1}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_2_2}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_2_3}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_5_2_4}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_1}</li>
+                    <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2_2}</li>
                   </ul>
                 </ul>
               </ul>
 
-              {/* <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7}</li>
+              {/* <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7}</li>
               <ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_1}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_1}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_1_1}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_1_1}</li>
                 </ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B6_1_2_2}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B6_1_2_2}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_2_1}</li>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_2_2}</li>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_2_3}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_2_1}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_2_2}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_2_3}</li>
                 </ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_3}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_3}</li>
                 <ul>
-                  <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B7_3_1}</li>
+                  <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B7_3_1}</li>
                 </ul>
               </ul> */}
 
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B8}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B8}</li>
               <ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B8_1}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B8_2}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B8_3}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B8_4}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B8_1}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B8_2}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B8_3}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B8_4}</li>
               </ul>
 
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B9}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B9}</li>
               <ul>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B9_1}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B9_2}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B9_3}</li>
-                <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B9_4}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B9_1}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B9_2}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B9_3}</li>
+                <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B9_4}</li>
               </ul>
 
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION1_B10}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION1_B10}</li>
             </ul>
           </div>
 
           <div className={styles.releaseSectionTitle}>
-            {DOWNLOAD_COPY.RELEASE_1_0.SECTION2}
+            {DOWNLOAD_COPY.RELEASE_2_0.SECTION2}
           </div>
 
           <div>
             <ul>
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION2_P1}</li>
-              <li>{DOWNLOAD_COPY.RELEASE_1_0.SECTION2_P2}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION2_P1}</li>
+              <li>{DOWNLOAD_COPY.RELEASE_2_0.SECTION2_P2}</li>
             </ul>
           </div>
 
           <div>
-            {DOWNLOAD_COPY.RELEASE_1_0.FOOTER}
+            {DOWNLOAD_COPY.RELEASE_2_0.FOOTER}
           </div>
 
         </div>
diff --git a/client/src/components/SidePanelInfo/__snapshots__/SidePanelInfo.test.tsx.snap b/client/src/components/SidePanelInfo/__snapshots__/SidePanelInfo.test.tsx.snap
index 332c2f33..15688eec 100644
--- a/client/src/components/SidePanelInfo/__snapshots__/SidePanelInfo.test.tsx.snap
+++ b/client/src/components/SidePanelInfo/__snapshots__/SidePanelInfo.test.tsx.snap
@@ -34,7 +34,7 @@ exports[`rendering of the component expects the render to match snapshot 1`] = `
         tabindex="0"
       />
       
-      , or locate yourself 
+      , or locate yourself
     
       <img
         alt="
@@ -70,7 +70,7 @@ exports[`rendering of the component expects the render to match snapshot 1`] = `
         tabindex="0"
       />
       
-    . Census tracts are a small unit of geography. They generally have populations 
+    . Census tracts are a small unit of geography. They generally have populations
     
       <img
         alt="
@@ -87,7 +87,7 @@ exports[`rendering of the component expects the render to match snapshot 1`] = `
       tabindex="0"
     >
       
-      Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted 
+      Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted
     
       <img
         alt="
@@ -131,7 +131,7 @@ exports[`rendering of the component expects the render to match snapshot 1`] = `
         tabindex="0"
       />
       
-    , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages 
+    , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages
     
       <img
         alt="
diff --git a/client/src/components/__snapshots__/mapInfoPanel.test.tsx.snap b/client/src/components/__snapshots__/mapInfoPanel.test.tsx.snap
index a619ebb2..4c6cf268 100644
--- a/client/src/components/__snapshots__/mapInfoPanel.test.tsx.snap
+++ b/client/src/components/__snapshots__/mapInfoPanel.test.tsx.snap
@@ -39,7 +39,7 @@ exports[`simulate app starting up, no click on map should match the snapshot of
           tabindex="0"
         />
         
-      , or locate yourself 
+      , or locate yourself
     
         <img
           alt="
@@ -75,7 +75,7 @@ exports[`simulate app starting up, no click on map should match the snapshot of
           tabindex="0"
         />
         
-    . Census tracts are a small unit of geography. They generally have populations 
+    . Census tracts are a small unit of geography. They generally have populations
     
         <img
           alt="
@@ -92,7 +92,7 @@ exports[`simulate app starting up, no click on map should match the snapshot of
         tabindex="0"
       >
         
-      Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted 
+      Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted
     
         <img
           alt="
@@ -136,7 +136,7 @@ exports[`simulate app starting up, no click on map should match the snapshot of
           tabindex="0"
         />
         
-    , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages 
+    , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages
     
         <img
           alt="
diff --git a/client/src/data/constants.tsx b/client/src/data/constants.tsx
index cbb3b890..2f88b502 100644
--- a/client/src/data/constants.tsx
+++ b/client/src/data/constants.tsx
@@ -1,5 +1,7 @@
-import { LngLatBoundsLike } from "maplibre-gl";
-import { isMobile as isMobileReactDeviceDetect } from "react-device-detect";
+/* eslint quotes: [2, "double"] */
+
+import {LngLatBoundsLike} from "maplibre-gl";
+import {isMobile as isMobileReactDeviceDetect} from "react-device-detect";
 
 export const isMobile = isMobileReactDeviceDetect;
 
@@ -77,6 +79,13 @@ export const SIDE_PANEL_STATE_VALUES = {
   ISLAND_AREAS: "Island Areas",
 };
 
+/**
+ * Note that the FIPS code is a string
+ * The FIPS codes listed are:
+ * 60: American Samoa, 66: Guam, 69: N. Mariana Islands, 78: US Virgin Islands
+ */
+export const TILES_ISLAND_AREA_FIPS_CODES = ["60", "66", "69", "78"];
+
 // Climate category
 export const IS_CLIMATE_FACTOR_DISADVANTAGED = "N_CLT";
 export const IS_CLIMATE_EXCEED_ONE_OR_MORE_INDICATORS = "N_CLT_EOMI";
@@ -221,7 +230,7 @@ export const IS_EXCEEDS_THRESH_FOR_ISLAND_AREA_BELOW_100_POVERTY = "IA_POV_ET";
 
 export const IS_WORKFORCE_EXCEED_BOTH_SOCIO_INDICATORS = "N_WKFC_EBSI";
 
-export const HIGH_SCHOOL_PROPERTY_PERCENTILE = `HSEF`;
+export const HIGH_SCHOOL_PROPERTY_PERCENTILE = "HSEF";
 export const IS_LOW_HS_EDUCATION_LOW_HIGHER_ED_PRIORITIZED = "LHE";
 export const ISLAND_AREAS_HS_EDU_PERCENTAGE_FIELD = "IAHSEF";
 export const ISLAND_AREA_LOW_HS_EDU = "IALHE";
@@ -371,3 +380,13 @@ export const CENSUS_TRACT_SURVEY_LINKS = {
   EN: "https://eop.gov1.qualtrics.com/jfe/form/SV_8J5wGa8Ya4dMP9c",
   ES: "https://eop.gov1.qualtrics.com/jfe/form/SV_eJXos5X4yekq6cC",
 };
+
+export const TILE_BASE_URL = process.env.DATA_SOURCE === "local" ?
+process.env.GATSBY_LOCAL_TILES_BASE_URL :
+process.env.GATSBY_CDN_TILES_BASE_URL;
+
+export const TILE_PATH = process.env.DATA_SOURCE === "local" ?
+process.env.GATSBY_DATA_PIPELINE_SCORE_PATH_LOCAL :
+process.env.GATSBY_1_0_SCORE_PATH;
+
+export const MAP_TRACT_SEARCH_PATH = "data_pipeline/data/score/search/tracts.json";
diff --git a/client/src/data/copy/about.tsx b/client/src/data/copy/about.tsx
index 4326ef4b..78a15480 100644
--- a/client/src/data/copy/about.tsx
+++ b/client/src/data/copy/about.tsx
@@ -12,16 +12,16 @@ export const FAQS_LINK = 'https://www.whitehouse.gov/wp-content/uploads/2022/02/
 export const FED_RECOGNIZED_INDIAN_ENTITIES = `https://www.federalregister.gov/documents/2022/01/28/2022-01789/indian-entities-recognized-by-and-eligible-to-receive-services-from-the-united-states-bureau-of`;
 export const EJSCREEN = 'https://www.epa.gov/ejscreen/how-does-epa-use-ejscreen';
 
-export const CEJST_INSTRUCT = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/CEQ-CEJST-Instructions.pdf`;
-export const CEJST_INSTRUCT_ES = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/CEQ-CEJST-Instructions-es.pdf`;
+export const CEJST_INSTRUCT = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/CEQ-CEJST-Instructions.pdf`;
+export const CEJST_INSTRUCT_ES = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/CEQ-CEJST-Instructions-es.pdf`;
 
 export const CEJST_MEMO = `https://www.whitehouse.gov/wp-content/uploads/2023/01/M-23-09_Signed_CEQ_CPO.pdf`;
-export const CEJST_MEMO_ES = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/M-23-09_Signed_CEQ_CPO_es.pdf`;
+export const CEJST_MEMO_ES = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/M-23-09_Signed_CEQ_CPO_es.pdf`;
 
-export const USE_MAP_TUTORIAL_LINK = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Tutorial.pdf`;
-export const USE_MAP_TUTORIAL_LINK_ES = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Tutorial-es.pdf`;
-export const USE_DATA_TUTORIAL_LINK = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial.pdf`;
-export const USE_DATA_TUTORIAL_LINK_ES = `https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial-es.pdf`;
+export const USE_MAP_TUTORIAL_LINK = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Tutorial.pdf`;
+export const USE_MAP_TUTORIAL_LINK_ES = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Tutorial-es.pdf`;
+export const USE_DATA_TUTORIAL_LINK = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial.pdf`;
+export const USE_DATA_TUTORIAL_LINK_ES = process.env.GATSBY_CDN_TILES_BASE_URL +`/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial-es.pdf`;
 
 export const GITHUB_LINK = 'https://github.com/usds/justice40-tool';
 export const GITHUB_LINK_ES = 'https://github.com/usds/justice40-tool/blob/main/README-es.md';
diff --git a/client/src/data/copy/common.tsx b/client/src/data/copy/common.tsx
index 1433615f..f9248517 100644
--- a/client/src/data/copy/common.tsx
+++ b/client/src/data/copy/common.tsx
@@ -28,7 +28,7 @@ export const linkFn = (to:string | IDefineMessage, isInternal:boolean, isOpenNew
 
 export const FEEDBACK_EMAIL = 'Screeningtool-Support@omb.eop.gov';
 
-export const METH_1_0_RELEASE_DATE = new Date(2022, 10, 22, 11, 59, 59); // Nov 22 2022
+export const METH_2_0_RELEASE_DATE = new Date(2022, 10, 22, 11, 59, 59); // Nov 22 2022
 export const METH_BETA_RELEASE_DATE = new Date(2022, 1, 18, 11, 59, 59); // Feb 18 2022
 
 
@@ -40,7 +40,7 @@ export const BETA_BANNER_CONTENT = <FormattedMessage
   values={{
     bold1: boldFn,
     relDate: <FormattedDate
-      value={METH_1_0_RELEASE_DATE}
+      value={METH_2_0_RELEASE_DATE}
       year="numeric"
       month="short"
       day="numeric"
@@ -75,7 +75,7 @@ export const ALERTS = {
     values={{
       link1: linkFn('https://www.whitehouse.gov/ceq/news-updates/2022/11/22/biden-harris-administration-launches-version-1-0-of-climate-and-economic-justice-screening-tool-key-step-in-implementing-president-bidens-justice40-initiative/', false, true),
       ver1RelDate: <FormattedDate
-        value={METH_1_0_RELEASE_DATE}
+        value={METH_2_0_RELEASE_DATE}
         year="numeric"
         month="numeric"
         day="numeric"
diff --git a/client/src/data/copy/downloads.tsx b/client/src/data/copy/downloads.tsx
index 9ec55f5c..d6a3b002 100644
--- a/client/src/data/copy/downloads.tsx
+++ b/client/src/data/copy/downloads.tsx
@@ -31,7 +31,7 @@ export const PAGE_INTRO = defineMessages({
 export const getDownloadFileUrl = (filePath:string | undefined, isBeta: boolean) => {
   return [
     process.env.GATSBY_CDN_TILES_BASE_URL,
-    (isBeta ? process.env.GATSBY_BETA_SCORE_PATH : process.env.GATSBY_1_0_SCORE_PATH),
+    (isBeta ? process.env.GATSBY_BETA_SCORE_PATH : process.env.GATSBY_2_0_SCORE_PATH),
     filePath,
   ].join('/');
 };
@@ -41,58 +41,58 @@ export const DOWNLOAD_FILES = {
   NARWAL: {
     COMMUNITIES_LIST_XLS: {
       SIZE: 35.6, // MB
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_XLS, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_XLS, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     COMMUNITIES_LIST_CSV: {
       SIZE: 42, // MB
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_CSV, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_CSV, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     SHAPE_FILE: {
       SIZE: 356.8, // MB
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_SHAPE_FILE_ZIP, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_SHAPE_FILE_ZIP, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     TSD: {
       SIZE: 4.4, // MB
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_TSD_PDF, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_TSD_PDF, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     TSD_ES: {
       SIZE: 4.8, // MB
       URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_TSD_ES_PDF, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     HOW_TO_COMMUNITIES: {
       SIZE: 687.9, // KB
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_PDF, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_PDF, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     HOW_TO_COMMUNITIES_ES: {
       SIZE: 761.8, // KB
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_COMMUNITIES_LIST_ES_PDF, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_COMMUNITIES_LIST_ES_PDF, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     INSTRUCTIONS: {
       SIZE: 228.4, // KB // Todo: Update when actual file is uploaded
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_INSTRUCT_PDF, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_INSTRUCT_PDF, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     INSTRUCTIONS_ES: {
       SIZE: 173.6, // KB // Todo: Update when actual file is uploaded
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_INSTRUCT_ES_PDF, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_INSTRUCT_ES_PDF, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     COMP_CHART: {
       SIZE: 33.1, // KB
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_TOOL_COMP_PDF, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_TOOL_COMP_PDF, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
     M_23_09_ES: {
       SIZE: 120.5, // KB
-      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_1_0_M_23_09_SIGNED_PDF, false),
-      LAST_UPDATED: COMMON_COPY.METH_1_0_RELEASE_DATE,
+      URL: getDownloadFileUrl(process.env.GATSBY_FILE_DL_PATH_2_0_M_23_09_SIGNED_PDF, false),
+      LAST_UPDATED: COMMON_COPY.METH_2_0_RELEASE_DATE,
     },
   },
   BETA: {
@@ -138,7 +138,7 @@ export const getDownloadIconAltTag = () => defineMessages({
   },
 });
 
-export const RELEASE_1_0 = {
+export const RELEASE_2_0 = {
   UPDATE_1: <FormattedMessage
     id={'download.page.release.update.title.1'}
     defaultMessage={`Version {release} Release update - {date}`}
@@ -146,7 +146,7 @@ export const RELEASE_1_0 = {
     values={{
       release: VERSION_NUMBER,
       date: <FormattedDate
-        value={COMMON_COPY.METH_1_0_RELEASE_DATE}
+        value={COMMON_COPY.METH_2_0_RELEASE_DATE}
         year="numeric"
         month="short"
         day="numeric"
diff --git a/client/src/data/copy/explore.tsx b/client/src/data/copy/explore.tsx
index 5c59d831..2429f59d 100644
--- a/client/src/data/copy/explore.tsx
+++ b/client/src/data/copy/explore.tsx
@@ -33,7 +33,7 @@ export const PAGE_INTRO = defineMessages({
 export const PAGE_DESCRIPTION1 = <FormattedMessage
   id={'explore.map.page.description.1'}
   defaultMessage={`
-    Census tracts that are overburdened and underserved are highlighted as being disadvantaged on the map. Federally Recognized Tribes, including Alaska Native Villages, are also considered disadvantaged communities. 
+    Census tracts that are overburdened and underserved are highlighted as being disadvantaged on the map. Federally Recognized Tribes, including Alaska Native Villages, are also considered disadvantaged communities.
   `}
   description={'On the explore the map page, the first description of the page'}
 />;
@@ -41,7 +41,7 @@ export const PAGE_DESCRIPTION1 = <FormattedMessage
 export const PAGE_DESCRIPTION2 = <FormattedMessage
   id={'explore.map.page.description.2'}
   defaultMessage={`
-    Zooming in and selecting shows information about each census tract. 
+    Zooming in and selecting shows information about each census tract.
   `}
   description={'On the explore the map page, the fifth description of the page'}
 />;
@@ -55,8 +55,8 @@ export const LEGEND = defineMessages({
   PRIORITY_DESCRIPT: {
     id: 'explore.map.page.legend.description.text',
     defaultMessage: `
-      Communities identified as disadvantaged by the map are those that are marginalized, underserved, 
-      and overburdened by pollution. These communities are at or above the thresholds in one or 
+      Communities identified as disadvantaged by the map are those that are marginalized, underserved,
+      and overburdened by pollution. These communities are at or above the thresholds in one or
       more of eight categories of criteria.
     `,
     description: 'On the explore the map page, the description of the legend',
@@ -92,7 +92,7 @@ export const MAP = defineMessages({
   },
   SEARCH_PLACEHOLDER: {
     id: 'explore.map.page.map.search.placeholder.text',
-    defaultMessage: 'Search for an address, city, state or ZIP',
+    defaultMessage: 'Search for an address, city, state, ZIP or Census Tract',
     description: 'On the explore the map page, on the map, the placeholder text for search',
   },
   SEARCH_PLACEHOLDER_MOBILE: {
@@ -108,7 +108,7 @@ export const MAP = defineMessages({
   LOWER48_SHORT: {
     id: 'explore.map.page.map.territoryFocus.lower48.short',
     defaultMessage: '48',
-    description: `On the explore the map page, on the map, the abbreviated name indicating the bounds of 
+    description: `On the explore the map page, on the map, the abbreviated name indicating the bounds of
       the Lower 48 states
     `,
   },
@@ -256,7 +256,7 @@ export const SIDE_PANEL_INITIAL_STATE = defineMessages({
   PARA1_PART3: {
     id: 'explore.map.page.side.panel.info.para.1.part.3',
     defaultMessage: `
-      , or locate yourself 
+      , or locate yourself
     `,
     description: '`Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show or locate yourself',
   },
@@ -291,7 +291,7 @@ export const SIDE_PANEL_INITIAL_STATE = defineMessages({
   PARA2_PART2: {
     id: 'explore.map.page.side.panel.info.para.2.part.2',
     defaultMessage: `
-    . Census tracts are a small unit of geography. They generally have populations 
+    . Census tracts are a small unit of geography. They generally have populations
     `,
     description: '`Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Census tracts are a small unit of geography. They generally have populations of between 1,200 - 8,000 people.',
   },
@@ -305,7 +305,7 @@ export const SIDE_PANEL_INITIAL_STATE = defineMessages({
   PARA3_PART1: {
     id: 'explore.map.page.side.panel.info.para.3.part.1',
     defaultMessage: `
-      Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted 
+      Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted
     `,
     description: '`Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Communities that are disadvantaged live in tracts that experience burdens. These tracts are highlighted on the map.',
   },
@@ -340,7 +340,7 @@ export const SIDE_PANEL_INITIAL_STATE = defineMessages({
   PARA5_PART2: {
     id: 'explore.map.page.side.panel.info.para.2',
     defaultMessage: `
-    , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages 
+    , or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages
     `,
     description: '`Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Thresholds, or cutoffs, are used to determine if communities in a tract are disadvantaged. Certain burdens use percentages or a simple yes/no.',
   },
@@ -598,7 +598,7 @@ export const SIDE_PANEL_TRIBAL_INFO = defineMessages({
   LAND_AREA_NAME: {
     id: 'explore.map.page.side.panel.tribalInfo.landAreaName',
     defaultMessage: 'Land Area Name:',
-    description: `Navigate to the explore the map page. Click on Tribal Lands, when the map is in view, 
+    description: `Navigate to the explore the map page. Click on Tribal Lands, when the map is in view,
     click on the map. The side panel will show the land area name of the feature selected`,
   },
 });
@@ -644,6 +644,14 @@ export const PRIORITIZATION_COPY = {
       bold: boldFn,
     }}
   />,
+  PRIO_ISLAND_LI: <FormattedMessage
+    id={'explore.map.page.side.panel.prio.copy.prio.island.li'}
+    defaultMessage={'This tract is considered disadvantaged because it meets the low income threshold <bold>AND</bold> is located in a U.S. Territory.'}
+    description={`Navigate to the explore the map page. Click on tract, The side panel will show This tract is considered disadvantaged. It is an island territory that meets an adjusted low income threshold.`}
+    values={{
+      bold: boldFn,
+    }}
+  />,
   PRIO_SURR_LI: <FormattedMessage
     id={'explore.map.page.side.panel.prio.copy.prio.donut'}
     defaultMessage={'This tract is considered disadvantaged. It is completely surrounded by tracts that are disadvantaged <bold>AND</bold> meets an adjusted low income threshold. The adjustment does not apply to any of the categories.'}
@@ -1064,7 +1072,7 @@ export const SIDE_PANEL_INDICATORS = defineMessages({
   POVERTY: {
     id: 'explore.map.page.side.panel.indicator.poverty',
     defaultMessage: 'Poverty',
-    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Unemployment`,
+    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Poverty`,
   },
   UNEMPLOY: {
     id: 'explore.map.page.side.panel.indicator.unemploy',
@@ -1147,7 +1155,7 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({
   EXP_BLD_LOSS: {
     id: 'explore.map.page.side.panel.indicator.description.exp.bld.loss',
     defaultMessage: 'Economic loss to building value resulting from natural hazards each year',
-    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side 
+    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side
     panel will show an indicator description of Economic loss rate to buildings resulting from natural hazards`,
   },
   EXP_POP_LOSS: {
@@ -1155,7 +1163,7 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({
     defaultMessage: `
       Fatalities and injuries resulting from natural hazards each year
     `,
-    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Economic loss rate to the population in fatalities and 
+    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Economic loss rate to the population in fatalities and
       injuries resulting from natural hazards`,
   },
   FLOODING: {
@@ -1175,17 +1183,17 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({
   LOW_INCOME: {
     id: 'explore.map.page.side.panel.indicator.description.low.income',
     defaultMessage: `
-     People in households where income is less than or equal to twice the federal poverty level, not including students enrolled in higher ed 
+     People in households where income is less than or equal to twice the federal poverty level, not including students enrolled in higher ed
     `,
     description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description ofPeople in households where income is less than or equal to twice the federal poverty level, not including students enrolled in higher ed`,
   },
   HIGH_ED: {
     id: 'explore.map.page.side.panel.indicator.description.high.ed',
     defaultMessage: `
-      Percent of the census tract's population 15 or older not enrolled in college, university, or 
-      graduate school 
+      Percent of the census tract's population 15 or older not enrolled in college, university, or
+      graduate school
     `,
-    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Percent of the census tract's population 15 or older not 
+    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Percent of the census tract's population 15 or older not
       enrolled in college, university, or graduate school`,
   },
 
@@ -1252,7 +1260,7 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({
     defaultMessage: `
       Share of homes that are likely to have lead paint
     `,
-    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of homes that are likely to have lead paint   
+    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of homes that are likely to have lead paint
     `,
   },
 
@@ -1330,14 +1338,14 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({
   POVERTY: {
     id: 'explore.map.page.side.panel.indicator.description.poverty',
     defaultMessage: `
-      Share of people in households where income is at or below 100% of the Federal poverty level 
+      Share of people in households where income is at or below 100% of the Federal poverty level
     `,
     description: `Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of people in households where the income is at or below 100% of the Federal poverty level`,
   },
   UNEMPLOY: {
     id: 'explore.map.page.side.panel.indicator.description.unemploy',
     defaultMessage: 'Number of unemployed people as a part of the labor force',
-    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side 
+    description: `Navigate to the explore the map page. When the map is in view, click on the map. The side
     panel will show an indicator description of Number of unemployed people as a part of the labor force`,
   },
   HIGH_SKL: {
@@ -1398,7 +1406,7 @@ export const DOWNLOAD_DRAFT = {
       <link1>Download the data sources</link1> used in the CEJST (.csv, .xlxs, .pdf that describes how to use the list, and a codebook, {downloadFileSize} unzipped). Last updated: {dateUpdated}.
     `}
     description={`
-      Navigate to the explore the map page. Under the map, you will see a link that is placed below the 
+      Navigate to the explore the map page. Under the map, you will see a link that is placed below the
       map that will download the data packet
     `}
     values={{
diff --git a/client/src/data/copy/previousVer.tsx b/client/src/data/copy/previousVer.tsx
index 42e4db31..5f7679dd 100644
--- a/client/src/data/copy/previousVer.tsx
+++ b/client/src/data/copy/previousVer.tsx
@@ -1,6 +1,6 @@
 import React from 'react';
 import {FormattedDate, FormattedMessage, defineMessages} from 'gatsby-plugin-intl';
-import {METH_1_0_RELEASE_DATE, METH_BETA_RELEASE_DATE} from './common';
+import {METH_2_0_RELEASE_DATE, METH_BETA_RELEASE_DATE} from './common';
 
 export const PAGE = defineMessages({
   TITLE: {
@@ -29,7 +29,7 @@ export const CARD = {
         day="numeric"
       />,
       releaseDate: <FormattedDate
-        value={METH_1_0_RELEASE_DATE}
+        value={METH_2_0_RELEASE_DATE}
         year="numeric"
         month="short"
         day="numeric"
diff --git a/client/src/intl/en.json b/client/src/intl/en.json
index 71c56013..faec8fff 100644
--- a/client/src/intl/en.json
+++ b/client/src/intl/en.json
@@ -688,7 +688,7 @@
     "description": "On the explore the map page, on the map, the placeholder text for search"
   },
   "explore.map.page.map.search.placeholder.text": {
-    "defaultMessage": "Search for an address, city, state or ZIP",
+    "defaultMessage": "Search for an address, city, state, ZIP or Census Tract",
     "description": "On the explore the map page, on the map, the placeholder text for search"
   },
   "explore.map.page.map.search.results.empty.text": {
@@ -741,7 +741,7 @@
   },
   "explore.map.page.map.territoryFocus.lower48.short": {
     "defaultMessage": "48",
-    "description": "On the explore the map page, on the map, the abbreviated name indicating the bounds of \n      the Lower 48 states\n    "
+    "description": "On the explore the map page, on the map, the abbreviated name indicating the bounds of\n      the Lower 48 states\n    "
   },
   "explore.map.page.map.territoryFocus.puerto_rico.long": {
     "defaultMessage": "Puerto Rico",
@@ -933,11 +933,11 @@
   },
   "explore.map.page.side.panel.indicator.description.exp.bld.loss": {
     "defaultMessage": "Economic loss to building value resulting from natural hazards each year",
-    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side \n    panel will show an indicator description of Economic loss rate to buildings resulting from natural hazards"
+    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side\n    panel will show an indicator description of Economic loss rate to buildings resulting from natural hazards"
   },
   "explore.map.page.side.panel.indicator.description.exp.pop.loss": {
     "defaultMessage": "Fatalities and injuries resulting from natural hazards each year",
-    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Economic loss rate to the population in fatalities and \n      injuries resulting from natural hazards"
+    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Economic loss rate to the population in fatalities and\n      injuries resulting from natural hazards"
   },
   "explore.map.page.side.panel.indicator.description.flooding": {
     "defaultMessage": "Projected risk to properties from projected floods, from tides, rain, riverine and storm surges within 30 years",
@@ -953,7 +953,7 @@
   },
   "explore.map.page.side.panel.indicator.description.high.ed": {
     "defaultMessage": "Percent of the census tract's population 15 or older not enrolled in college, university, or graduate school",
-    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Percent of the census tract's population 15 or older not \n      enrolled in college, university, or graduate school"
+    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Percent of the census tract's population 15 or older not\n      enrolled in college, university, or graduate school"
   },
   "explore.map.page.side.panel.indicator.description.high.school": {
     "defaultMessage": "Percent of people ages 25 years or older whose high school education is less than a high school diploma",
@@ -977,7 +977,7 @@
   },
   "explore.map.page.side.panel.indicator.description.leadPaint": {
     "defaultMessage": "Share of homes that are likely to have lead paint",
-    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of homes that are likely to have lead paint   \n    "
+    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show an indicator description of Share of homes that are likely to have lead paint\n    "
   },
   "explore.map.page.side.panel.indicator.description.leaky.tanks": {
     "defaultMessage": "Formula of the density of leaking underground storage tanks and number of all active underground storage tanks within 1500 feet of the census tract boundaries",
@@ -1025,7 +1025,7 @@
   },
   "explore.map.page.side.panel.indicator.description.unemploy": {
     "defaultMessage": "Number of unemployed people as a part of the labor force",
-    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side \n    panel will show an indicator description of Number of unemployed people as a part of the labor force"
+    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side\n    panel will show an indicator description of Number of unemployed people as a part of the labor force"
   },
   "explore.map.page.side.panel.indicator.description.wasteWater": {
     "defaultMessage": "Modeled toxic concentrations at parts of streams within 500 meters",
@@ -1145,7 +1145,7 @@
   },
   "explore.map.page.side.panel.indicator.poverty": {
     "defaultMessage": "Poverty",
-    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Unemployment"
+    "description": "Navigate to the explore the map page. When the map is in view, click on the map. The side panel will show Poverty"
   },
   "explore.map.page.side.panel.indicator.prox.haz": {
     "defaultMessage": "Proximity to hazardous waste facilities",
@@ -1427,6 +1427,10 @@
     "defaultMessage": "The {numPoints} that are Federally Recognized Tribes in this tract are are {also} considered disadvantaged.",
     "description": "Navigate to the explore the map page. Click on tract, The {numPoints} that are Federally Recognized Tribes in this tract ares are {also} considered disadvantaged."
   },
+  "explore.map.page.side.panel.prio.copy.prio.island.li": {
+    "defaultMessage": "This tract is considered disadvantaged because it meets the low income threshold <bold>AND</bold> is located in a U.S. Territory.",
+    "description": "Navigate to the explore the map page. Click on tract, The side panel will show This tract is considered disadvantaged. It is an island territory that meets an adjusted low income threshold."
+  },
   "explore.map.page.side.panel.prio.copy.prio.n.burden": {
     "defaultMessage": "This tract is considered disadvantaged because it meets {burdens} burden threshold <bold>AND</bold> the associated socioeconomic threshold.",
     "description": "Navigate to the explore the map page. Click on tract, This tract is considered disadvantaged because it meets {burdens} burden thresholds <bold>AND</bold> the associated socioeconomic threshold."
@@ -1453,7 +1457,7 @@
   },
   "explore.map.page.side.panel.tribalInfo.landAreaName": {
     "defaultMessage": "Land Area Name:",
-    "description": "Navigate to the explore the map page. Click on Tribal Lands, when the map is in view, \n    click on the map. The side panel will show the land area name of the feature selected"
+    "description": "Navigate to the explore the map page. Click on Tribal Lands, when the map is in view,\n    click on the map. The side panel will show the land area name of the feature selected"
   },
   "explore.map.page.side.panel.version.title": {
     "defaultMessage": "Methodology version {version}",
@@ -1465,7 +1469,7 @@
   },
   "explore.map.page.under.map.download.draft.ptag.1": {
     "defaultMessage": "<link1>Download the data sources</link1> used in the CEJST (.csv, .xlxs, .pdf that describes how to use the list, and a codebook, {downloadFileSize} unzipped). Last updated: {dateUpdated}.",
-    "description": "\n      Navigate to the explore the map page. Under the map, you will see a link that is placed below the \n      map that will download the data packet\n    "
+    "description": "\n      Navigate to the explore the map page. Under the map, you will see a link that is placed below the\n      map that will download the data packet\n    "
   },
   "explore.map.page.under.map.note.on.territories.intro": {
     "defaultMessage": "U.S. territories note",
diff --git a/client/src/intl/es.json b/client/src/intl/es.json
index 94b5188d..7bd2b14d 100644
--- a/client/src/intl/es.json
+++ b/client/src/intl/es.json
@@ -26,10 +26,10 @@
   "about.page.title.text": "Información básica",
   "about.page.use.data.heading": "Cómo utilizar los datos ",
   "about.page.use.data.paragraph": "Los datos de la herramienta están disponibles para <link1>descargar</link1>. Estos datos se pueden usar para filtrar por estado o condado.",
-  "about.page.use.data.tutorial":"Descarga el tutorial de la hoja de cálculo (.pdf 5,4 MB)",
+  "about.page.use.data.tutorial": "Descarga el tutorial de la hoja de cálculo (.pdf 5,4 MB)",
   "about.page.use.map.heading": "Utilización del mapa",
   "about.page.use.map.para": "Amplíe y seleccione cualquier distrito censal para ver si se considera como desfavorecido.",
-  "about.page.use.map.tutorial":"Descarga el tutorial CEJST (.pdf 9.6 MB)",
+  "about.page.use.map.tutorial": "Descarga el tutorial CEJST (.pdf 9.6 MB)",
   "common.pages.alerts.additional_docs_available.description": "Descargue un nuevo <link1>documento de apoyo técnico</link1> y otra documentación, y <link2>envíe sus comentarios</link2>.",
   "common.pages.alerts.banner.beta.content": "<bold1>Herramienta actualizada.</bold1> La versión 1.0 de la herramienta fue publicada el {relDate}.",
   "common.pages.alerts.census.tract.title": "Ya hay más documentación disponible",
@@ -172,7 +172,7 @@
   "explore.map.page.map.layer.selector.tribal.long": "Tierras tribales",
   "explore.map.page.map.layer.selector.tribal.short": "Tribal",
   "explore.map.page.map.search.placeholder.mobile.text": "Búsqueda de ubicaciones ",
-  "explore.map.page.map.search.placeholder.text": "Busque una dirección, ciudad, estado o código postal.",
+  "explore.map.page.map.search.placeholder.text": "Busque una dirección, ciudad, estado, código postal o distrito censal.",
   "explore.map.page.map.search.results.empty.text": "No se encontró la ubicación o ubicación desconocida. Intente una búsqueda distinta.",
   "explore.map.page.map.territoryFocus.alaska.long": "Alaska",
   "explore.map.page.map.territoryFocus.alaska.short": "AK",
@@ -348,6 +348,7 @@
   "explore.map.page.side.panel.num.categories.exceeded": "en {numberOfDisCategories, plural, one {# categoría} otras {# categorías}}",
   "explore.map.page.side.panel.partial.community.of.focus": "PARCIALMENTE",
   "explore.map.page.side.panel.prio.copy.not.prio": "Este distrito censal no se considera desfavorecido. No cumple con ninguno de los umbrales <bold>O</bold> con al menos un umbral socioeconómico relacionado.",
+  "explore.map.page.side.panel.prio.copy.prio.island.li": "Este distrito censal se considera desfavorecido porque cumple con el umbral de bajos ingresos <bold>Y</bold> está ubicado en un territorio de los EE. UU.",
   "explore.map.page.side.panel.prio.copy.not.prio.n.burden": "Este distrito censal no se considera desfavorecido. Cumple con más de 1 umbral de carga <bold>PERO</bold> con ningún umbral socioeconómico asociado.",
   "explore.map.page.side.panel.prio.copy.not.prio.one.burden": "Este distrito censal no se considera desfavorecido. Cumple con 1 umbral de carga <bold>PERO</bold> con ningún umbral socioeconómico asociado.",
   "explore.map.page.side.panel.prio.copy.not.prio.surr.li": "Este distrito censal no se considera desfavorecido. Está rodeado de distritos censales desfavorecidos <bold>PERO</bold> no cumple con el umbral ajustado de bajos ingresos. El ajuste no corresponde a ninguna de las categorías.",
diff --git a/client/src/pages/tests/__snapshots__/about.test.tsx.snap b/client/src/pages/tests/__snapshots__/about.test.tsx.snap
index c9391b5b..beb8df29 100644
--- a/client/src/pages/tests/__snapshots__/about.test.tsx.snap
+++ b/client/src/pages/tests/__snapshots__/about.test.tsx.snap
@@ -490,7 +490,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                   <a
                     class="usa-link usa-link--external"
                     data-cy=""
-                    href="https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/CEQ-CEJST-Instructions.pdf"
+                    href="undefined/data-versions/2.0/data/score/downloadable/CEQ-CEJST-Instructions.pdf"
                     rel="noreferrer"
                     target="_blank"
                   >
@@ -614,7 +614,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                     <a
                       class="usa-link usa-link--external"
                       data-cy=""
-                      href="https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Tutorial.pdf"
+                      href="undefined/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Tutorial.pdf"
                       rel="noreferrer"
                       target="_blank"
                     >
@@ -681,7 +681,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                     <a
                       class="usa-link usa-link--external"
                       data-cy=""
-                      href="https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial.pdf"
+                      href="undefined/data-versions/2.0/data/score/downloadable/Using-the-CEJST-Spreadsheet-Tutorial.pdf"
                       rel="noreferrer"
                       target="_blank"
                     >
diff --git a/client/src/pages/tests/__snapshots__/freqAskedQuestions.test.tsx.snap b/client/src/pages/tests/__snapshots__/freqAskedQuestions.test.tsx.snap
index a337eaa5..d3d90ca8 100644
--- a/client/src/pages/tests/__snapshots__/freqAskedQuestions.test.tsx.snap
+++ b/client/src/pages/tests/__snapshots__/freqAskedQuestions.test.tsx.snap
@@ -490,7 +490,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                     <a
                       class="usa-link usa-link--external"
                       data-cy=""
-                      href="https://static-data-screeningtool.geoplatform.gov/data-versions/1.0/data/score/downloadable/CEQ-CEJST-Instructions.pdf"
+                      href="undefined/data-versions/2.0/data/score/downloadable/CEQ-CEJST-Instructions.pdf"
                       rel="noreferrer"
                       target="_blank"
                     >
diff --git a/data/data-pipeline/.flake8 b/data/data-pipeline/.flake8
index f217b57d..c9ae846f 100644
--- a/data/data-pipeline/.flake8
+++ b/data/data-pipeline/.flake8
@@ -1,9 +1,11 @@
 [flake8]
+# E266, to many leading '#' for block comment
+# W503, line break before binary operator
+# TODO: Uncomment and fix the issues here that we care about, see
+# https://github.com/usds/justice40-tool/issues/1123
 ignore =
-  E266,  # to many leading '#' for block comment
-  W503,  # line break before binary operator
-  # TODO: Uncomment and fix the issues here that we care about, see
-  # https://github.com/usds/justice40-tool/issues/1123
+  E266,
+  W503,
   PD002,
   PD003,
   PD004,
diff --git a/data/data-pipeline/data_pipeline/application.py b/data/data-pipeline/data_pipeline/application.py
index cde48ae1..66323542 100644
--- a/data/data-pipeline/data_pipeline/application.py
+++ b/data/data-pipeline/data_pipeline/application.py
@@ -33,6 +33,31 @@ dataset_cli_help = "Grab the data from either 'local' for local access or 'aws'
 
 LOG_LINE_WIDTH = 60
 
+use_cache_option = click.option(
+    "-u",
+    "--use-cache",
+    is_flag=True,
+    default=False,
+    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
+)
+
+dataset_option = click.option(
+    "-d",
+    "--dataset",
+    required=False,
+    type=str,
+    help=dataset_cli_help,
+)
+
+data_source_option = click.option(
+    "-s",
+    "--data-source",
+    default="local",
+    required=False,
+    type=str,
+    help=dataset_cli_help,
+)
+
 
 @click.group()
 def cli():
@@ -51,7 +76,6 @@ def census_cleanup():
     census_reset(data_path)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(help="Clean up all data folders")
@@ -70,7 +94,6 @@ def data_cleanup():
     geo_score_folder_cleanup()
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
@@ -82,13 +105,7 @@ def data_cleanup():
     is_flag=True,
     help="Upload to AWS S3 a zipped archive of the census data.",
 )
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@use_cache_option
 def census_data_download(zip_compress, use_cache):
     """CLI command to download all census shape files from the Census FTP and extract the geojson
     to generate national and by state Census Block Group CSVs"""
@@ -105,18 +122,10 @@ def census_data_download(zip_compress, use_cache):
         zip_census_data()
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(help="Retrieve census data from source")
-@click.option(
-    "-s",
-    "--data-source",
-    default="local",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@data_source_option
 def pull_census_data(data_source: str):
 
     log_title("Pull Census Data")
@@ -126,26 +135,13 @@ def pull_census_data(data_source: str):
     check_census_data_source(data_path, data_source)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Run all ETL processes or a specific one",
 )
-@click.option(
-    "-d",
-    "--dataset",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@dataset_option
+@use_cache_option
 def etl_run(dataset: str, use_cache: bool):
     """Run a specific or all ETL processes
 
@@ -161,7 +157,6 @@ def etl_run(dataset: str, use_cache: bool):
     etl_runner(dataset, use_cache)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
@@ -178,19 +173,12 @@ def score_run():
     score_generate()
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Run ETL + Score Generation",
 )
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@use_cache_option
 def score_full_run(use_cache: bool):
     """CLI command to run ETL and generate the score in one command"""
     log_title("Score Full Run", "Run ETL and Generate Score (no tiles)")
@@ -207,20 +195,12 @@ def score_full_run(use_cache: bool):
     score_generate()
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Run etl_score_post to create score csv, tile csv, and downloadable zip"
 )
-@click.option(
-    "-s",
-    "--data-source",
-    default="local",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@data_source_option
 def generate_score_post(data_source: str):
     """CLI command to generate score, tile, and downloadable files
 
@@ -244,18 +224,10 @@ def generate_score_post(data_source: str):
     score_post(data_source)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(help="Generate GeoJSON files with scores baked in")
-@click.option(
-    "-s",
-    "--data-source",
-    default="local",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@data_source_option
 def geo_score(data_source: str):
     """CLI command to combine score with GeoJSON data and generate low and high files
 
@@ -280,7 +252,6 @@ def geo_score(data_source: str):
     score_geo(data_source=data_source)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
@@ -304,7 +275,6 @@ def generate_map_tiles(generate_tribal_layer):
     generate_tiles(data_path, generate_tribal_layer)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
@@ -316,21 +286,8 @@ def generate_map_tiles(generate_tribal_layer):
     is_flag=True,
     help="Check if data run has been run before, and don't run it if so.",
 )
-@click.option(
-    "-s",
-    "--data-source",
-    default="local",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@data_source_option
+@use_cache_option
 def data_full_run(check: bool, data_source: str, use_cache: bool):
     """CLI command to run ETL, score, JSON combine and generate tiles in one command
 
@@ -388,19 +345,12 @@ def data_full_run(check: bool, data_source: str, use_cache: bool):
     call(cmd, shell=True)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Print data sources for all ETL processes (or a specific one)",
 )
-@click.option(
-    "-d",
-    "--dataset",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@dataset_option
 def print_data_sources(dataset: str):
     """Print data sources for all ETL processes (or a specific one)
 
@@ -421,26 +371,13 @@ def print_data_sources(dataset: str):
         log_info(s)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Fetch data sources for all ETL processes (or a specific one)",
 )
-@click.option(
-    "-d",
-    "--dataset",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
-@click.option(
-    "-u",
-    "--use-cache",
-    is_flag=True,
-    default=False,
-    help="Check if data source has been downloaded already, and if it has, use the cached version of the data source.",
-)
+@dataset_option
+@use_cache_option
 def extract_data_sources(dataset: str, use_cache: bool):
     """Extract and cache data source(s) for all ETL processes (or a specific one)
 
@@ -457,19 +394,12 @@ def extract_data_sources(dataset: str, use_cache: bool):
     extract_ds(dataset, use_cache)
 
     log_goodbye()
-    sys.exit()
 
 
 @cli.command(
     help="Clear data source cache for all ETL processes (or a specific one)",
 )
-@click.option(
-    "-d",
-    "--dataset",
-    required=False,
-    type=str,
-    help=dataset_cli_help,
-)
+@dataset_option
 def clear_data_source_cache(dataset: str):
     """Clear data source(s) cache for all ETL processes (or a specific one)
 
@@ -485,7 +415,33 @@ def clear_data_source_cache(dataset: str):
     clear_ds_cache(dataset)
 
     log_goodbye()
-    sys.exit()
+
+
+@cli.command(
+    help="Generate scoring and tiles",
+)
+@click.pass_context
+def full_post_etl(ctx):
+    """Generate scoring and tiles"""
+    ctx.invoke(score_run)
+    ctx.invoke(generate_score_post, data_source=None)
+    ctx.invoke(geo_score, data_source=None)
+    ctx.invoke(generate_map_tiles, generate_tribal_layer=False)
+
+
+@cli.command(
+    help="Run all downloads, extracts, and generate scores and tiles",
+)
+@use_cache_option
+@click.pass_context
+def full_run(ctx, use_cache):
+    """Run all downloads, ETLs, and generate scores and tiles"""
+    if not use_cache:
+        ctx.invoke(data_cleanup)
+    ctx.invoke(census_data_download, zip_compress=False, use_cache=use_cache)
+    ctx.invoke(extract_data_sources, dataset=None, use_cache=use_cache)
+    ctx.invoke(etl_run, dataset=None, use_cache=use_cache)
+    ctx.invoke(full_post_etl)
 
 
 def log_title(title: str, subtitle: str = None):
diff --git a/data/data-pipeline/data_pipeline/comparator.py b/data/data-pipeline/data_pipeline/comparator.py
index 09af4e8d..860be7bb 100644
--- a/data/data-pipeline/data_pipeline/comparator.py
+++ b/data/data-pipeline/data_pipeline/comparator.py
@@ -1,11 +1,12 @@
 import sys
 import click
-import difflib
 import pandas as pd
+from pathlib import Path
 
 from data_pipeline.etl.score import constants
 from data_pipeline.utils import get_module_logger, download_file_from_url
 from data_pipeline.application import log_title, log_info, log_goodbye
+from data_pipeline.score import field_names
 
 logger = get_module_logger(__name__)
 
@@ -15,6 +16,42 @@ pd.set_option("display.max_rows", None)
 pd.set_option("display.width", 10000)
 pd.set_option("display.colheader_justify", "left")
 
+result_text = []
+
+
+def _add_text(text: str):
+    """Add a line to the output result.
+
+    Args:
+        line (str): a line to add
+    """
+    result_text.append(text)
+
+
+def _get_result_doc() -> str:
+    """Gets the document with results.
+
+    Returns:
+        str: the results document as text
+    """
+    return "".join(result_text)
+
+
+def _read_from_file(file_path: Path):
+    """Read a CSV file into a Dataframe."""
+    if not file_path.is_file():
+        logger.error(
+            f"- No score file exists at {file_path}. "
+            "Please generate the score and try again."
+        )
+        sys.exit(1)
+    return pd.read_csv(
+        file_path,
+        index_col="GEOID10_TRACT",
+        dtype={"GEOID10_TRACT": str},
+        low_memory=False,
+    ).sort_index()
+
 
 @click.group()
 def cli():
@@ -33,8 +70,24 @@ def cli():
     default="1.0",
     required=False,
     type=str,
+    help="Set the production score version to compare to",
 )
-def compare_score(compare_to_version: str):
+@click.option(
+    "-f",
+    "--compare_to_file",
+    type=click.Path(exists=True, dir_okay=False, path_type=Path),
+    help="Compare to the specified score CSV file instead of downloading from production",
+)
+@click.option(
+    "-l",
+    "--local_score_file",
+    type=click.Path(exists=True, dir_okay=False, path_type=Path),
+    default=constants.DATA_SCORE_CSV_FULL_FILE_PATH,
+    help="Compare to the specified score CSV file instead of downloading from production",
+)
+def compare_score(
+    compare_to_version: str, compare_to_file: str, local_score_file: str
+):
     """Compares the score in the production environment to the locally generated score. The
     algorithm is pretty simple:
 
@@ -50,43 +103,27 @@ def compare_score(compare_to_version: str):
     FLOAT_ROUNDING_PLACES = 2
     WORKING_PATH = constants.TMP_PATH / "Comparator" / "Score"
 
-    summary = "# Score Comparison Summary\n"
-    summary += f"Hi! I'm the Score Comparator. I compared the score in production (version {compare_to_version}) to the"
-    summary += " locally calculated score. Here are the results.\n"
-
     log_title("Compare Score", "Compare production score to local score")
 
-    locally_generated_score_path = constants.DATA_SCORE_CSV_FULL_FILE_PATH
-    if not locally_generated_score_path.is_file():
-        logger.error(
-            f"- No score file exists at {locally_generated_score_path}. Please generate the score and try again."
+    if compare_to_file:
+        log_info(f"Comparing to file {compare_to_file}...")
+        production_score_path = compare_to_file
+    else:
+        # TODO: transition to downloader code when it's available
+        production_score_url = f"https://justice40-data.s3.amazonaws.com/data-versions/{compare_to_version}/data/score/csv/full/usa.csv"
+        production_score_path = WORKING_PATH / "usa.csv"
+
+        log_info(f"Fetching score version {compare_to_version} from AWS")
+        production_score_path.parent.mkdir(parents=True, exist_ok=True)
+        download_file_from_url(
+            file_url=production_score_url,
+            download_file_name=production_score_path,
         )
-        sys.exit(1)
 
-    # TODO: transition to downloader code when it's available
-    production_score_url = f"https://justice40-data.s3.amazonaws.com/data-versions/{compare_to_version}/data/score/csv/full/usa.csv"
-    production_score_path = WORKING_PATH / "usa.csv"
-
-    log_info(f"Fetching score version {compare_to_version} from AWS")
-    production_score_path.parent.mkdir(parents=True, exist_ok=True)
-    download_file_from_url(
-        file_url=production_score_url, download_file_name=production_score_path
-    )
-
-    log_info("Loading files into pandas for comparisons")
-
-    local_score_df = pd.read_csv(
-        locally_generated_score_path,
-        index_col="GEOID10_TRACT",
-        dtype={"GEOID10_TRACT": str},
-        low_memory=False,
-    ).sort_index()
-    production_score_df = pd.read_csv(
-        production_score_path,
-        index_col="GEOID10_TRACT",
-        dtype={"GEOID10_TRACT": str},
-        low_memory=False,
-    ).sort_index()
+    log_info(f"Loading local score from {local_score_file}")
+    local_score_df = _read_from_file(local_score_file)
+    log_info(f"Loading production score from {production_score_path}")
+    production_score_df = _read_from_file(production_score_path)
 
     # Because of variations in Python versions and machine-level calculations, some of
     # our numbers can be really close but not the same. That throws off our comparisons.
@@ -99,67 +136,82 @@ def compare_score(compare_to_version: str):
     production_score_df_columns = sorted(
         production_score_df.columns.array.tolist()
     )
-
-    log_info("Comparing columns (production vs local). Differences are: ")
-    summary += "\n## Columns\n"
-    summary += "I compared the columns. Here's what I found.\n"
-
-    col_diff = difflib.unified_diff(
-        production_score_df_columns, local_score_df_columns
+    extra_cols_in_local = set(local_score_df_columns) - set(
+        production_score_df_columns
+    )
+    extra_cols_in_prod = set(production_score_df_columns) - set(
+        local_score_df_columns
     )
-    col_diff_res = ""
-    for d in col_diff:
-        col_diff_res += str(d) + "\n"
 
-    if len(col_diff_res) == 0:
-        log_info("None. Columns are the same")
-        summary += "* There are no differences in the column names.\n"
+    _add_text("# Score Comparison Summary\n")
+    _add_text(
+        f"Hi! I'm the Score Comparator. I compared the score in production (version {compare_to_version}) to the"
+        " locally calculated score. Here are the results:\n\n"
+    )
+
+    #####################
+    # Compare the columns
+    #####################
+    log_info("Comparing columns (production vs local)")
+    _add_text("## Columns\n")
+    if len(extra_cols_in_local) == 0 and len(extra_cols_in_prod) == 0:
+        _add_text("* There are no differences in the column names.\n")
     else:
-        log_info("There are differences. The diff is:")
-        log_info(col_diff_res)
-        summary += f"* There are differences in the column names. Here's a diff:\n{col_diff_res}\n"
+        _add_text(
+            f"* There are {len(extra_cols_in_local)} columns that were added as compared to the production score."
+        )
+        if len(extra_cols_in_local) > 0:
+            _add_text(f" Those colums are:\n{extra_cols_in_local}")
+        _add_text(
+            f"\n* There are {len(extra_cols_in_prod)} columns that were removed as compared to the production score."
+        )
+        if len(extra_cols_in_prod) > 0:
+            _add_text(f" Those colums are:\n{extra_cols_in_prod}")
 
+    ####################
+    # Compare the scores
+    ####################
     log_info("Comparing dataframe contents (production vs local)")
-    summary += "\n## Scores\n"
-    summary += "I compared the scores, too. Here's what I found.\n"
+    _add_text("\n\n## Scores\n")
 
     production_row_count = len(production_score_df.index)
     local_row_count = len(local_score_df.index)
 
-    summary += f"* The production score has {production_row_count:,} census tracts, and the freshly calculated score has {local_row_count:,}."
-    summary += (
-        " They match!\n"
-        if production_row_count == local_row_count
-        else " They don't match.\n"
+    # Tract comparison
+    _add_text(
+        f"* The production score has {production_row_count:,} census tracts, and the freshly calculated score has {local_row_count:,}."
     )
+    if production_row_count == local_row_count:
+        _add_text(" They match!\n")
+    else:
+        _add_text(" They don't match. The differences are:\n")
+        _add_text(
+            "  * New tracts added to the local score are:\n"
+            f"{local_score_df.index.difference(production_score_df.index).to_list()}"
+            "\n  * Tracts removed from the local score are:\n"
+            f"{production_score_df.index.difference(local_score_df.index).to_list()}"
+            "\n"
+        )
 
-    production_total_population = production_score_df["Total population"].sum()
-    local_total_population = local_score_df["Total population"].sum()
+    # Population comparison
+    production_total_population = production_score_df[
+        field_names.TOTAL_POP_FIELD
+    ].sum()
+    local_total_population = local_score_df[field_names.TOTAL_POP_FIELD].sum()
 
-    log_info(
-        f"The total population in all census tracts in production is {production_total_population:,}."
+    _add_text(
+        f"* The total population in all census tracts in the production score is {production_total_population:,}. "
+        f"The total population in all census tracts locally is {local_total_population:,}. "
     )
-    log_info(
-        f"The total population in all census tracts locally is {local_total_population:,}."
-    )
-    log_info(
-        f"The difference in population is {abs(production_total_population - local_total_population):,}."
-    )
-
-    summary += f"* The total population in all census tracts in the production score is {production_total_population:,}."
-    summary += f" The total population in all census tracts locally is {local_total_population:,}."
-    summary += (
-        " They match!\n"
+    _add_text(
+        "They match!\n"
         if production_total_population == local_total_population
-        else f"  The difference is {abs(production_total_population - local_total_population):,}.\n"
+        else f"The difference is {abs(production_total_population - local_total_population):,}.\n"
     )
 
-    production_disadvantaged_tracts_df = production_score_df.query(
-        "`Definition N community, including adjacency index tracts` == True"
-    )
-    local_disadvantaged_tracts_df = local_score_df.query(
-        "`Definition N community, including adjacency index tracts` == True"
-    )
+    dacs_query = f"`{field_names.FINAL_SCORE_N_BOOLEAN}` == True"
+    production_disadvantaged_tracts_df = production_score_df.query(dacs_query)
+    local_disadvantaged_tracts_df = local_score_df.query(dacs_query)
 
     production_disadvantaged_tracts_set = set(
         production_disadvantaged_tracts_df.index.array
@@ -169,38 +221,27 @@ def compare_score(compare_to_version: str):
     )
 
     production_pct_of_population_represented = (
-        production_disadvantaged_tracts_df["Total population"].sum()
+        production_disadvantaged_tracts_df[field_names.TOTAL_POP_FIELD].sum()
         / production_total_population
     )
     local_pct_of_population_represented = (
-        local_disadvantaged_tracts_df["Total population"].sum()
+        local_disadvantaged_tracts_df[field_names.TOTAL_POP_FIELD].sum()
         / local_total_population
     )
 
-    log_info(
-        f"There are {len(production_disadvantaged_tracts_set):,} disadvantaged tracts in the production score."
+    # DACS comparison
+    _add_text(
+        f"* There are {len(production_disadvantaged_tracts_set):,} disadvantaged tracts in the production score representing"
+        f" {production_pct_of_population_represented:.1%} of the total population, and {len(local_disadvantaged_tracts_set):,}"
     )
-    log_info(
-        f"This represents {production_pct_of_population_represented:.1%} of the total population."
+    _add_text(
+        f" in the locally generated score representing {local_pct_of_population_represented:.1%} of the total population."
     )
-    log_info(
-        f"There are {len(local_disadvantaged_tracts_set):,} in the locally generated score."
-    )
-    log_info(
-        f"This represents {local_pct_of_population_represented:.1%} of the total population."
-    )
-    log_info(
-        f"The difference is {abs(len(production_disadvantaged_tracts_set) - len(local_disadvantaged_tracts_set)):,} tract(s)."
-    )
-
-    summary += f"* There are {len(production_disadvantaged_tracts_set):,} disadvantaged tracts in the production score representing"
-    summary += f" {production_pct_of_population_represented:.1%} of the total population, and {len(local_disadvantaged_tracts_set):,}"
-    summary += f" in the locally generated score representing {local_pct_of_population_represented:.1%} of the total population."
-    summary += (
-        " The number of tracts match!\n"
+    _add_text(
+        " The number of tracts match!\n "
         if len(production_disadvantaged_tracts_set)
         == len(local_disadvantaged_tracts_set)
-        else f" The difference is {abs(len(production_disadvantaged_tracts_set) - len(local_disadvantaged_tracts_set)):,} tract(s).\n"
+        else f" The difference is {abs(len(production_disadvantaged_tracts_set) - len(local_disadvantaged_tracts_set))} tract(s).\n "
     )
 
     removed_tracts = production_disadvantaged_tracts_set.difference(
@@ -209,53 +250,72 @@ def compare_score(compare_to_version: str):
     added_tracts = local_disadvantaged_tracts_set.difference(
         production_disadvantaged_tracts_set
     )
-
-    log_info(
-        f"There are {len(removed_tracts):,} tract(s) marked as disadvantaged in the prod score that are not disadvantaged in the local score."
-    )
-    log_info(
-        f"There are {len(added_tracts):,} tract(s) marked as disadvantaged in the local score that are not disadvantaged in the prod score."
-    )
-
-    summary += (
+    _add_text(
         f"* There are {len(removed_tracts):,} tract(s) marked as disadvantaged in the production score that are not disadvantaged in the locally"
-        " generated score (i.e. disadvantaged tracts that were removed by the new score)."
-        f" There are {len(added_tracts):,} tract(s) marked as disadvantaged in the locally generated score that are not disadvantaged in the"
-        " production score (i.e. disadvantaged tracts that were added by the new score).\n"
+        f" generated score (i.e. disadvantaged tracts that were removed by the new score). "
     )
+    if len(removed_tracts) > 0:
+        _add_text(f"Those tracts are:\n{removed_tracts}")
 
+    _add_text(
+        f"\n* There are {len(added_tracts):,} tract(s) marked as disadvantaged in the locally generated score that are not disadvantaged in the"
+        f" production score (i.e. disadvantaged tracts that were added by the new score). "
+    )
+    if len(added_tracts) > 0:
+        _add_text(f"Those tracts are:\n{added_tracts}\n")
+
+    # Grandfathered tracts from v1.0
+    grandfathered_tracts = local_score_df.loc[
+        local_score_df[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0]
+    ].index
+    if len(grandfathered_tracts) > 0:
+        _add_text(
+            f"* This includes {len(grandfathered_tracts)} grandfathered tract(s) from v1.0 scoring. They are:\n"
+            f"{grandfathered_tracts.to_list()}\n"
+        )
+    else:
+        _add_text("* There are NO grandfathered tracts from v1.0 scoring.\n")
+
+    ################
+    # Create a delta
+    ################
+    _add_text("\n## Delta\n")
+    # First we make the columns on two dataframes to be the same to be able to compare
+    trimmed_prod_df = production_score_df.drop(extra_cols_in_prod, axis=1)
+    trimmed_local_df = local_score_df.drop(extra_cols_in_local, axis=1)
     try:
 
-        comparison_results_df = production_score_df.compare(
-            local_score_df, align_axis=1, keep_shape=False, keep_equal=False
+        comparison_results_df = trimmed_prod_df.compare(
+            trimmed_local_df, align_axis=1, keep_shape=False, keep_equal=False
         ).rename({"self": "Production", "other": "Local"}, axis=1, level=1)
 
-        summary += "* I compared all values across all census tracts."
-        summary += f" There are {len(comparison_results_df.index):,} tracts with at least one difference."
-        summary += " Please examine the logs or run the score comparison locally to view them all.\n"
-        log_info(
-            f"There are {len(comparison_results_df.index)} rows with any differences."
+        _add_text(
+            "* I compared all values across all census tracts. Note this ignores any columns that have been added or removed."
+            f" There are {len(comparison_results_df.index):,} tracts with at least one difference.\n"
         )
 
-        log_info("Those differences are:")
-        log_info("\n" + str(comparison_results_df))
-
         comparison_path = WORKING_PATH / "deltas.csv"
         comparison_results_df.to_csv(path_or_buf=comparison_path)
 
-        log_info(f"Wrote comparison results to {comparison_path}")
+        _add_text(f"* Wrote comparison results to {comparison_path}")
 
     except ValueError as e:
-        summary += "* I could not run a full comparison. This is likely because there are column or index (census tract) differences."
-        summary += " Please examine the logs or run the score comparison locally to find out more.\n"
-        log_info(
-            f"Encountered an exception while performing the comparison: {repr(e)}"
+        _add_text(
+            "* I could not run a full comparison. This is likely because there are column or index (census tract) differences."
+            " Please examine the logs or run the score comparison locally to find out more.\n"
+        )
+        _add_text(
+            f"Encountered an exception while performing the comparison: {repr(e)}\n"
         )
 
+    result_doc = _get_result_doc()
+    print(result_doc)
+
+    # Write the report
     summary_path = WORKING_PATH / "comparison-summary.md"
 
     with open(summary_path, "w", encoding="utf-8") as f:
-        f.write(summary)
+        f.write(result_doc)
         log_info(f"Wrote comparison summary to {summary_path}")
 
     log_goodbye()
diff --git a/data/data-pipeline/data_pipeline/constants.py b/data/data-pipeline/data_pipeline/constants.py
new file mode 100644
index 00000000..8d4b422f
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/constants.py
@@ -0,0 +1,7 @@
+import logging
+
+LOG_LEVEL = logging.DEBUG
+"""Log level for all loggers."""
+
+NO_SSL_VERIFY = True
+"""Set to true to skip SSL verification when downloading files. Useful for local development."""
diff --git a/data/data-pipeline/data_pipeline/content/static_data/README.md b/data/data-pipeline/data_pipeline/content/static_data/README.md
new file mode 100644
index 00000000..840e2f78
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/content/static_data/README.md
@@ -0,0 +1,5 @@
+Static data is used in the computation of the score as stated.
+
+# v1.0-score-results-usa.csv
+This is the v1.0 score results used for grandfathering computations. It is a 
+stripped down version of the v1.0 score to include only the columns needed.
\ No newline at end of file
diff --git a/data/data-pipeline/data_pipeline/content/static_data/v1.0-score-results-usa.csv b/data/data-pipeline/data_pipeline/content/static_data/v1.0-score-results-usa.csv
new file mode 100644
index 00000000..79b1f26b
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/content/static_data/v1.0-score-results-usa.csv
@@ -0,0 +1,74135 @@
+GEOID10_TRACT,Total population,Is low income (imputed and adjusted)?,"Definition N community, including adjacency index tracts"
+01001020100,1993.0,False,False
+01001020200,1959.0,True,False
+01001020300,3507.0,False,False
+01001020400,3878.0,False,False
+01001020500,10596.0,False,False
+01001020600,3668.0,True,False
+01001020700,3586.0,True,False
+01001020801,3196.0,False,False
+01001020802,11142.0,False,False
+01001020900,6143.0,False,False
+01001021000,2527.0,True,False
+01001021100,3185.0,True,True
+01003010100,3433.0,True,True
+01003010200,3158.0,True,True
+01003010300,8284.0,False,False
+01003010400,5181.0,False,False
+01003010500,5463.0,False,False
+01003010600,3730.0,True,True
+01003010701,8756.0,False,False
+01003010703,19962.0,False,False
+01003010704,6456.0,True,False
+01003010705,9344.0,False,False
+01003010800,6856.0,False,False
+01003010903,6235.0,False,False
+01003010904,6605.0,False,False
+01003010905,8316.0,False,False
+01003010906,5188.0,True,False
+01003011000,4644.0,True,True
+01003011101,11802.0,False,False
+01003011102,4903.0,False,False
+01003011201,5091.0,False,False
+01003011202,5997.0,False,True
+01003011300,5269.0,False,False
+01003011401,11757.0,False,False
+01003011403,11169.0,False,False
+01003011405,4624.0,False,False
+01003011406,3140.0,False,False
+01003011407,5005.0,False,False
+01003011408,558.0,False,False
+01003011501,6931.0,False,False
+01003011502,12957.0,True,True
+01003011601,5873.0,False,False
+01003011602,6143.0,False,False
+01003990000,0.0,False,False
+01005950100,3091.0,True,True
+01005950200,3949.0,True,True
+01005950300,1598.0,True,True
+01005950400,3817.0,True,True
+01005950500,3285.0,True,True
+01005950600,1960.0,True,True
+01005950700,1521.0,True,True
+01005950800,2259.0,True,True
+01005950900,3881.0,True,True
+01007010001,2242.0,True,True
+01007010002,5694.0,True,True
+01007010003,4922.0,False,False
+01007010004,9635.0,True,True
+01009050101,7594.0,False,False
+01009050102,7108.0,True,True
+01009050200,4249.0,True,True
+01009050300,4972.0,True,True
+01009050400,3868.0,False,True
+01009050500,7840.0,True,True
+01009050601,4268.0,False,False
+01009050602,9148.0,False,False
+01009050700,8634.0,True,True
+01011952100,1579.0,True,True
+01011952200,5814.0,True,True
+01011952500,2855.0,True,True
+01013952700,1930.0,True,True
+01013952800,1278.0,False,True
+01013952900,2336.0,True,True
+01013953000,1565.0,True,True
+01013953100,2939.0,True,True
+01013953200,3610.0,True,True
+01013953300,1861.0,False,True
+01013953400,2765.0,True,True
+01013953500,1544.0,True,True
+01015000200,2324.0,True,True
+01015000300,2377.0,True,True
+01015000400,3143.0,True,True
+01015000500,1031.0,True,True
+01015000600,1893.0,True,True
+01015000700,2918.0,True,True
+01015000800,981.0,True,True
+01015000900,3617.0,True,True
+01015001000,5992.0,False,False
+01015001100,7389.0,False,False
+01015001201,2830.0,True,True
+01015001202,4260.0,True,True
+01015001300,2009.0,True,True
+01015001400,3105.0,True,True
+01015001500,5076.0,True,True
+01015001600,3621.0,True,True
+01015001700,6858.0,True,True
+01015001800,7051.0,False,True
+01015002000,7331.0,False,False
+01015002101,3327.0,False,True
+01015002102,3350.0,False,False
+01015002103,6784.0,True,True
+01015002200,3391.0,True,True
+01015002300,3454.0,True,True
+01015002400,4636.0,False,False
+01015002501,6719.0,False,False
+01015002502,4557.0,False,False
+01015002600,4594.0,False,False
+01015981901,0.0,False,False
+01015981902,0.0,False,False
+01015981903,0.0,False,False
+01017953800,3767.0,True,True
+01017953900,3128.0,True,True
+01017954000,5946.0,True,True
+01017954200,3445.0,True,True
+01017954300,5311.0,False,False
+01017954400,796.0,True,True
+01017954500,3883.0,True,True
+01017954600,3163.0,True,True
+01017954700,4221.0,False,False
+01019955701,3236.0,True,True
+01019955702,3549.0,True,True
+01019955800,5571.0,True,True
+01019955900,3912.0,True,True
+01019956000,4168.0,False,False
+01019956100,5467.0,False,False
+01021060101,4623.0,True,True
+01021060102,6884.0,True,True
+01021060200,2756.0,False,False
+01021060300,3622.0,True,True
+01021060401,5157.0,True,True
+01021060402,9362.0,True,True
+01021060500,3302.0,True,True
+01021060600,3545.0,True,False
+01021060700,4804.0,False,False
+01023956700,3395.0,True,True
+01023956800,4043.0,True,True
+01023956900,3365.0,True,True
+01023957000,2122.0,True,True
+01025957500,1384.0,True,True
+01025957601,1008.0,True,True
+01025957602,5064.0,True,True
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+72145560702,5223.0,True,True
+72145560801,2257.0,True,True
+72145560802,3097.0,True,True
+72145560900,4603.0,True,True
+72145990000,0.0,False,False
+72147950500,4208.0,True,True
+72147950600,4434.0,True,True
+72147990000,0.0,False,False
+72149720100,4684.0,True,True
+72149720300,5083.0,True,True
+72149720400,1679.0,True,True
+72149720502,2775.0,True,True
+72149720503,4363.0,True,True
+72149720504,3819.0,True,True
+72151950600,5660.0,True,True
+72151950700,5081.0,True,True
+72151950800,3179.0,True,True
+72151950900,5330.0,True,True
+72151951000,3119.0,True,True
+72151951100,2311.0,True,True
+72151951200,3866.0,True,True
+72151951300,4953.0,True,True
+72151990000,0.0,False,False
+72153750101,2133.0,True,True
+72153750102,3189.0,True,True
+72153750201,2729.0,True,True
+72153750202,3327.0,True,True
+72153750300,3896.0,True,True
+72153750400,2905.0,True,True
+72153750501,6113.0,True,True
+72153750502,2388.0,True,True
+72153750503,2126.0,True,True
+72153750601,4397.0,True,True
+72153750602,2225.0,True,True
+78010970100,1863.0,False,False
+78010970200,3122.0,False,True
+78010970300,4723.0,False,True
+78010970400,4709.0,False,False
+78010970500,3428.0,False,False
+78010970600,4222.0,False,False
+78010970700,2243.0,False,False
+78010970800,4105.0,False,True
+78010970900,2232.0,False,True
+78010971000,1977.0,False,False
+78010971100,4072.0,False,True
+78010971200,4425.0,False,False
+78010971300,3454.0,False,True
+78010971400,2491.0,False,False
+78010971500,3535.0,False,False
+78010990000,,False,False
+78020950100,1435.0,False,False
+78020950200,2735.0,False,False
+78020990000,,False,False
+78030960100,3711.0,False,False
+78030960200,4398.0,False,False
+78030960300,4520.0,False,False
+78030960400,4709.0,False,False
+78030960500,5431.0,False,False
+78030960600,3987.0,False,False
+78030960700,3520.0,False,False
+78030960800,4088.0,False,False
+78030960900,4878.0,False,False
+78030961000,5220.0,False,False
+78030961100,4356.0,False,False
+78030961200,2816.0,False,True
+78030990000,,False,False
diff --git a/data/data-pipeline/data_pipeline/etl/datasource.py b/data/data-pipeline/data_pipeline/etl/datasource.py
index 3d299207..f77656c1 100644
--- a/data/data-pipeline/data_pipeline/etl/datasource.py
+++ b/data/data-pipeline/data_pipeline/etl/datasource.py
@@ -22,6 +22,7 @@ from pathlib import Path
 from typing import List
 from dataclasses import dataclass
 from abc import ABC, abstractmethod
+from data_pipeline.constants import NO_SSL_VERIFY
 
 from data_pipeline.etl.downloader import Downloader
 from data_pipeline.etl.sources.census_acs.etl_utils import (
@@ -65,7 +66,7 @@ class FileDataSource(DataSource):
         Downloader.download_file_from_url(
             file_url=self.source,
             download_file_name=self.destination,
-            verify=True,
+            verify=not NO_SSL_VERIFY,
         )
 
     def __str__(self):
@@ -85,7 +86,7 @@ class ZIPDataSource(DataSource):
         Downloader.download_zip_file_from_url(
             file_url=self.source,
             unzipped_file_path=self.destination,
-            verify=True,
+            verify=not NO_SSL_VERIFY,
         )
 
     def __str__(self):
diff --git a/data/data-pipeline/data_pipeline/etl/downloader.py b/data/data-pipeline/data_pipeline/etl/downloader.py
index 53ea2a38..fd0fec50 100644
--- a/data/data-pipeline/data_pipeline/etl/downloader.py
+++ b/data/data-pipeline/data_pipeline/etl/downloader.py
@@ -6,6 +6,9 @@ import shutil
 
 from pathlib import Path
 from data_pipeline.config import settings
+from data_pipeline.utils import get_module_logger
+
+logger = get_module_logger(__name__)
 
 
 class Downloader:
@@ -34,13 +37,15 @@ class Downloader:
         urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
         download_file_name.parent.mkdir(parents=True, exist_ok=True)
-
+        logger.debug(f"Downloading {file_url}")
         response = requests.get(
             file_url, verify=verify, timeout=settings.REQUESTS_DEFAULT_TIMOUT
         )
         if response.status_code == 200:
             file_contents = response.content
+            logger.debug("Downloaded.")
         else:
+            # pylint: disable-next=broad-exception-raised
             raise Exception(
                 f"HTTP response {response.status_code} from url {file_url}. Info: {response.content}"
             )
diff --git a/data/data-pipeline/data_pipeline/etl/score/constants.py b/data/data-pipeline/data_pipeline/etl/score/constants.py
index ea63f854..964a76f3 100644
--- a/data/data-pipeline/data_pipeline/etl/score/constants.py
+++ b/data/data-pipeline/data_pipeline/etl/score/constants.py
@@ -9,6 +9,7 @@ from data_pipeline.score import field_names
 
 # Base Paths
 DATA_PATH = Path(settings.APP_ROOT) / "data"
+STATIC_DATA_PATH = Path(settings.APP_ROOT) / "content" / "static_data"
 TMP_PATH = DATA_PATH / "tmp"
 FILES_PATH = Path(settings.APP_ROOT) / "files"
 
@@ -23,6 +24,7 @@ DATA_CENSUS_DIR = DATA_PATH / "census"
 DATA_CENSUS_CSV_DIR = DATA_CENSUS_DIR / "csv"
 DATA_CENSUS_CSV_FILE_PATH = DATA_CENSUS_CSV_DIR / "us.csv"
 DATA_CENSUS_CSV_STATE_FILE_PATH = DATA_CENSUS_CSV_DIR / "fips_states_2010.csv"
+DATA_CENSUS_GEOJSON_FILE_PATH = DATA_CENSUS_DIR / "geojson" / "us.json"
 
 # Score paths
 DATA_SCORE_DIR = DATA_PATH / "score"
@@ -45,6 +47,9 @@ DATA_SCORE_JSON_INDEX_FILE_PATH = (
 ## Tile path
 DATA_SCORE_TILES_DIR = DATA_SCORE_DIR / "tiles"
 
+## Tiles search
+DATA_TILES_SEARCH_DIR = DATA_SCORE_DIR / "search"
+
 # Downloadable paths
 if not os.environ.get("J40_VERSION_LABEL_STRING"):
     version_str = "beta"
@@ -81,6 +86,7 @@ SCORE_VERSIONING_README_FILE_NAME = f"readme-version-{version_str}.md"
 SCORE_VERSIONING_README_FILE_PATH = (
     FILES_PATH / SCORE_VERSIONING_README_FILE_NAME
 )
+SCORE_TRACT_SEARCH_FILE_PATH = DATA_TILES_SEARCH_DIR / "tracts.json"
 
 # For the codebook
 CEJST_SCORE_COLUMN_NAME = "score_name"
@@ -275,6 +281,7 @@ TILES_SCORE_COLUMNS = {
     # temporarily update this so that it's the Narwhal score that gets visualized on the map
     # The NEW final score value INCLUDES the adjacency index.
     field_names.FINAL_SCORE_N_BOOLEAN: "SN_C",
+    field_names.FINAL_SCORE_N_BOOLEAN_V1_0: "SN_C_V10",
     field_names.IS_TRIBAL_DAC: "SN_T",
     field_names.DIABETES_LOW_INCOME_FIELD: "DLI",
     field_names.ASTHMA_LOW_INCOME_FIELD: "ALI",
@@ -315,18 +322,18 @@ TILES_SCORE_COLUMNS = {
     field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD: "IAPLHSE",
     field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD: "IALMILHSE",
     # Percentiles for Island areas' workforce columns
-    field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009
+    field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019
     + field_names.PERCENTILE_FIELD_SUFFIX: "IALMILHSE_PFS",
-    field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009
+    field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019
     + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
     + field_names.PERCENTILE_FIELD_SUFFIX: "IAPLHSE_PFS",
-    field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009
+    field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019
     + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
     + field_names.PERCENTILE_FIELD_SUFFIX: "IAULHSE_PFS",
     field_names.LOW_HS_EDUCATION_FIELD: "LHE",
     field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD: "IALHE",
     # Percentage of HS Degree completion for Islands
-    field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009: "IAHSEF",
+    field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019: "IAHSEF",
     # Booleans for the front end about the types of thresholds exceeded
     field_names.CLIMATE_THRESHOLD_EXCEEDED: "N_CLT_EOMI",
     field_names.ENERGY_THRESHOLD_EXCEEDED: "N_ENY_EOMI",
diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score.py b/data/data-pipeline/data_pipeline/etl/score/etl_score.py
index 0314512b..6dc13fe0 100644
--- a/data/data-pipeline/data_pipeline/etl/score/etl_score.py
+++ b/data/data-pipeline/data_pipeline/etl/score/etl_score.py
@@ -54,6 +54,7 @@ class ScoreETL(ExtractTransformLoad):
         self.eamlis_df: pd.DataFrame
         self.fuds_df: pd.DataFrame
         self.tribal_overlap_df: pd.DataFrame
+        self.v1_0_score_results_df: pd.DataFrame
 
         self.ISLAND_DEMOGRAPHIC_BACKFILL_FIELDS: List[str] = []
 
@@ -166,7 +167,7 @@ class ScoreETL(ExtractTransformLoad):
         census_decennial_csv = (
             constants.DATA_PATH
             / "dataset"
-            / "census_decennial_2010"
+            / "census_decennial_2020"
             / "usa.csv"
         )
         self.census_decennial_df = pd.read_csv(
@@ -205,6 +206,25 @@ class ScoreETL(ExtractTransformLoad):
             header=None,
         )
 
+        # Load v1.0 score results for grandfathering purposes
+        score_v1_0_csv = (
+            constants.STATIC_DATA_PATH / "v1.0-score-results-usa.csv"
+        )
+        self.v1_0_score_results_df = pd.read_csv(
+            score_v1_0_csv,
+            dtype={self.GEOID_TRACT_FIELD_NAME: "string"},
+            low_memory=False,
+        )
+        # Only keep the columns we need and rename them as they will clash
+        # with the new score DF.
+        self.v1_0_score_results_df = self.v1_0_score_results_df[
+            [field_names.GEOID_TRACT_FIELD, field_names.FINAL_SCORE_N_BOOLEAN]
+        ].rename(
+            columns={
+                field_names.FINAL_SCORE_N_BOOLEAN: field_names.FINAL_SCORE_N_BOOLEAN_V1_0,
+            }
+        )
+
     def _join_tract_dfs(self, census_tract_dfs: list) -> pd.DataFrame:
         logger.debug("Joining Census Tract dataframes")
 
@@ -364,6 +384,7 @@ class ScoreETL(ExtractTransformLoad):
             self.eamlis_df,
             self.fuds_df,
             self.tribal_overlap_df,
+            self.v1_0_score_results_df,
         ]
 
         # Sanity check each data frame before merging.
@@ -470,12 +491,14 @@ class ScoreETL(ExtractTransformLoad):
             field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD,
             field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD,
             field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD,
-            field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009,
-            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009,
-            field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009,
+            field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019,
             field_names.CENSUS_UNEMPLOYMENT_FIELD_2010,
             field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
-            field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2009,
+            field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019,
             field_names.UST_FIELD,
             field_names.DOT_TRAVEL_BURDEN_FIELD,
             field_names.FUTURE_FLOOD_RISK_FIELD,
@@ -512,6 +535,7 @@ class ScoreETL(ExtractTransformLoad):
             field_names.ELIGIBLE_FUDS_BINARY_FIELD_NAME,
             field_names.HISTORIC_REDLINING_SCORE_EXCEEDED,
             field_names.IS_TRIBAL_DAC,
+            field_names.FINAL_SCORE_N_BOOLEAN_V1_0,
         ]
 
         # For some columns, high values are "good", so we want to reverse the percentile
@@ -541,8 +565,8 @@ class ScoreETL(ExtractTransformLoad):
                 low_field_name=field_names.LOW_LIFE_EXPECTANCY_FIELD,
             ),
             ReversePercentile(
-                field_name=field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009,
-                low_field_name=field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009,
+                field_name=field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019,
+                low_field_name=field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019,
             ),
         ]
 
@@ -657,7 +681,7 @@ class ScoreETL(ExtractTransformLoad):
         df_copy[field_names.COMBINED_CENSUS_TOTAL_POPULATION_2010] = df_copy[
             [
                 field_names.TOTAL_POP_FIELD,
-                field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2009,
+                field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019,
             ]
         ].mean(axis=1, skipna=True)
 
diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_score_post.py b/data/data-pipeline/data_pipeline/etl/score/etl_score_post.py
index 85ce1ba5..8a0b959f 100644
--- a/data/data-pipeline/data_pipeline/etl/score/etl_score_post.py
+++ b/data/data-pipeline/data_pipeline/etl/score/etl_score_post.py
@@ -4,6 +4,7 @@ from pathlib import Path
 import numpy as np
 from numpy import float64
 import pandas as pd
+import geopandas as gpd
 
 from data_pipeline.content.schemas.download_schemas import CodebookConfig
 from data_pipeline.content.schemas.download_schemas import CSVConfig
@@ -42,10 +43,12 @@ class PostScoreETL(ExtractTransformLoad):
         self.input_counties_df: pd.DataFrame
         self.input_states_df: pd.DataFrame
         self.input_score_df: pd.DataFrame
+        self.input_census_geo_df: gpd.GeoDataFrame
 
         self.output_score_county_state_merged_df: pd.DataFrame
         self.output_score_tiles_df: pd.DataFrame
         self.output_downloadable_df: pd.DataFrame
+        self.output_tract_search_df: pd.DataFrame
 
         # Define some constants for the YAML file
         # TODO: Implement this as a marshmallow schema.
@@ -105,6 +108,18 @@ class PostScoreETL(ExtractTransformLoad):
 
         return df
 
+    def _extract_census_geojson(self, geo_path: Path) -> gpd.GeoDataFrame:
+        """
+        Read in the Census Geo JSON data.
+
+        Returns:
+           gpd.GeoDataFrame: the census geo json data
+        """
+        logger.debug("Reading Census GeoJSON")
+        with open(geo_path, "r", encoding="utf-8") as file:
+            data = gpd.read_file(file)
+        return data
+
     def extract(self, use_cached_data_sources: bool = False) -> None:
 
         super().extract(
@@ -131,6 +146,9 @@ class PostScoreETL(ExtractTransformLoad):
         self.input_score_df = self._extract_score(
             constants.DATA_SCORE_CSV_FULL_FILE_PATH
         )
+        self.input_census_geo_df = self._extract_census_geojson(
+            constants.DATA_CENSUS_GEOJSON_FILE_PATH
+        )
 
     def _transform_counties(
         self, initial_counties_df: pd.DataFrame
@@ -392,7 +410,23 @@ class PostScoreETL(ExtractTransformLoad):
 
         return final_df
 
+    def _create_tract_search_data(
+        self, census_geojson: gpd.GeoDataFrame
+    ) -> pd.DataFrame:
+        """
+        Generate a dataframe with only the tract IDs and the center lat/lon of each tract.
+
+        Returns:
+            pd.DataFrame: a dataframe with the tract search data
+        """
+        logger.debug("Creating Census tract search data")
+        columns_to_extract = ["GEOID10", "INTPTLAT10", "INTPTLON10"]
+        return pd.DataFrame(census_geojson[columns_to_extract])
+
     def transform(self) -> None:
+        self.output_tract_search_df = self._create_tract_search_data(
+            self.input_census_geo_df
+        )
         transformed_counties = self._transform_counties(self.input_counties_df)
         transformed_states = self._transform_states(self.input_states_df)
         transformed_score = self._transform_score(self.input_score_df)
@@ -409,6 +443,9 @@ class PostScoreETL(ExtractTransformLoad):
         self.output_score_county_state_merged_df = (
             output_score_county_state_merged_df
         )
+        self.output_tract_search_df = self._create_tract_search_data(
+            self.input_census_geo_df
+        )
 
     def _load_score_csv_full(
         self, score_county_state_merged: pd.DataFrame, score_csv_path: Path
@@ -592,6 +629,13 @@ class PostScoreETL(ExtractTransformLoad):
         ]
         zip_files(version_data_documentation_zip_path, files_to_compress)
 
+    def _load_search_tract_data(self, output_path: Path):
+        """Write the Census tract search data."""
+        logger.debug("Writing Census tract search data")
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        # We use the records orientation to easily import the JSON in JS.
+        self.output_tract_search_df.to_json(output_path, orient="records")
+
     def load(self) -> None:
         self._load_score_csv_full(
             self.output_score_county_state_merged_df,
@@ -600,4 +644,5 @@ class PostScoreETL(ExtractTransformLoad):
         self._load_tile_csv(
             self.output_score_tiles_df, constants.DATA_SCORE_CSV_TILES_FILE_PATH
         )
+        self._load_search_tract_data(constants.SCORE_TRACT_SEARCH_FILE_PATH)
         self._load_downloadable_zip(constants.SCORE_DOWNLOADABLE_DIR)
diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/conftest.py b/data/data-pipeline/data_pipeline/etl/score/tests/conftest.py
index 8353869d..81a029a2 100644
--- a/data/data-pipeline/data_pipeline/etl/score/tests/conftest.py
+++ b/data/data-pipeline/data_pipeline/etl/score/tests/conftest.py
@@ -3,6 +3,7 @@ from importlib import reload
 from pathlib import Path
 
 import pandas as pd
+import geopandas as gpd
 import pytest
 from data_pipeline import config
 from data_pipeline.etl.score import etl_score_post
@@ -129,8 +130,28 @@ def tile_data_expected():
     return pd.read_pickle(pytest.SNAPSHOT_DIR / "tile_data_expected.pkl")
 
 
+@pytest.fixture()
+def create_tile_score_data_input():
+    return pd.read_pickle(pytest.SNAPSHOT_DIR / "create_tile_score_data_input.pkl")
+
+
+@pytest.fixture()
+def create_tile_data_expected():
+    return pd.read_pickle(pytest.SNAPSHOT_DIR / "create_tile_data_expected.pkl")
+
+
 @pytest.fixture()
 def downloadable_data_expected():
     return pd.read_pickle(
         pytest.SNAPSHOT_DIR / "downloadable_data_expected.pkl"
     )
+
+
+@pytest.fixture()
+def census_geojson_sample_data(sample_data_dir) -> gpd.GeoDataFrame:
+    with open(
+        sample_data_dir / "census_60.geojson", "r", encoding="utf-8"
+    ) as file:
+        data = gpd.read_file(file)
+        return data
+    return None
diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/census_60.geojson b/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/census_60.geojson
new file mode 100644
index 00000000..f4715274
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/etl/score/tests/sample_data/census_60.geojson
@@ -0,0 +1,25 @@
+{
+"type": "FeatureCollection",
+"name": "tl_2010_60_tract10",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::4269" } },
+"features": [
+{ "type": "Feature", "properties": { "STATEFP10": "60", "COUNTYFP10": "010", "TRACTCE10": "950900", "GEOID10": "60010950900", "NAME10": "9509", "NAMELSAD10": "Census Tract 9509", "MTFCC10": "G5020", "FUNCSTAT10": "S", "ALAND10": 14401534, "AWATER10": 55111663, "INTPTLAT10": "-14.3040180", "INTPTLON10": "-170.7133703" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -170.747496, -14.236581 ], [ -170.742524, -14.2441 ], [ -170.739297, -14.248981 ], [ -170.734202, -14.256985 ], [ -170.728663, -14.266079 ], [ -170.728046, -14.266935 ], [ -170.722556, -14.272711 ], [ -170.721353, -14.272967 ], [ -170.716235, -14.277501 ], [ -170.715913, -14.277787 ], [ -170.715211, -14.27841 ], [ -170.715064, -14.278668 ], [ -170.712332, -14.283746 ], [ -170.7116, -14.284991 ], [ -170.712013, -14.28568 ], [ -170.712678, -14.286719 ], [ -170.713368, -14.287835 ], [ -170.715918, -14.291903 ], [ -170.716644, -14.293061 ], [ -170.719303, -14.297379 ], [ -170.714939, -14.296722 ], [ -170.712559, 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+]
+}
diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/README.md b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/README.md
new file mode 100644
index 00000000..01ae8488
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/README.md
@@ -0,0 +1,23 @@
+These files are used as inputs to unit tests. Some notes in their creation is below.
+
+### create_tile_data_expected.pkl
+1. Set a breakpoint in the `test_create_tile_data` method in `data_pipeline/etl/score/tests/test_score_post.py` 
+after the call to `_create_tile_data` and debug the test.
+2. Extract a subset of the `output_tiles_df_actual` dataframe. Do not extract the whole score as the file 
+will be too big and the test will run slow. Also, you need to extract the same tracts that are in
+the `create_tile_score_data_input.pkl` input data. For example, use the following command once the breakpoint is reached 
+to extract a few rows at the top and bottom of the score. This will some capture states and territories.
+```python
+import pandas as pd
+pd.concat([output_tiles_df_actual.head(3), output_tiles_df_actual.tail(3)], ignore_index=True).to_pickle('data_pipeline/etl/score/tests/snapshots/create_tile_data_expected.pkl')
+```
+
+### create_tile_score_data_input.pkl
+1. Set a breakpoint in the transform method in `data_pipeline/etl/score/etl_score_post.py` before the call to
+`_create_tile_data` and run the post scoring.
+2. Extract a subset of the `output_score_county_state_merged_df` dataframe. Do not extract the whole score as the file 
+will be too big and the test will run slow. For example, use the following command once the breakpoint is reached 
+to extract a few rows at the top and bottom of the score. This will some capture states and territories.
+```python
+pd.concat([output_score_county_state_merged_df.head(3), output_score_county_state_merged_df.tail(3)], ignore_index=True).to_pickle('data_pipeline/etl/score/tests/snapshots/create_tile_score_data_input.pkl')
+```
\ No newline at end of file
diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_data_expected.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_data_expected.pkl
new file mode 100644
index 00000000..3257e33c
Binary files /dev/null and b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_data_expected.pkl differ
diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_score_data_input.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_score_data_input.pkl
new file mode 100644
index 00000000..8d21a5b9
Binary files /dev/null and b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/create_tile_score_data_input.pkl differ
diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/test_score_post.py b/data/data-pipeline/data_pipeline/etl/score/tests/test_score_post.py
index 9fc51a66..d3c762c6 100644
--- a/data/data-pipeline/data_pipeline/etl/score/tests/test_score_post.py
+++ b/data/data-pipeline/data_pipeline/etl/score/tests/test_score_post.py
@@ -5,9 +5,12 @@ from pathlib import Path
 
 import pandas.api.types as ptypes
 import pandas.testing as pdt
+import pandas as pd
+import geopandas as gpd
 from data_pipeline.content.schemas.download_schemas import CSVConfig
 from data_pipeline.etl.score import constants
 from data_pipeline.utils import load_yaml_dict_from_file
+from data_pipeline.etl.score.etl_score_post import PostScoreETL
 
 # See conftest.py for all fixtures used in these tests
 
@@ -80,11 +83,11 @@ def test_create_score_data(
     )
 
 
-def test_create_tile_data(etl, score_data_expected, tile_data_expected):
-    output_tiles_df_actual = etl._create_tile_data(score_data_expected)
+def test_create_tile_data(etl, create_tile_score_data_input, create_tile_data_expected):
+    output_tiles_df_actual = etl._create_tile_data(create_tile_score_data_input)
     pdt.assert_frame_equal(
         output_tiles_df_actual,
-        tile_data_expected,
+        create_tile_data_expected,
     )
 
 
@@ -150,3 +153,16 @@ def test_load_downloadable_zip(etl, monkeypatch, score_data_expected):
     assert constants.SCORE_DOWNLOADABLE_EXCEL_FILE_PATH.is_file()
     assert constants.SCORE_DOWNLOADABLE_CSV_ZIP_FILE_PATH.is_file()
     assert constants.SCORE_DOWNLOADABLE_XLS_ZIP_FILE_PATH.is_file()
+
+
+def test_create_tract_search_data(census_geojson_sample_data: gpd.GeoDataFrame):
+    # Sanity check
+    assert len(census_geojson_sample_data) > 0
+    
+    result = PostScoreETL()._create_tract_search_data(census_geojson_sample_data)
+    assert isinstance(result, pd.DataFrame)
+    assert not result.columns.empty
+    columns = ["GEOID10", "INTPTLAT10", "INTPTLON10"]
+    for col in columns:
+        assert col in result.columns
+    assert len(census_geojson_sample_data) == len(result)
diff --git a/data/data-pipeline/data_pipeline/etl/sources/census/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census/etl.py
index 1f4b260a..e0ad704a 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/census/etl.py
+++ b/data/data-pipeline/data_pipeline/etl/sources/census/etl.py
@@ -215,6 +215,7 @@ class CensusETL(ExtractTransformLoad):
             state_gdf = gpd.read_file(file_name)
             usa_df = usa_df.append(state_gdf)
 
+        logger.debug("Converting to CRS")
         usa_df = usa_df.to_crs(
             "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
         )
diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py
index d67a2bc3..5a894a3c 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py
+++ b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl.py
@@ -25,6 +25,9 @@ class CensusACSETL(ExtractTransformLoad):
     NAME = "census_acs"
     ACS_YEAR = 2019
     MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION = 1
+    ImputeVariables = namedtuple(
+        "ImputeVariables", ["raw_field_name", "imputed_field_name"]
+    )
 
     def __init__(self):
 
@@ -284,7 +287,7 @@ class CensusACSETL(ExtractTransformLoad):
 
         self.COLUMNS_TO_KEEP = (
             [
-                self.GEOID_TRACT_FIELD_NAME,
+                field_names.GEOID_TRACT_FIELD,
                 field_names.TOTAL_POP_FIELD,
                 self.UNEMPLOYED_FIELD_NAME,
                 self.LINGUISTIC_ISOLATION_FIELD_NAME,
@@ -335,15 +338,15 @@ class CensusACSETL(ExtractTransformLoad):
                 destination=self.census_acs_source,
                 acs_year=self.ACS_YEAR,
                 variables=variables,
-                tract_output_field_name=self.GEOID_TRACT_FIELD_NAME,
+                tract_output_field_name=field_names.GEOID_TRACT_FIELD,
                 data_path_for_fips_codes=self.DATA_PATH,
                 acs_type="acs5",
             )
         ]
 
     # pylint: disable=too-many-arguments
-    def _merge_geojson(
-        self,
+    @staticmethod
+    def merge_geojson(
         df: pd.DataFrame,
         usa_geo_df: gpd.GeoDataFrame,
         geoid_field: str = "GEOID10",
@@ -364,7 +367,7 @@ class CensusACSETL(ExtractTransformLoad):
                         county_code_field,
                     ]
                 ],
-                left_on=[self.GEOID_TRACT_FIELD_NAME],
+                left_on=[field_names.GEOID_TRACT_FIELD],
                 right_on=[geoid_field],
             )
         )
@@ -377,7 +380,7 @@ class CensusACSETL(ExtractTransformLoad):
 
         self.df = pd.read_csv(
             self.census_acs_source,
-            dtype={self.GEOID_TRACT_FIELD_NAME: "string"},
+            dtype={field_names.GEOID_TRACT_FIELD: "string"},
         )
 
     def transform(self) -> None:
@@ -401,7 +404,7 @@ class CensusACSETL(ExtractTransformLoad):
             self.DATA_PATH / "census" / "geojson" / "us.json",
         )
 
-        df = self._merge_geojson(
+        df = CensusACSETL.merge_geojson(
             df=df,
             usa_geo_df=geo_df,
         )
@@ -608,23 +611,19 @@ class CensusACSETL(ExtractTransformLoad):
         # we impute income for both income measures
         ## TODO: Convert to pydantic for clarity
         logger.debug("Imputing income information")
-        ImputeVariables = namedtuple(
-            "ImputeVariables", ["raw_field_name", "imputed_field_name"]
-        )
-
         df = calculate_income_measures(
             impute_var_named_tup_list=[
-                ImputeVariables(
+                CensusACSETL.ImputeVariables(
                     raw_field_name=self.POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
                     imputed_field_name=self.IMPUTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME,
                 ),
-                ImputeVariables(
+                CensusACSETL.ImputeVariables(
                     raw_field_name=self.COLLEGE_ATTENDANCE_FIELD,
                     imputed_field_name=self.IMPUTED_COLLEGE_ATTENDANCE_FIELD,
                 ),
             ],
             geo_df=df,
-            geoid_field=self.GEOID_TRACT_FIELD_NAME,
+            geoid_field=field_names.GEOID_TRACT_FIELD,
             minimum_population_required_for_imputation=self.MINIMUM_POPULATION_REQUIRED_FOR_IMPUTATION,
         )
 
diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py
index ad0514ff..4fcf0d9f 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py
+++ b/data/data-pipeline/data_pipeline/etl/sources/census_acs/etl_imputations.py
@@ -39,6 +39,7 @@ def _choose_best_mask(
     for mask in masks_in_priority_order:
         if any(geo_df[mask][column_to_impute].notna()):
             return mask
+    # pylint: disable-next=broad-exception-raised
     raise Exception("No mask found")
 
 
diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/README.md b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/README.md
index e043f232..3f9b3c16 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/README.md
+++ b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/README.md
@@ -1,10 +1,10 @@
 # Decennial Census of Island Areas
 
-Adding a new ETL folder for Decennial Census of Island Areas since the Island areas aren't included in ACS.
+Decennial Census of Island Areas since the Island areas aren't included in ACS.
 
 There's the American Samoa Summary File, the Guam Summary File, the Commonwealth of the Northern Mariana Islands Summary File, and the US Virgin Islands Summary File:
 
-1. https://api.census.gov/data/2010/dec/as.html
-1. https://api.census.gov/data/2010/dec/gu.html
-1. https://api.census.gov/data/2010/dec/mp.html
-1. https://api.census.gov/data/2010/dec/vi.html
+1. https://api.census.gov/data/2020/dec/dhcas.html
+1. https://api.census.gov/data/2020/dec/dhcgu.html
+1. https://api.census.gov/data/2020/dec/dhcmp.html
+1. https://api.census.gov/data/2020/dec/dhcvi.html
diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py
new file mode 100644
index 00000000..9cf52bc4
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/constants.py
@@ -0,0 +1,211 @@
+from enum import Enum
+from types import MappingProxyType
+from data_pipeline.score import field_names
+
+
+class DEC_FIELD_NAMES(str, Enum):
+    """Field/column names for the decennial data"""
+
+    MALE_HIGH_SCHOOL_ED = "Total male high school graduates 25 and over"
+    FEMALE_HIGH_SCHOOL_ED = "Total female high school graduates 25 and over"
+    IMPUTED_COLLEGE_ATTENDANCE = "Percent enrollment in college, graduate or professional school, imputed"
+    TOTAL_RACE_POPULATION = "Total population surveyed on racial data"
+    BLACK = "Black or African American"
+    AMERICAN_INDIAN = "American Indian / Alaska Native"
+    ASIAN = "Asian"
+    HAWAIIAN = "Native Hawaiian or Pacific"
+    TWO_OR_MORE_RACES = "two or more races"
+    NON_HISPANIC_WHITE = "White"
+    HISPANIC = "Hispanic or Latino"
+    OTHER_RACE = "other races"
+    HOUSEHOLD_POVERTY_LEVEL_UNDER_0_5 = (
+        "Household poverty level Under 0.50 IN 2019"
+    )
+    HOUSEHOLD_POVERTY_LEVEL_UNDER_0_74 = (
+        "Household poverty level Under 0.74 IN 2019"
+    )
+    HOUSEHOLD_POVERTY_LEVEL_UNDER_0_99 = (
+        "Household poverty level Under 0.99 IN 2019"
+    )
+    HOUSEHOLD_POVERTY_LEVEL_OVER_2_0 = (
+        "Household poverty level Over 2.0 IN 2019"
+    )
+    IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL = f"{field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019}, imputed"
+    TOTAL_HOUSEHOLD_POVERTY_LEVEL = "Total Household poverty level IN 2019"
+    TERRITORY_MEDIAN_INCOME = "Territory Median Income"
+    EMPLOYMENT_MALE_UNEMPLOYED = "Total males not in labor force"
+    EMPLOYMENT_FEMALE_UNEMPLOYED = "Total females not in labor force"
+    EMPLOYMENT_MALE_IN_LABOR_FORCE = "Total males in labor force"
+    EMPLOYMENT_FEMALE_IN_LABOR_FORCE = "Total females in labor force"
+    COLLEGE_ATTENDANCE_TOTAL_ENROLLED = "Total asked enrolled in college or graduate school (excludes military housing)"
+    COLLEGE_NON_ATTENDANCE = "Percent of population not currently enrolled in college, graduate or professional school"
+    COLLEGE_ATTENDANCE_MALE_ENROLLED = "Males enrolled in college or graduate school (excludes military housing)"
+    COLLEGE_ATTENDANCE_FEMALE_ENROLLED = "Females enrolled in college or graduate school (excludes military housing)"
+    COLLEGE_ATTENDANCE_POPULATION = (
+        "Population enrolled in college, graduate or professional school"
+    )
+    COLLEGE_ATTENDANCE_PERCENT = (
+        "Percent enrollment in college, graduate or professional school"
+    )
+    IMPUTED_COLLEGE_ATTENDANCE_PERCENT = (
+        f"{COLLEGE_ATTENDANCE_PERCENT}, imputed"
+    )
+    COLLEGE_NON_ATTENDANCE_PERCENT = "Percent of population not currently enrolled in college, graduate or professional school"
+
+    def __str__(self) -> str:
+        """This method removes the need to use the value attribute from the Enums"""
+        return str.__str__(self)
+
+
+__FIELD_NAME_COMMON_XWALK = {
+    "P1_001N": field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019,
+    "PBG19_005N": DEC_FIELD_NAMES.MALE_HIGH_SCHOOL_ED,
+    "PBG19_012N": DEC_FIELD_NAMES.FEMALE_HIGH_SCHOOL_ED,
+    "PCT31_001N": DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_TOTAL_ENROLLED,
+    "PBG32_003N": DEC_FIELD_NAMES.EMPLOYMENT_MALE_IN_LABOR_FORCE,
+    "PBG32_007N": DEC_FIELD_NAMES.EMPLOYMENT_MALE_UNEMPLOYED,
+    "PBG32_010N": DEC_FIELD_NAMES.EMPLOYMENT_FEMALE_IN_LABOR_FORCE,
+    "PBG32_014N": DEC_FIELD_NAMES.EMPLOYMENT_FEMALE_UNEMPLOYED,
+    "PCT34_003N": DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_MALE_ENROLLED,
+    "PCT34_016N": DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_FEMALE_ENROLLED,
+    "PBG43_001N": field_names.CENSUS_DECENNIAL_MEDIAN_INCOME_2019,
+    "PBG74_001N": DEC_FIELD_NAMES.TOTAL_HOUSEHOLD_POVERTY_LEVEL,
+    "PBG74_002N": DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_5,
+    "PBG74_003N": DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_74,
+    "PBG74_004N": DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_99,
+    "PBG74_010N": DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_OVER_2_0,
+}
+"""
+Census variable to text column name mapping. For details on Census variables see:
+https://api.census.gov/data/2020/dec/dhcas/variables.html
+https://api.census.gov/data/2020/dec/dhcgu/variables.html
+https://api.census.gov/data/2020/dec/dhcmp/variables.html
+https://api.census.gov/data/2020/dec/dhcvi/variables.html
+"""
+
+# Note that the 2010 census for island areas does not break out
+# hispanic and non-hispanic white, so this is slightly different from
+# our other demographic data
+__FIELD_NAME_AS_XWALK = {
+    "PCT9_001N": DEC_FIELD_NAMES.TOTAL_RACE_POPULATION,
+    "PCT9_003N": DEC_FIELD_NAMES.HAWAIIAN,
+    "PCT9_079N": DEC_FIELD_NAMES.ASIAN,
+    "PCT9_130N": DEC_FIELD_NAMES.NON_HISPANIC_WHITE,
+    "PCT9_155N": DEC_FIELD_NAMES.BLACK,
+    "PCT9_180N": DEC_FIELD_NAMES.AMERICAN_INDIAN,
+    "PCT9_205N": DEC_FIELD_NAMES.OTHER_RACE,
+    "PCT9_230N": DEC_FIELD_NAMES.TWO_OR_MORE_RACES,
+    "P5_002N": DEC_FIELD_NAMES.HISPANIC,
+}
+"""American Samoa specific race fields."""
+
+__FIELD_NAME_VI_XWALK = {
+    "PCT7_001N": DEC_FIELD_NAMES.TOTAL_RACE_POPULATION,
+    "PCT7_003N": DEC_FIELD_NAMES.BLACK,
+    "PCT7_205N": DEC_FIELD_NAMES.ASIAN,
+    "PCT7_230N": DEC_FIELD_NAMES.AMERICAN_INDIAN,
+    "PCT7_255N": DEC_FIELD_NAMES.HAWAIIAN,
+    "PCT7_280N": DEC_FIELD_NAMES.OTHER_RACE,
+    "PCT7_305N": DEC_FIELD_NAMES.TWO_OR_MORE_RACES,
+    "P5_021N": DEC_FIELD_NAMES.NON_HISPANIC_WHITE,
+    "PCT6_003N": DEC_FIELD_NAMES.HISPANIC,
+}
+"""US Virgin Islands specific race fields."""
+
+__FIELD_NAME_GU_XWALK = {
+    "PCT10_001N": DEC_FIELD_NAMES.TOTAL_RACE_POPULATION,
+    "PCT10_003N": DEC_FIELD_NAMES.HAWAIIAN,
+    "PCT10_204N": DEC_FIELD_NAMES.ASIAN,
+    "PCT10_330N": DEC_FIELD_NAMES.BLACK,
+    "PCT10_355N": DEC_FIELD_NAMES.AMERICAN_INDIAN,
+    "PCT10_380N": DEC_FIELD_NAMES.OTHER_RACE,
+    "PCT10_405N": DEC_FIELD_NAMES.TWO_OR_MORE_RACES,
+    "P5_026N": DEC_FIELD_NAMES.NON_HISPANIC_WHITE,
+    "PCT9_003N": DEC_FIELD_NAMES.HISPANIC,
+}
+"""Guam specific race fields."""
+
+__FIELD_NAME_MP_XWALK = {
+    "PCT9_001N": DEC_FIELD_NAMES.TOTAL_RACE_POPULATION,
+    "PCT9_003N": DEC_FIELD_NAMES.ASIAN,
+    "PCT9_129N": DEC_FIELD_NAMES.HAWAIIAN,
+    "PCT9_330N": DEC_FIELD_NAMES.BLACK,
+    "PCT9_355N": DEC_FIELD_NAMES.AMERICAN_INDIAN,
+    "PCT9_380N": DEC_FIELD_NAMES.OTHER_RACE,
+    "PCT9_405N": DEC_FIELD_NAMES.TWO_OR_MORE_RACES,
+    "P5_002N": DEC_FIELD_NAMES.HISPANIC,
+    "P5_024N": DEC_FIELD_NAMES.NON_HISPANIC_WHITE,
+}
+"""Northern Mariana Islands specific race fields."""
+
+OUTPUT_RACE_FIELDS = [
+    DEC_FIELD_NAMES.BLACK,
+    DEC_FIELD_NAMES.AMERICAN_INDIAN,
+    DEC_FIELD_NAMES.ASIAN,
+    DEC_FIELD_NAMES.HAWAIIAN,
+    DEC_FIELD_NAMES.TWO_OR_MORE_RACES,
+    DEC_FIELD_NAMES.NON_HISPANIC_WHITE,
+    DEC_FIELD_NAMES.HISPANIC,
+    DEC_FIELD_NAMES.OTHER_RACE,
+]
+"""Race fields to output in the results."""
+
+DEC_TERRITORY_PARAMS = [
+    MappingProxyType(
+        {
+            "state_abbreviation": "as",
+            "fips": "60",
+            # https://www2.census.gov/geo/docs/reference/codes2020/cou/st60_as_cou2020.txt
+            "county_fips": ("010", "020", "030", "040", "050"),
+            "xwalk": MappingProxyType(
+                __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_AS_XWALK
+            ),
+            # Note: we hardcode the median income for each territory in this dict,
+            # because that data is hard to programmatically access.
+            # https://www.ruralhealthinfo.org/states/american-samoa
+            "median_income": 26352,
+        }
+    ),
+    MappingProxyType(
+        {
+            "state_abbreviation": "gu",
+            "fips": "66",
+            # https://www2.census.gov/geo/docs/reference/codes2020/cou/st66_gu_cou2020.txt
+            "county_fips": ("010",),
+            "xwalk": MappingProxyType(
+                __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_GU_XWALK
+            ),
+            # https://www.ruralhealthinfo.org/states/guam
+            # https://data.census.gov/table/DECENNIALDPGU2020.DP3?g=040XX00US66&d=DECIA%20Guam%20Demographic%20Profile
+            "median_income": 58289,
+        }
+    ),
+    MappingProxyType(
+        {
+            "state_abbreviation": "mp",
+            "fips": "69",
+            # https://www2.census.gov/geo/docs/reference/codes2020/cou/st69_mp_cou2020.txt
+            "county_fips": ("085", "100", "110", "120"),
+            "xwalk": MappingProxyType(
+                __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_MP_XWALK
+            ),
+            # https://www.ruralhealthinfo.org/states/northern-mariana
+            # https://data.census.gov/table/DECENNIALDPMP2020.DP3?d=DECIA%20Commonwealth%20of%20the%20Northern%20Mariana%20Islands%20Demographic%20Profile
+            "median_income": 31362,
+        }
+    ),
+    MappingProxyType(
+        {
+            "state_abbreviation": "vi",
+            "fips": "78",
+            # https://www2.census.gov/geo/docs/reference/codes2020/cou/st78_vi_cou2020.txt
+            "county_fips": ("010", "020", "030"),
+            "xwalk": MappingProxyType(
+                __FIELD_NAME_COMMON_XWALK | __FIELD_NAME_VI_XWALK
+            ),
+            # https://www.ruralhealthinfo.org/states/us-virgin-islands
+            "median_income": 40408,
+        }
+    ),
+]
+"""Read-only list of territories to process."""
diff --git a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py
index 4fe26249..8d32ec98 100644
--- a/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py
+++ b/data/data-pipeline/data_pipeline/etl/sources/census_decennial/etl.py
@@ -1,14 +1,24 @@
-import json
-from typing import List
 import os
-
 import numpy as np
 import pandas as pd
+import geopandas as gpd
+import json
+from typing import List
+from pathlib import Path
+from data_pipeline.etl.sources.census_decennial.constants import (
+    DEC_TERRITORY_PARAMS,
+    DEC_FIELD_NAMES,
+    OUTPUT_RACE_FIELDS,
+)
 from data_pipeline.etl.base import ExtractTransformLoad
-from data_pipeline.score import field_names
-from data_pipeline.utils import get_module_logger
 from data_pipeline.etl.datasource import DataSource
 from data_pipeline.etl.datasource import FileDataSource
+from data_pipeline.score import field_names
+from data_pipeline.utils import get_module_logger
+from data_pipeline.etl.sources.census_acs.etl import CensusACSETL
+from data_pipeline.etl.sources.census_acs.etl_imputations import (
+    calculate_income_measures,
+)
 
 pd.options.mode.chained_assignment = "raise"
 
@@ -16,514 +26,278 @@ logger = get_module_logger(__name__)
 
 
 class CensusDecennialETL(ExtractTransformLoad):
-    def __init__(self):
-        self.DECENNIAL_YEAR = 2010
-        self.OUTPUT_PATH = (
-            self.DATA_PATH
-            / "dataset"
-            / f"census_decennial_{self.DECENNIAL_YEAR}"
-        )
+    DECENNIAL_YEAR = 2020
+    OUTPUT_PATH = (
+        ExtractTransformLoad.DATA_PATH
+        / "dataset"
+        / f"census_decennial_{DECENNIAL_YEAR}"
+    )
+    CENSUS_GEOJSON_PATH = (
+        ExtractTransformLoad.DATA_PATH / "census" / "geojson" / "us.json"
+    )
 
-        # Income Fields
-        # AS, GU, and MP all share the same variable names, but VI is different
-        # https://api.census.gov/data/2010/dec/as.html
-        # https://api.census.gov/data/2010/dec/gu/variables.html
-        # https://api.census.gov/data/2010/dec/mp/variables.html
-        # https://api.census.gov/data/2010/dec/vi/variables.html
-
-        # Total population field is the same in all island areas
-        self.TOTAL_POP_FIELD = self.TOTAL_POP_VI_FIELD = "P001001"
-        self.TOTAL_POP_FIELD_NAME = "Total population in 2009"
-
-        self.MEDIAN_INCOME_FIELD = "PBG049001"
-        self.MEDIAN_INCOME_VI_FIELD = "PBG047001"
-        self.MEDIAN_INCOME_FIELD_NAME = "Median household income in 2009 ($)"
-        self.AREA_MEDIAN_INCOME_FIELD_NAME = (
-            "Median household income as a percent of "
-            "territory median income in 2009"
+    def __get_api_url(
+        self,
+        state_abbreviation: str,
+        name_list: List[str],
+        fips: str,
+        county: str,
+    ) -> str:
+        url = (
+            f"https://api.census.gov/data/{self.DECENNIAL_YEAR}/dec/dhc{state_abbreviation}?get=NAME,{name_list}"
+            + f"&for=tract:*&in=state:{fips}%20county:{county}"
         )
-
-        self.TERRITORY_MEDIAN_INCOME_FIELD = "Territory Median Income"
-
-        self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD = "PBG083001"
-        self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_VI_FIELD = (
-            "PBG077001"
-        )
-        self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME = (
-            "TOTAL; RATIO OF INCOME TO POVERTY LEVEL IN 2009"
-        )
-
-        self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD = "PBG083010"
-        self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD = "PBG077010"
-        self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME = (
-            "Total!!2.00 and over; RATIO OF INCOME TO POVERTY LEVEL IN 2009"
-        )
-
-        self.PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL_FIELD_NAME = (
-            "Percentage households below 200% of federal poverty line in 2009"
-        )
-
-        # We will combine three fields to get households < 100% FPL.
-        self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE = (
-            "PBG083002"  # Total!!Under .50
-        )
-        self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO = (
-            "PBG083003"  # Total!!.50 to .74
-        )
-        self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE = (
-            "PBG083004"  # Total!!.75 to .99
-        )
-
-        # Same fields, for Virgin Islands.
-        self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_ONE = (
-            "PBG077002"  # Total!!Under .50
-        )
-        self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_TWO = (
-            "PBG077003"  # Total!!.50 to .74
-        )
-        self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_THREE = (
-            "PBG077004"  # Total!!.75 to .99
-        )
-
-        self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD = "PBG083010"
-        self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD = "PBG077010"
-        self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME = (
-            "Total!!2.00 and over; RATIO OF INCOME TO POVERTY LEVEL IN 2009"
-        )
-
-        self.PERCENTAGE_HOUSEHOLDS_BELOW_100_PERC_POVERTY_LEVEL_FIELD_NAME = (
-            "Percentage households below 100% of federal poverty line in 2009"
-        )
-
-        # High School Education Fields
-        self.TOTAL_POPULATION_FIELD = "PBG026001"
-        self.TOTAL_POPULATION_VI_FIELD = "PCT032001"
-        self.TOTAL_POPULATION_FIELD_NAME = "Total; SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER"
-
-        self.MALE_HIGH_SCHOOL_ED_FIELD = "PBG026005"
-        self.MALE_HIGH_SCHOOL_ED_VI_FIELD = "PCT032011"
-        self.MALE_HIGH_SCHOOL_ED_FIELD_NAME = (
-            "Total!!Male!!High school graduate, GED, or alternative; "
-            "SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER"
-        )
-
-        self.FEMALE_HIGH_SCHOOL_ED_FIELD = "PBG026012"
-        self.FEMALE_HIGH_SCHOOL_ED_VI_FIELD = "PCT032028"
-        self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME = (
-            "Total!!Female!!High school graduate, GED, or alternative; "
-            "SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER"
-        )
-
-        self.PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME = "Percent individuals age 25 or over with less than high school degree in 2009"
-
-        # Employment fields
-        self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD = (
-            "PBG038003"  # Total!!Male!!In labor force
-        )
-        self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD = (
-            "PBG038007"  # Total!!Male!!In labor force!!Civilian!!Unemployed
-        )
-        self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD = (
-            "PBG038010"  # Total!!Female!!In labor force
-        )
-        self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD = (
-            "PBG038014"  # Total!!Female!!In labor force!!Civilian!!Unemployed
-        )
-
-        # Same fields, Virgin Islands.
-        self.EMPLOYMENT_MALE_IN_LABOR_FORCE_VI_FIELD = (
-            "PBG036003"  # Total!!Male!!In labor force
-        )
-        self.EMPLOYMENT_MALE_UNEMPLOYED_VI_FIELD = (
-            "PBG036007"  # Total!!Male!!In labor force!!Civilian!!Unemployed
-        )
-        self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_VI_FIELD = (
-            "PBG036010"  # Total!!Female!!In labor force
-        )
-        self.EMPLOYMENT_FEMALE_UNEMPLOYED_VI_FIELD = (
-            "PBG036014"  # Total!!Female!!In labor force!!Civilian!!Unemployed
-        )
-
-        self.UNEMPLOYMENT_FIELD_NAME = (
-            field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009
-        )
-
-        # Race/Ethnicity fields
-        self.TOTAL_RACE_POPULATION_FIELD = "PCT086001"  # Total
-        self.ASIAN_FIELD = "PCT086002"  # Total!!Asian
-        self.BLACK_FIELD = "PCT086003"  # Total!!Black or African American
-        self.HAWAIIAN_FIELD = (
-            "PCT086004"  # Total!!Native Hawaiian and Other Pacific Islander
-        )
-        # Note that the 2010 census for island araeas does not break out
-        # hispanic and non-hispanic white, so this is slightly different from
-        # our other demographic data
-        self.NON_HISPANIC_WHITE_FIELD = "PCT086005"  # Total!!White
-        self.HISPANIC_FIELD = "PCT086006"  # Total!!Hispanic or Latino
-        self.OTHER_RACE_FIELD = "PCT086007"  # Total!!Other Ethnic Origin or Ra
-
-        self.TOTAL_RACE_POPULATION_VI_FIELD = "P003001"  # Total
-        self.BLACK_VI_FIELD = (
-            "P003003"  # Total!!One race!!Black or African American alone
-        )
-        self.AMERICAN_INDIAN_VI_FIELD = "P003005"  # Total!!One race!!American Indian and Alaska Native alone
-        self.ASIAN_VI_FIELD = "P003006"  # Total!!One race!!Asian alone
-        self.HAWAIIAN_VI_FIELD = "P003007"  # Total!!One race!!Native Hawaiian and Other Pacific Islander alone
-        self.TWO_OR_MORE_RACES_VI_FIELD = "P003009"  # Total!!Two or More Races
-        self.NON_HISPANIC_WHITE_VI_FIELD = (
-            "P005006"  # Total!!Not Hispanic or Latino!!One race!!White alone
-        )
-        self.HISPANIC_VI_FIELD = "P005002"  # Total!!Hispanic or Latino
-        self.OTHER_RACE_VI_FIELD = (
-            "P003008"  # Total!!One race!!Some Other Race alone
-        )
-        self.TOTAL_RACE_POPULATION_VI_FIELD = "P003001"  # Total
-
-        self.TOTAL_RACE_POPULATION_FIELD_NAME = (
-            "Total population surveyed on racial data"
-        )
-        self.BLACK_FIELD_NAME = "Black or African American"
-        self.AMERICAN_INDIAN_FIELD_NAME = "American Indian / Alaska Native"
-        self.ASIAN_FIELD_NAME = "Asian"
-        self.HAWAIIAN_FIELD_NAME = "Native Hawaiian or Pacific"
-        self.TWO_OR_MORE_RACES_FIELD_NAME = "two or more races"
-        self.NON_HISPANIC_WHITE_FIELD_NAME = "White"
-        self.HISPANIC_FIELD_NAME = "Hispanic or Latino"
-        # Note that `other` is lowercase because the whole field will show up in the download
-        # file as "Percent other races"
-        self.OTHER_RACE_FIELD_NAME = "other races"
-
-        # Name output demographics fields.
-        self.RE_OUTPUT_FIELDS = [
-            self.BLACK_FIELD_NAME,
-            self.AMERICAN_INDIAN_FIELD_NAME,
-            self.ASIAN_FIELD_NAME,
-            self.HAWAIIAN_FIELD_NAME,
-            self.TWO_OR_MORE_RACES_FIELD_NAME,
-            self.NON_HISPANIC_WHITE_FIELD_NAME,
-            self.HISPANIC_FIELD_NAME,
-            self.OTHER_RACE_FIELD_NAME,
-        ]
-
-        var_list = [
-            self.MEDIAN_INCOME_FIELD,
-            self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD,
-            self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD,
-            self.TOTAL_POPULATION_FIELD,
-            self.MALE_HIGH_SCHOOL_ED_FIELD,
-            self.FEMALE_HIGH_SCHOOL_ED_FIELD,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE,
-            self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD,
-            self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD,
-            self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD,
-            self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD,
-            self.TOTAL_POP_FIELD,
-            self.TOTAL_RACE_POPULATION_FIELD,
-            self.ASIAN_FIELD,
-            self.BLACK_FIELD,
-            self.HAWAIIAN_FIELD,
-            self.NON_HISPANIC_WHITE_FIELD,
-            self.HISPANIC_FIELD,
-            self.OTHER_RACE_FIELD,
-        ]
-        var_list = ",".join(var_list)
-
-        var_list_vi = [
-            self.MEDIAN_INCOME_VI_FIELD,
-            self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_VI_FIELD,
-            self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD,
-            self.TOTAL_POPULATION_VI_FIELD,
-            self.MALE_HIGH_SCHOOL_ED_VI_FIELD,
-            self.FEMALE_HIGH_SCHOOL_ED_VI_FIELD,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_ONE,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_TWO,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_THREE,
-            self.EMPLOYMENT_MALE_IN_LABOR_FORCE_VI_FIELD,
-            self.EMPLOYMENT_MALE_UNEMPLOYED_VI_FIELD,
-            self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_VI_FIELD,
-            self.EMPLOYMENT_FEMALE_UNEMPLOYED_VI_FIELD,
-            self.TOTAL_POP_VI_FIELD,
-            self.BLACK_VI_FIELD,
-            self.AMERICAN_INDIAN_VI_FIELD,
-            self.ASIAN_VI_FIELD,
-            self.HAWAIIAN_VI_FIELD,
-            self.TWO_OR_MORE_RACES_VI_FIELD,
-            self.NON_HISPANIC_WHITE_VI_FIELD,
-            self.HISPANIC_VI_FIELD,
-            self.OTHER_RACE_VI_FIELD,
-            self.TOTAL_RACE_POPULATION_VI_FIELD,
-        ]
-        var_list_vi = ",".join(var_list_vi)
-
-        self.FIELD_NAME_XWALK = {
-            self.MEDIAN_INCOME_FIELD: self.MEDIAN_INCOME_FIELD_NAME,
-            self.MEDIAN_INCOME_VI_FIELD: self.MEDIAN_INCOME_FIELD_NAME,
-            self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD: self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME,
-            self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_VI_FIELD: self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME,
-            self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD: self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME,
-            self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_VI_FIELD: self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME,
-            self.TOTAL_POPULATION_FIELD: self.TOTAL_POPULATION_FIELD_NAME,
-            self.TOTAL_POPULATION_VI_FIELD: self.TOTAL_POPULATION_FIELD_NAME,
-            self.MALE_HIGH_SCHOOL_ED_FIELD: self.MALE_HIGH_SCHOOL_ED_FIELD_NAME,
-            self.MALE_HIGH_SCHOOL_ED_VI_FIELD: self.MALE_HIGH_SCHOOL_ED_FIELD_NAME,
-            self.FEMALE_HIGH_SCHOOL_ED_FIELD: self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME,
-            self.FEMALE_HIGH_SCHOOL_ED_VI_FIELD: self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_ONE: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_TWO: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE,
-            self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_VI_PART_THREE: self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE,
-            self.EMPLOYMENT_MALE_IN_LABOR_FORCE_VI_FIELD: self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD,
-            self.EMPLOYMENT_MALE_UNEMPLOYED_VI_FIELD: self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD,
-            self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_VI_FIELD: self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD,
-            self.EMPLOYMENT_FEMALE_UNEMPLOYED_VI_FIELD: self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD,
-            self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD: self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD,
-            self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD: self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD,
-            self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD: self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD,
-            self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD: self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD,
-            self.TOTAL_RACE_POPULATION_FIELD: self.TOTAL_RACE_POPULATION_FIELD_NAME,
-            self.TOTAL_RACE_POPULATION_VI_FIELD: self.TOTAL_RACE_POPULATION_FIELD_NAME,
-            # Note there is no American Indian data for AS/GU/MI
-            self.AMERICAN_INDIAN_VI_FIELD: self.AMERICAN_INDIAN_FIELD_NAME,
-            self.ASIAN_FIELD: self.ASIAN_FIELD_NAME,
-            self.ASIAN_VI_FIELD: self.ASIAN_FIELD_NAME,
-            self.BLACK_FIELD: self.BLACK_FIELD_NAME,
-            self.BLACK_VI_FIELD: self.BLACK_FIELD_NAME,
-            self.HAWAIIAN_FIELD: self.HAWAIIAN_FIELD_NAME,
-            self.HAWAIIAN_VI_FIELD: self.HAWAIIAN_FIELD_NAME,
-            self.TWO_OR_MORE_RACES_VI_FIELD: self.TWO_OR_MORE_RACES_FIELD_NAME,
-            self.NON_HISPANIC_WHITE_FIELD: self.NON_HISPANIC_WHITE_FIELD_NAME,
-            self.NON_HISPANIC_WHITE_VI_FIELD: self.NON_HISPANIC_WHITE_FIELD_NAME,
-            self.HISPANIC_FIELD: self.HISPANIC_FIELD_NAME,
-            self.HISPANIC_VI_FIELD: self.HISPANIC_FIELD_NAME,
-            self.OTHER_RACE_FIELD: self.OTHER_RACE_FIELD_NAME,
-            self.OTHER_RACE_VI_FIELD: self.OTHER_RACE_FIELD_NAME,
-        }
-
-        # To do: Ask Census Slack Group about whether you need to hardcode the county fips
-        # https://uscensusbureau.slack.com/archives/C6DGLC05B/p1635218909012600
-        self.ISLAND_TERRITORIES = [
-            {
-                "state_abbreviation": "as",
-                "fips": "60",
-                "county_fips": ["010", "020", "030", "040", "050"],
-                "var_list": var_list,
-                # Note: we hardcode the median income for each territory in this dict,
-                # because that data is hard to programmatically access.
-                self.TERRITORY_MEDIAN_INCOME_FIELD: 23892,
-            },
-            {
-                "state_abbreviation": "gu",
-                "fips": "66",
-                "county_fips": ["010"],
-                "var_list": var_list,
-                self.TERRITORY_MEDIAN_INCOME_FIELD: 48274,
-            },
-            {
-                "state_abbreviation": "mp",
-                "fips": "69",
-                "county_fips": ["085", "100", "110", "120"],
-                "var_list": var_list,
-                self.TERRITORY_MEDIAN_INCOME_FIELD: 19958,
-            },
-            {
-                "state_abbreviation": "vi",
-                "fips": "78",
-                "county_fips": ["010", "020", "030"],
-                "var_list": var_list_vi,
-                self.TERRITORY_MEDIAN_INCOME_FIELD: 37254,
-            },
-        ]
-
-        self.API_URL = (
-            "https://api.census.gov/data/{}/dec/{}?get=NAME,{}"
-            + "&for=tract:*&in=state:{}%20county:{}"
-        )
-
         census_api_key = os.environ.get("CENSUS_API_KEY")
         if census_api_key:
-            self.API_URL = self.API_URL + f"&key={census_api_key}"
+            url += f"&key={census_api_key}"
+        return url
 
-        self.final_race_fields: List[str] = []
+    def __get_destination_path(
+        self,
+        state_abbreviation: str,
+        fips: str,
+        county: str,
+        test_path: Path = None,
+    ) -> str:
+        root_path = test_path or self.get_sources_path()
+        return (
+            root_path
+            / str(self.DECENNIAL_YEAR)
+            / state_abbreviation
+            / fips
+            / county
+            / "census.json"
+        )
 
-        self.df: pd.DataFrame
-        self.df_vi: pd.DataFrame
-        self.df_all: pd.DataFrame
-
-    def get_data_sources(self) -> [DataSource]:
+    def __init__(self):
+        self.df_all = pd.DataFrame()
+        self.final_race_fields = []
 
+    def get_data_sources(self) -> List[DataSource]:
         sources = []
-
-        for island in self.ISLAND_TERRITORIES:
+        for island in DEC_TERRITORY_PARAMS:
             for county in island["county_fips"]:
-
-                api_url = self.API_URL.format(
-                    self.DECENNIAL_YEAR,
+                api_url = self.__get_api_url(
                     island["state_abbreviation"],
-                    island["var_list"],
+                    ",".join(island["xwalk"].keys()),
                     island["fips"],
                     county,
                 )
-
                 sources.append(
                     FileDataSource(
-                        source=api_url,
-                        destination=self.get_sources_path()
-                        / str(self.DECENNIAL_YEAR)
-                        / island["state_abbreviation"]
-                        / island["fips"]
-                        / county
-                        / "census.json",
+                        api_url,
+                        self.__get_destination_path(
+                            island["state_abbreviation"], island["fips"], county
+                        ),
                     )
                 )
-
         return sources
 
-    def extract(self, use_cached_data_sources: bool = False) -> None:
-
-        super().extract(
-            use_cached_data_sources
-        )  # download and extract data sources
-
-        dfs = []
-        dfs_vi = []
-        for island in self.ISLAND_TERRITORIES:
-            logger.debug(
-                f"Downloading data for state/territory {island['state_abbreviation']}"
-            )
-            for county in island["county_fips"]:
-
+    def extract(
+        self,
+        use_cached_data_sources: bool = False,
+        test_territory_params=None,
+        test_path: Path = None,
+    ) -> None:
+        super().extract(use_cached_data_sources)
+        for territory in test_territory_params or DEC_TERRITORY_PARAMS:
+            for county in territory["county_fips"]:
+                abbr = territory["state_abbreviation"]
+                file_path = self.__get_destination_path(
+                    abbr, territory["fips"], county, test_path=test_path
+                )
                 try:
-                    filepath = (
-                        self.get_sources_path()
-                        / str(self.DECENNIAL_YEAR)
-                        / island["state_abbreviation"]
-                        / island["fips"]
-                        / county
-                        / "census.json"
-                    )
-                    df = json.load(filepath.open())
-                except ValueError as e:
+                    json_data = json.load(file_path.open())
+                except (FileNotFoundError, ValueError) as e:
                     logger.error(
                         f"Could not load content in census decennial ETL because {e}."
                     )
+                    raise
+                df = pd.DataFrame(json_data[1:], columns=json_data[0])
+                # Rename the columns to their common names
+                df.rename(columns=territory["xwalk"], inplace=True)
 
-                # First row is the header
-                df = pd.DataFrame(df[1:], columns=df[0])
+                # Convert columns to numeric where applicable
+                for column in df.columns:
+                    if column not in ["state", "county", "NAME", "tract"]:
+                        df[column] = pd.to_numeric(df[column], errors="ignore")
 
-                for col in island["var_list"].split(","):
-                    # Converting appropriate variables to numeric.
-                    # Also replacing 0s with NaNs
-                    df[col] = pd.to_numeric(df[col])
+                # Add the territory median income
+                df.loc[
+                    df[field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019]
+                    > 0,
+                    DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME,
+                ] = territory["median_income"]
+                self.df_all = pd.concat([self.df_all, df], ignore_index=True)
 
-                    # TO-DO: CHECK THIS. I think it makes sense to replace 0 with NaN
-                    # because for our variables of interest (e.g. Median Household Income,
-                    # it doesn't make sense for that to be 0.)
-                    # Likely, it's actually missing but can't find a cite for that in the docs
-                    df[col] = df[col].replace(0, np.nan)
+    def _merge_tracts_2010_compatibility(self):
+        """Merges tract 69120950200 to match 2010 tracts"""
+        # MP 69/120 69120950200 = 69120950201, 69120950202
+        # Tract has been split, but 69120950202 has no data, so we just make 69120950200 = 69120950201
+        self.df_all = self.df_all.drop(
+            self.df_all[
+                self.df_all[field_names.GEOID_TRACT_FIELD] == "69120950202"
+            ].index
+        )
+        self.df_all.loc[
+            self.df_all[field_names.GEOID_TRACT_FIELD] == "69120950201",
+            field_names.GEOID_TRACT_FIELD,
+        ] = "69120950200"
 
-                if island["state_abbreviation"] == "vi":
-                    dfs_vi.append(df)
-                else:
-                    dfs.append(df)
+    def _impute_income(self, geojson_path: Path):
+        """Impute income for both income measures."""
+        # Merges Census geojson to imput values from.
+        logger.debug(f"Reading GeoJSON from {geojson_path}")
+        geo_df = gpd.read_file(geojson_path)
+        self.df_all = CensusACSETL.merge_geojson(
+            df=self.df_all,
+            usa_geo_df=geo_df,
+        )
 
-        self.df = pd.concat(dfs)
-        self.df_vi = pd.concat(dfs_vi)
+        logger.debug("Imputing income information")
+        impute_var_named_tup_list = [
+            CensusACSETL.ImputeVariables(
+                raw_field_name=field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019,
+                imputed_field_name=DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL,
+            ),
+        ]
+        self.df_all = calculate_income_measures(
+            impute_var_named_tup_list=impute_var_named_tup_list,
+            geo_df=self.df_all,
+            geoid_field=self.GEOID_TRACT_FIELD_NAME,
+            population_field=field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019,
+        )
 
-    def transform(self) -> None:
-        # Rename All Fields
-        self.df.rename(columns=self.FIELD_NAME_XWALK, inplace=True)
-        self.df_vi.rename(columns=self.FIELD_NAME_XWALK, inplace=True)
+        logger.debug("Calculating with imputed values")
+        self.df_all[
+            field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019
+        ] = (
+            self.df_all[
+                field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019
+            ].fillna(
+                self.df_all[
+                    DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL
+                ]
+            )
+            # Use clip to ensure that the values are not negative
+        ).clip(
+            lower=0
+        )
 
-        # Combine the dfs after renaming
-        self.df_all = pd.concat([self.df, self.df_vi])
+        # All values should have a value at this point for tracts with >0 population
+        assert (
+            self.df_all[
+                self.df_all[
+                    field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019
+                ]
+                >= 1
+            ][
+                field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019
+            ]
+            .isna()
+            .sum()
+            == 0
+        ), "Error: not all values were filled with imputations..."
 
-        # Rename total population:
-        self.df_all[self.TOTAL_POP_FIELD_NAME] = self.df_all[
-            self.TOTAL_POP_FIELD
+        # We generate a boolean that is TRUE when there is an imputed income but not a baseline income, and FALSE otherwise.
+        # This allows us to see which tracts have an imputed income.
+        self.df_all[field_names.ISLAND_AREAS_IMPUTED_INCOME_FLAG_FIELD] = (
+            self.df_all[
+                field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019
+            ].notna()
+            & self.df_all[
+                field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019
+            ].isna()
+        )
+
+    def transform(self, geojson_path: Path = CENSUS_GEOJSON_PATH) -> None:
+        # Creating Geo ID (Census Block Group) Field Name
+        self.df_all[field_names.GEOID_TRACT_FIELD] = (
+            self.df_all["state"] + self.df_all["county"] + self.df_all["tract"]
+        )
+
+        # Combine the two MP 2020 tracts that were split from one 2010 tract
+        self._merge_tracts_2010_compatibility()
+
+        # Replace invalid numeric values with NaN
+        numeric_columns = self.df_all.select_dtypes(include="number").columns
+        for num_column in numeric_columns:
+            self.df_all.loc[self.df_all[num_column] < -999, num_column] = np.nan
+
+        # Percentage of households below 100% FPL
+        self.df_all[
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019
+        ] = (
+            self.df_all[DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_5]
+            + self.df_all[DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_74]
+            + self.df_all[DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_UNDER_0_99]
+        ) / self.df_all[
+            DEC_FIELD_NAMES.TOTAL_HOUSEHOLD_POVERTY_LEVEL
         ]
 
         # Percentage of households below 200% which is
-        # [PBG083001 (total) - PBG083010 (num households over 200%)] / PBG083001 (total)
         self.df_all[
-            self.PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL_FIELD_NAME
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019
         ] = (
-            self.df_all[
-                self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME
-            ]
-            - self.df_all[self.HOUSEHOLD_OVER_200_PERC_POVERTY_LEVEL_FIELD_NAME]
+            self.df_all[DEC_FIELD_NAMES.TOTAL_HOUSEHOLD_POVERTY_LEVEL]
+            - self.df_all[DEC_FIELD_NAMES.HOUSEHOLD_POVERTY_LEVEL_OVER_2_0]
         ) / self.df_all[
-            self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME
-        ]
-
-        # Percentage of households below 100% FPL
-        # which we get by adding `Total!!Under .50`, `Total!!.50 to .74`, ` Total!!.75 to .99`,
-        # and then dividing by PBG083001 (total)
-        self.df_all[
-            self.PERCENTAGE_HOUSEHOLDS_BELOW_100_PERC_POVERTY_LEVEL_FIELD_NAME
-        ] = (
-            self.df_all[
-                self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_ONE
-            ]
-            + self.df_all[
-                self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_TWO
-            ]
-            + self.df_all[
-                self.HOUSEHOLD_UNDER_100_PERC_POVERTY_LEVEL_FIELD_PART_THREE
-            ]
-        ) / self.df_all[
-            self.TOTAL_HOUSEHOLD_RATIO_INCOME_TO_POVERTY_LEVEL_FIELD_NAME
+            DEC_FIELD_NAMES.TOTAL_HOUSEHOLD_POVERTY_LEVEL
         ]
 
         # Percentage High School Achievement is
         # Percentage = (Male + Female) / (Total)
-        self.df_all[self.PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME] = (
-            self.df_all[self.MALE_HIGH_SCHOOL_ED_FIELD_NAME]
-            + self.df_all[self.FEMALE_HIGH_SCHOOL_ED_FIELD_NAME]
-        ) / self.df_all[self.TOTAL_POPULATION_FIELD_NAME]
+        self.df_all[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019] = (
+            self.df_all[DEC_FIELD_NAMES.MALE_HIGH_SCHOOL_ED]
+            + self.df_all[DEC_FIELD_NAMES.FEMALE_HIGH_SCHOOL_ED]
+        ) / self.df_all[
+            field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019
+        ]
 
         # Calculate employment.
-        self.df_all[self.UNEMPLOYMENT_FIELD_NAME] = (
-            self.df_all[self.EMPLOYMENT_MALE_UNEMPLOYED_FIELD]
-            + self.df_all[self.EMPLOYMENT_FEMALE_UNEMPLOYED_FIELD]
+        self.df_all[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019] = (
+            self.df_all[DEC_FIELD_NAMES.EMPLOYMENT_MALE_UNEMPLOYED]
+            + self.df_all[DEC_FIELD_NAMES.EMPLOYMENT_FEMALE_UNEMPLOYED]
         ) / (
-            self.df_all[self.EMPLOYMENT_MALE_IN_LABOR_FORCE_FIELD]
-            + self.df_all[self.EMPLOYMENT_FEMALE_IN_LABOR_FORCE_FIELD]
+            self.df_all[DEC_FIELD_NAMES.EMPLOYMENT_MALE_IN_LABOR_FORCE]
+            + self.df_all[DEC_FIELD_NAMES.EMPLOYMENT_FEMALE_IN_LABOR_FORCE]
         )
 
         # Calculate area median income
-        median_income_df = pd.DataFrame(self.ISLAND_TERRITORIES)
-        median_income_df = median_income_df[
-            ["fips", self.TERRITORY_MEDIAN_INCOME_FIELD]
-        ]
-        self.df_all = self.df_all.merge(
-            right=median_income_df, left_on="state", right_on="fips", how="left"
-        )
-        self.df_all[self.AREA_MEDIAN_INCOME_FIELD_NAME] = (
-            self.df_all[self.MEDIAN_INCOME_FIELD_NAME]
-            / self.df_all[self.TERRITORY_MEDIAN_INCOME_FIELD]
+        self.df_all[
+            field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019
+        ] = (
+            self.df_all[field_names.CENSUS_DECENNIAL_MEDIAN_INCOME_2019]
+            / self.df_all[DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME]
         )
 
-        # Creating Geo ID (Census Block Group) Field Name
-        self.df_all[self.GEOID_TRACT_FIELD_NAME] = (
-            self.df_all["state"] + self.df_all["county"] + self.df_all["tract"]
+        # Calculate college attendance
+        self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_POPULATION] = (
+            self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_MALE_ENROLLED]
+            + self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_FEMALE_ENROLLED]
+        )
+        self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_PERCENT] = (
+            self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_MALE_ENROLLED]
+            + self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_FEMALE_ENROLLED]
+        ) / self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_TOTAL_ENROLLED]
+        self.df_all[DEC_FIELD_NAMES.COLLEGE_NON_ATTENDANCE_PERCENT] = (
+            1 - self.df_all[DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_PERCENT]
         )
 
         # Calculate stats by race
-        for race_field_name in self.RE_OUTPUT_FIELDS:
+        for race_field_name in OUTPUT_RACE_FIELDS:
             output_field_name = (
                 field_names.PERCENT_PREFIX
                 + race_field_name
-                + field_names.ISLAND_AREA_BACKFILL_SUFFIX
+                # 2010 vs 2020 WARNING
+                # We must keep the old 2009 date to make it compatible with all the other 2010 data
+                + f" in {field_names.DEC_DATA_YEAR}"
             )
-            self.final_race_fields.append(output_field_name)
             self.df_all[output_field_name] = (
                 self.df_all[race_field_name]
-                / self.df_all[self.TOTAL_RACE_POPULATION_FIELD_NAME]
+                / self.df_all[DEC_FIELD_NAMES.TOTAL_RACE_POPULATION]
             )
+            self.final_race_fields.append(output_field_name)
 
         # Reporting Missing Values
         for col in self.df_all.columns:
@@ -532,22 +306,27 @@ class CensusDecennialETL(ExtractTransformLoad):
                 f"There are {missing_value_count} missing values in the field {col} out of a total of {self.df_all.shape[0]} rows"
             )
 
+        self._impute_income(geojson_path)
+
     def load(self) -> None:
-        # mkdir census
         self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
-
         columns_to_include = [
-            self.GEOID_TRACT_FIELD_NAME,
-            self.TOTAL_POP_FIELD_NAME,
-            self.MEDIAN_INCOME_FIELD_NAME,
-            self.TERRITORY_MEDIAN_INCOME_FIELD,
-            self.AREA_MEDIAN_INCOME_FIELD_NAME,
-            self.PERCENTAGE_HOUSEHOLDS_BELOW_100_PERC_POVERTY_LEVEL_FIELD_NAME,
-            self.PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL_FIELD_NAME,
-            self.PERCENTAGE_HIGH_SCHOOL_ED_FIELD_NAME,
-            self.UNEMPLOYMENT_FIELD_NAME,
+            field_names.GEOID_TRACT_FIELD,
+            field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_MEDIAN_INCOME_2019,
+            DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME,
+            field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019,
+            DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL,
+            field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019,
+            DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_PERCENT,
+            DEC_FIELD_NAMES.COLLEGE_NON_ATTENDANCE,
+            DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_POPULATION,
+            field_names.ISLAND_AREAS_IMPUTED_INCOME_FLAG_FIELD,
         ] + self.final_race_fields
-
         self.df_all[columns_to_include].to_csv(
             path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False
         )
diff --git a/data/data-pipeline/data_pipeline/files/readme-version-2.0.md b/data/data-pipeline/data_pipeline/files/readme-version-2.0.md
new file mode 100644
index 00000000..e69de29b
diff --git a/data/data-pipeline/data_pipeline/ipython/donut_hole_testing_2024.ipynb b/data/data-pipeline/data_pipeline/ipython/donut_hole_testing_2024.ipynb
new file mode 100644
index 00000000..ba196d42
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/ipython/donut_hole_testing_2024.ipynb
@@ -0,0 +1,5773 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "75d2705f",
+   "metadata": {},
+   "source": [
+    "# EXPLORATION OF DONUT HOLE WATER BOUNDARY ISSUE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dea57328",
+   "metadata": {},
+   "source": [
+    "## Imports, constants, and data load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7ac0d5d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "module_path = os.path.abspath(os.path.join(\"../..\"))\n",
+    "if module_path not in sys.path:\n",
+    "    sys.path.append(module_path)\n",
+    "\n",
+    "from data_pipeline.config import settings\n",
+    "from data_pipeline.etl.sources.geo_utils import (\n",
+    "    add_tracts_for_geometries,\n",
+    "    get_tract_geojson,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "16b9ffd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pull out necessary definitions from field_names.py\n",
+    "ORIGINAL_TRACT = \"ORIGINAL_TRACT\"\n",
+    "SCORE_N_COMMUNITIES = \"Definition N (communities)\"\n",
+    "GEOID_TRACT_FIELD = \"GEOID10_TRACT\"\n",
+    "ADJACENT_MEAN_SUFFIX = \" (based on adjacency index and low income alone)\"\n",
+    "ADJACENCY_INDEX_SUFFIX = \" (average of neighbors)\"\n",
+    "CAM_ID = '48061012305'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b3c256f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-11-19 19:29:38,131 [     data_pipeline.etl.sources.geo_utils] DEBUG    Loading tract geometry data from census ETL\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Index(['STATEFP10', 'COUNTYFP10', 'TRACTCE10', 'GEOID10_TRACT', 'NAME10',\n",
+       "       'NAMELSAD10', 'MTFCC10', 'FUNCSTAT10', 'ALAND10', 'AWATER10',\n",
+       "       'INTPTLAT10', 'INTPTLON10', 'geometry'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load Census data\n",
+    "tract_data = get_tract_geojson()\n",
+    "tract_data.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f829c206",
+   "metadata": {},
+   "source": [
+    "## Look at example tract in Cameron, TX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6d133ca",
+   "metadata": {},
+   "source": [
+    "Tract no. 48061012305 in Cameron, TX is an example of a tract that displays the donut hole water boundary issue:  all the tracts that it borders are classified as disadvantaged, and it meets the poverty threshold, yet it is being incorrectly categorized as non-disadvantaged due to its water boundary. Here we pull all adjacent tracts, compare to what we see on the map, and then look for solutions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "106425b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STATEFP10</th>\n",
+       "      <th>COUNTYFP10</th>\n",
+       "      <th>TRACTCE10</th>\n",
+       "      <th>ORIGINAL_TRACT</th>\n",
+       "      <th>NAME10</th>\n",
+       "      <th>NAMELSAD10</th>\n",
+       "      <th>MTFCC10</th>\n",
+       "      <th>FUNCSTAT10</th>\n",
+       "      <th>ALAND10</th>\n",
+       "      <th>AWATER10</th>\n",
+       "      <th>INTPTLAT10</th>\n",
+       "      <th>INTPTLON10</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>Definition N (communities)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>+26.2732070</td>\n",
+       "      <td>-097.2763703</td>\n",
+       "      <td>POLYGON ((-97.22490 26.41153, -97.22436 26.411...</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  STATEFP10 COUNTYFP10 TRACTCE10 ORIGINAL_TRACT  NAME10           NAMELSAD10  \\\n",
+       "0        48        061    012305    48061012305  123.05  Census Tract 123.05   \n",
+       "\n",
+       "  MTFCC10 FUNCSTAT10   ALAND10   AWATER10   INTPTLAT10    INTPTLON10  \\\n",
+       "0   G5020          S  25920881  324996211  +26.2732070  -097.2763703   \n",
+       "\n",
+       "                                            geometry  \\\n",
+       "0  POLYGON ((-97.22490 26.41153, -97.22436 26.411...   \n",
+       "\n",
+       "   Definition N (communities)  \n",
+       "0                       False  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## look at census data for Cameron\n",
+    "cam_only_df = pd.DataFrame({GEOID_TRACT_FIELD:[CAM_ID], SCORE_N_COMMUNITIES:[False]})\n",
+    "\n",
+    "cam_only_df: gpd.GeoDataFrame = tract_data.merge(\n",
+    "        cam_only_df, on=GEOID_TRACT_FIELD\n",
+    "    )\n",
+    "cam_only_df = cam_only_df.rename(columns={GEOID_TRACT_FIELD: ORIGINAL_TRACT})\n",
+    "cam_only_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "87cdbbaf",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STATEFP10_left</th>\n",
+       "      <th>COUNTYFP10_left</th>\n",
+       "      <th>TRACTCE10_left</th>\n",
+       "      <th>ORIGINAL_TRACT</th>\n",
+       "      <th>NAME10_left</th>\n",
+       "      <th>NAMELSAD10_left</th>\n",
+       "      <th>MTFCC10_left</th>\n",
+       "      <th>FUNCSTAT10_left</th>\n",
+       "      <th>ALAND10_left</th>\n",
+       "      <th>AWATER10_left</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TRACTCE10_right</th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>NAME10_right</th>\n",
+       "      <th>NAMELSAD10_right</th>\n",
+       "      <th>MTFCC10_right</th>\n",
+       "      <th>FUNCSTAT10_right</th>\n",
+       "      <th>ALAND10_right</th>\n",
+       "      <th>AWATER10_right</th>\n",
+       "      <th>INTPTLAT10_right</th>\n",
+       "      <th>INTPTLON10_right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>+26.2713946</td>\n",
+       "      <td>-097.4414470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>+26.5154317</td>\n",
+       "      <td>-097.5790835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>+26.5064260</td>\n",
+       "      <td>-097.2240134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>+25.9786218</td>\n",
+       "      <td>-097.2580863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012304</td>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>123.04</td>\n",
+       "      <td>Census Tract 123.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5761299</td>\n",
+       "      <td>6286773</td>\n",
+       "      <td>+26.0631407</td>\n",
+       "      <td>-097.2184868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012301</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>123.01</td>\n",
+       "      <td>Census Tract 123.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>137622651</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>+26.1581170</td>\n",
+       "      <td>-097.3180348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>268272237</td>\n",
+       "      <td>+26.1902408</td>\n",
+       "      <td>-097.1473235</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  STATEFP10_left COUNTYFP10_left TRACTCE10_left ORIGINAL_TRACT NAME10_left  \\\n",
+       "0             48             061         012305    48061012305      123.05   \n",
+       "0             48             061         012305    48061012305      123.05   \n",
+       "0             48             061         012305    48061012305      123.05   \n",
+       "0             48             061         012305    48061012305      123.05   \n",
+       "0             48             061         012305    48061012305      123.05   \n",
+       "0             48             061         012305    48061012305      123.05   \n",
+       "0             48             061         012305    48061012305      123.05   \n",
+       "\n",
+       "       NAMELSAD10_left MTFCC10_left FUNCSTAT10_left  ALAND10_left  \\\n",
+       "0  Census Tract 123.05        G5020               S      25920881   \n",
+       "0  Census Tract 123.05        G5020               S      25920881   \n",
+       "0  Census Tract 123.05        G5020               S      25920881   \n",
+       "0  Census Tract 123.05        G5020               S      25920881   \n",
+       "0  Census Tract 123.05        G5020               S      25920881   \n",
+       "0  Census Tract 123.05        G5020               S      25920881   \n",
+       "0  Census Tract 123.05        G5020               S      25920881   \n",
+       "\n",
+       "   AWATER10_left  ... TRACTCE10_right GEOID10_TRACT NAME10_right  \\\n",
+       "0      324996211  ...          010100   48061010100          101   \n",
+       "0      324996211  ...          950700   48489950700         9507   \n",
+       "0      324996211  ...          990000   48489990000         9900   \n",
+       "0      324996211  ...          012700   48061012700          127   \n",
+       "0      324996211  ...          012304   48061012304       123.04   \n",
+       "0      324996211  ...          012301   48061012301       123.01   \n",
+       "0      324996211  ...          990000   48061990000         9900   \n",
+       "\n",
+       "      NAMELSAD10_right  MTFCC10_right FUNCSTAT10_right ALAND10_right  \\\n",
+       "0     Census Tract 101          G5020                S     365458278   \n",
+       "0    Census Tract 9507          G5020                S    1025415174   \n",
+       "0    Census Tract 9900          G5020                S             0   \n",
+       "0     Census Tract 127          G5020                S     129622799   \n",
+       "0  Census Tract 123.04          G5020                S       5761299   \n",
+       "0  Census Tract 123.01          G5020                S     137622651   \n",
+       "0    Census Tract 9900          G5020                S             0   \n",
+       "\n",
+       "  AWATER10_right INTPTLAT10_right INTPTLON10_right  \n",
+       "0      113041841      +26.2713946     -097.4414470  \n",
+       "0      377000048      +26.5154317     -097.5790835  \n",
+       "0      121414926      +26.5064260     -097.2240134  \n",
+       "0       67725054      +25.9786218     -097.2580863  \n",
+       "0        6286773      +26.0631407     -097.2184868  \n",
+       "0      119430273      +26.1581170     -097.3180348  \n",
+       "0      268272237      +26.1902408     -097.1473235  \n",
+       "\n",
+       "[7 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get all the tracts that are adjacent to cameron\n",
+    "cam_adjacent_tracts: gpd.GeoDataFrame = cam_only_df.sjoin(\n",
+    "        tract_data, predicate=\"touches\"\n",
+    "    )\n",
+    "cam_adjacent_tracts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "af3aa581",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['STATEFP10_left', 'COUNTYFP10_left', 'TRACTCE10_left', 'ORIGINAL_TRACT',\n",
+       "       'NAME10_left', 'NAMELSAD10_left', 'MTFCC10_left', 'FUNCSTAT10_left',\n",
+       "       'ALAND10_left', 'AWATER10_left', 'INTPTLAT10_left', 'INTPTLON10_left',\n",
+       "       'geometry', 'Definition N (communities)', 'index_right',\n",
+       "       'STATEFP10_right', 'COUNTYFP10_right', 'TRACTCE10_right',\n",
+       "       'GEOID10_TRACT', 'NAME10_right', 'NAMELSAD10_right', 'MTFCC10_right',\n",
+       "       'FUNCSTAT10_right', 'ALAND10_right', 'AWATER10_right',\n",
+       "       'INTPTLAT10_right', 'INTPTLON10_right'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cam_adjacent_tracts.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "85559ed4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ORIGINAL_TRACT</th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>AWATER10_left</th>\n",
+       "      <th>ALAND10_left</th>\n",
+       "      <th>AWATER10_right</th>\n",
+       "      <th>ALAND10_right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>365458278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>1025415174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>129622799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>6286773</td>\n",
+       "      <td>5761299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>137622651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>268272237</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  ORIGINAL_TRACT GEOID10_TRACT  AWATER10_left  ALAND10_left  AWATER10_right  \\\n",
+       "0    48061012305   48061010100      324996211      25920881       113041841   \n",
+       "0    48061012305   48489950700      324996211      25920881       377000048   \n",
+       "0    48061012305   48489990000      324996211      25920881       121414926   \n",
+       "0    48061012305   48061012700      324996211      25920881        67725054   \n",
+       "0    48061012305   48061012304      324996211      25920881         6286773   \n",
+       "0    48061012305   48061012301      324996211      25920881       119430273   \n",
+       "0    48061012305   48061990000      324996211      25920881       268272237   \n",
+       "\n",
+       "   ALAND10_right  \n",
+       "0      365458278  \n",
+       "0     1025415174  \n",
+       "0              0  \n",
+       "0      129622799  \n",
+       "0        5761299  \n",
+       "0      137622651  \n",
+       "0              0  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cam_adjacent_tracts[['ORIGINAL_TRACT', 'GEOID10_TRACT', \n",
+    "                 'AWATER10_left', 'ALAND10_left', 'AWATER10_right', 'ALAND10_right']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "60d0801f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # compare to what I see on the map for Cameron:\n",
+    "\n",
+    "# 48489950700 Willacy County\n",
+    "# 48061010100 Cameron County (contains Laguna Atascosa)\n",
+    "# 48061012301 Cameron County (contains KPIL)\n",
+    "# 48061012304 Cameron County (contains Port Isabel)\n",
+    "# 48061012700 Cameron County (contains Boca Chica Village)\n",
+    "\n",
+    "# # what about 48489990000 and 48061990000?\n",
+    "# both have ALAND10 = 0\n",
+    "# and both tract IDs end in 9990000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5201e422",
+   "metadata": {},
+   "source": [
+    "Proposed solution: remove tracts where ALAND10=10 (ie water-only tracts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43dc09e8",
+   "metadata": {},
+   "source": [
+    "## Compare water-only tracts to tracts with land area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4045bb4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "367"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How many of these water-only tracts exist?\n",
+    "len(tract_data[tract_data.ALAND10==0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "37fdf29b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STATEFP10</th>\n",
+       "      <th>COUNTYFP10</th>\n",
+       "      <th>TRACTCE10</th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>NAME10</th>\n",
+       "      <th>NAMELSAD10</th>\n",
+       "      <th>MTFCC10</th>\n",
+       "      <th>FUNCSTAT10</th>\n",
+       "      <th>ALAND10</th>\n",
+       "      <th>AWATER10</th>\n",
+       "      <th>INTPTLAT10</th>\n",
+       "      <th>INTPTLON10</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>water_only</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>36</td>\n",
+       "      <td>063</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>36063990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1550634668</td>\n",
+       "      <td>+43.4569085</td>\n",
+       "      <td>-078.7926530</td>\n",
+       "      <td>POLYGON ((-78.46554 43.37337, -78.47053 43.372...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>36</td>\n",
+       "      <td>085</td>\n",
+       "      <td>008900</td>\n",
+       "      <td>36085008900</td>\n",
+       "      <td>89</td>\n",
+       "      <td>Census Tract 89</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100710</td>\n",
+       "      <td>+40.6469814</td>\n",
+       "      <td>-074.1024106</td>\n",
+       "      <td>POLYGON ((-74.10504 40.64808, -74.10025 40.648...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1149</th>\n",
+       "      <td>36</td>\n",
+       "      <td>085</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>36085990100</td>\n",
+       "      <td>9901</td>\n",
+       "      <td>Census Tract 9901</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80255151</td>\n",
+       "      <td>+40.5255512</td>\n",
+       "      <td>-074.1085829</td>\n",
+       "      <td>POLYGON ((-74.25482 40.49390, -74.25308 40.491...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1219</th>\n",
+       "      <td>36</td>\n",
+       "      <td>013</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>36013990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1015062614</td>\n",
+       "      <td>+42.5006582</td>\n",
+       "      <td>-079.5141161</td>\n",
+       "      <td>POLYGON ((-79.24982 42.53745, -79.24982 42.537...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1226</th>\n",
+       "      <td>36</td>\n",
+       "      <td>075</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>36075990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>724224875</td>\n",
+       "      <td>+43.5955454</td>\n",
+       "      <td>-076.4442549</td>\n",
+       "      <td>POLYGON ((-76.55001 43.45923, -76.55064 43.458...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73379</th>\n",
+       "      <td>23</td>\n",
+       "      <td>015</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>23015990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>375299033</td>\n",
+       "      <td>+43.8029690</td>\n",
+       "      <td>-069.4652520</td>\n",
+       "      <td>POLYGON ((-69.68695 43.81613, -69.68695 43.815...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73399</th>\n",
+       "      <td>23</td>\n",
+       "      <td>031</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>23031990100</td>\n",
+       "      <td>9901</td>\n",
+       "      <td>Census Tract 9901</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>567248819</td>\n",
+       "      <td>+43.1483531</td>\n",
+       "      <td>-070.4998364</td>\n",
+       "      <td>POLYGON ((-70.22209 43.46688, -70.22432 43.464...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73509</th>\n",
+       "      <td>23</td>\n",
+       "      <td>023</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>23023990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>161372101</td>\n",
+       "      <td>+43.7084463</td>\n",
+       "      <td>-069.7721513</td>\n",
+       "      <td>POLYGON ((-69.69756 43.81594, -69.69755 43.813...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73527</th>\n",
+       "      <td>23</td>\n",
+       "      <td>013</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>23013990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1636919697</td>\n",
+       "      <td>+43.9538894</td>\n",
+       "      <td>-068.9327255</td>\n",
+       "      <td>POLYGON ((-68.73563 44.11917, -68.73179 44.117...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73629</th>\n",
+       "      <td>23</td>\n",
+       "      <td>005</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>23005990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>361952519</td>\n",
+       "      <td>+43.6058367</td>\n",
+       "      <td>-070.0776892</td>\n",
+       "      <td>POLYGON ((-70.07870 43.66881, -70.06517 43.673...</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>367 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      STATEFP10 COUNTYFP10 TRACTCE10 GEOID10_TRACT NAME10         NAMELSAD10  \\\n",
+       "808          36        063    990000   36063990000   9900  Census Tract 9900   \n",
+       "1015         36        085    008900   36085008900     89    Census Tract 89   \n",
+       "1149         36        085    990100   36085990100   9901  Census Tract 9901   \n",
+       "1219         36        013    990000   36013990000   9900  Census Tract 9900   \n",
+       "1226         36        075    990000   36075990000   9900  Census Tract 9900   \n",
+       "...         ...        ...       ...           ...    ...                ...   \n",
+       "73379        23        015    990000   23015990000   9900  Census Tract 9900   \n",
+       "73399        23        031    990100   23031990100   9901  Census Tract 9901   \n",
+       "73509        23        023    990000   23023990000   9900  Census Tract 9900   \n",
+       "73527        23        013    990000   23013990000   9900  Census Tract 9900   \n",
+       "73629        23        005    990000   23005990000   9900  Census Tract 9900   \n",
+       "\n",
+       "      MTFCC10 FUNCSTAT10  ALAND10    AWATER10   INTPTLAT10    INTPTLON10  \\\n",
+       "808     G5020          S        0  1550634668  +43.4569085  -078.7926530   \n",
+       "1015    G5020          S        0      100710  +40.6469814  -074.1024106   \n",
+       "1149    G5020          S        0    80255151  +40.5255512  -074.1085829   \n",
+       "1219    G5020          S        0  1015062614  +42.5006582  -079.5141161   \n",
+       "1226    G5020          S        0   724224875  +43.5955454  -076.4442549   \n",
+       "...       ...        ...      ...         ...          ...           ...   \n",
+       "73379   G5020          S        0   375299033  +43.8029690  -069.4652520   \n",
+       "73399   G5020          S        0   567248819  +43.1483531  -070.4998364   \n",
+       "73509   G5020          S        0   161372101  +43.7084463  -069.7721513   \n",
+       "73527   G5020          S        0  1636919697  +43.9538894  -068.9327255   \n",
+       "73629   G5020          S        0   361952519  +43.6058367  -070.0776892   \n",
+       "\n",
+       "                                                geometry  water_only  \n",
+       "808    POLYGON ((-78.46554 43.37337, -78.47053 43.372...        True  \n",
+       "1015   POLYGON ((-74.10504 40.64808, -74.10025 40.648...        True  \n",
+       "1149   POLYGON ((-74.25482 40.49390, -74.25308 40.491...        True  \n",
+       "1219   POLYGON ((-79.24982 42.53745, -79.24982 42.537...        True  \n",
+       "1226   POLYGON ((-76.55001 43.45923, -76.55064 43.458...        True  \n",
+       "...                                                  ...         ...  \n",
+       "73379  POLYGON ((-69.68695 43.81613, -69.68695 43.815...        True  \n",
+       "73399  POLYGON ((-70.22209 43.46688, -70.22432 43.464...        True  \n",
+       "73509  POLYGON ((-69.69756 43.81594, -69.69755 43.813...        True  \n",
+       "73527  POLYGON ((-68.73563 44.11917, -68.73179 44.117...        True  \n",
+       "73629  POLYGON ((-70.07870 43.66881, -70.06517 43.673...        True  \n",
+       "\n",
+       "[367 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# make a copy of the tract data\n",
+    "tract_explore = tract_data.copy()\n",
+    "\n",
+    "# add bool for whether tract has land area\n",
+    "tract_explore['water_only'] = tract_explore.ALAND10==0\n",
+    "\n",
+    "# preview water tracts\n",
+    "tract_explore[tract_explore.water_only]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d0c30aef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# confirm we can get the tract code stored in TRACTCE10 by taking last 6 chars of GEOID10_TRACT\n",
+    "# (because input df may not contain the isolated tract code column)\n",
+    "tract_explore['tract_code'] = tract_explore.GEOID10_TRACT.apply(lambda x: x[-6:])\n",
+    "assert all(tract_explore.tract_code==tract_explore.TRACTCE10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5436fdd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>TRACTCE10</th>\n",
+       "      <th>tract_code</th>\n",
+       "      <th>tract_code_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20071958100</td>\n",
+       "      <td>958100</td>\n",
+       "      <td>958100</td>\n",
+       "      <td>958100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20175965600</td>\n",
+       "      <td>965600</td>\n",
+       "      <td>965600</td>\n",
+       "      <td>965600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20175965700</td>\n",
+       "      <td>965700</td>\n",
+       "      <td>965700</td>\n",
+       "      <td>965700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20043020300</td>\n",
+       "      <td>020300</td>\n",
+       "      <td>020300</td>\n",
+       "      <td>20300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20043020200</td>\n",
+       "      <td>020200</td>\n",
+       "      <td>020200</td>\n",
+       "      <td>20200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT TRACTCE10 tract_code  tract_code_num\n",
+       "0   20071958100    958100     958100          958100\n",
+       "1   20175965600    965600     965600          965600\n",
+       "2   20175965700    965700     965700          965700\n",
+       "3   20043020300    020300     020300           20300\n",
+       "4   20043020200    020200     020200           20200"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert tract codes to ints to check ranges\n",
+    "tract_explore['tract_code_num'] = tract_explore['tract_code'].apply(lambda x: int(x))\n",
+    "tract_explore[['GEOID10_TRACT', 'TRACTCE10', 'tract_code', 'tract_code_num']].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2e1814b",
+   "metadata": {},
+   "source": [
+    "Per Census documentation:\n",
+    "\n",
+    "- 000100 to 989900—Basic number range for census tracts\n",
+    "- 990000 to 990099—Basic number for census tracts in water areas\n",
+    "- 990100 to 998900—Basic number range for census tracts\n",
+    "\n",
+    "source: https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2017/TGRSHP2017_TechDoc_Ch3.pdf  (page 3-26)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cf09b1b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>TRACTCE10</th>\n",
+       "      <th>tract_code</th>\n",
+       "      <th>tract_code_num</th>\n",
+       "      <th>tract_in_water_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>20071958100</td>\n",
+       "      <td>958100</td>\n",
+       "      <td>958100</td>\n",
+       "      <td>958100</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20175965600</td>\n",
+       "      <td>965600</td>\n",
+       "      <td>965600</td>\n",
+       "      <td>965600</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20175965700</td>\n",
+       "      <td>965700</td>\n",
+       "      <td>965700</td>\n",
+       "      <td>965700</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20043020300</td>\n",
+       "      <td>020300</td>\n",
+       "      <td>020300</td>\n",
+       "      <td>20300</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20043020200</td>\n",
+       "      <td>020200</td>\n",
+       "      <td>020200</td>\n",
+       "      <td>20200</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT TRACTCE10 tract_code  tract_code_num  tract_in_water_range\n",
+       "0   20071958100    958100     958100          958100                 False\n",
+       "1   20175965600    965600     965600          965600                 False\n",
+       "2   20175965700    965700     965700          965700                 False\n",
+       "3   20043020300    020300     020300           20300                 False\n",
+       "4   20043020200    020200     020200           20200                 False"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def in_water_range(x):\n",
+    "    if x >= 990000 and x <= 990099:\n",
+    "        return True\n",
+    "    return False\n",
+    "\n",
+    "tract_explore['tract_in_water_range'] = tract_explore['tract_code_num'].apply(in_water_range)\n",
+    "tract_explore[['GEOID10_TRACT', 'TRACTCE10', 'tract_code', 'tract_code_num', 'tract_in_water_range']].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1c0fab10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>TRACTCE10</th>\n",
+       "      <th>tract_code</th>\n",
+       "      <th>tract_code_num</th>\n",
+       "      <th>tract_in_water_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>808</th>\n",
+       "      <td>36063990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>36085008900</td>\n",
+       "      <td>008900</td>\n",
+       "      <td>008900</td>\n",
+       "      <td>8900</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1149</th>\n",
+       "      <td>36085990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1219</th>\n",
+       "      <td>36013990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1226</th>\n",
+       "      <td>36075990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73379</th>\n",
+       "      <td>23015990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73399</th>\n",
+       "      <td>23031990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73509</th>\n",
+       "      <td>23023990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73527</th>\n",
+       "      <td>23013990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73629</th>\n",
+       "      <td>23005990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>367 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      GEOID10_TRACT TRACTCE10 tract_code  tract_code_num  tract_in_water_range\n",
+       "808     36063990000    990000     990000          990000                  True\n",
+       "1015    36085008900    008900     008900            8900                 False\n",
+       "1149    36085990100    990100     990100          990100                 False\n",
+       "1219    36013990000    990000     990000          990000                  True\n",
+       "1226    36075990000    990000     990000          990000                  True\n",
+       "...             ...       ...        ...             ...                   ...\n",
+       "73379   23015990000    990000     990000          990000                  True\n",
+       "73399   23031990100    990100     990100          990100                 False\n",
+       "73509   23023990000    990000     990000          990000                  True\n",
+       "73527   23013990000    990000     990000          990000                  True\n",
+       "73629   23005990000    990000     990000          990000                  True\n",
+       "\n",
+       "[367 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# look at water-only tracts\n",
+    "tract_explore[tract_explore.water_only]\\\n",
+    "    [['GEOID10_TRACT', 'TRACTCE10', 'tract_code', 'tract_code_num', 'tract_in_water_range']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ba84ff5",
+   "metadata": {},
+   "source": [
+    "Some of the tracts with ALAND10=0 have tract numbers not in the water-only range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "320bd044",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">tract_in_water_range</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>water_only</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>73767</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>367</td>\n",
+       "      <td>227</td>\n",
+       "      <td>0.618529</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           GEOID10_TRACT tract_in_water_range          \n",
+       "                   count                  sum      mean\n",
+       "water_only                                             \n",
+       "False              73767                    0  0.000000\n",
+       "True                 367                  227  0.618529"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tract_explore.groupby('water_only').agg({'GEOID10_TRACT': 'count', \n",
+    "                                         'tract_in_water_range': ['sum', 'mean']})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "612c72b8",
+   "metadata": {},
+   "source": [
+    "There are no tracts with land area that have tract codes in the water-only range. But about 38% of tracts with 0 land area have normal tract codes outside the water range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e0d5fb77",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STATEFP10</th>\n",
+       "      <th>COUNTYFP10</th>\n",
+       "      <th>TRACTCE10</th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>NAME10</th>\n",
+       "      <th>NAMELSAD10</th>\n",
+       "      <th>MTFCC10</th>\n",
+       "      <th>FUNCSTAT10</th>\n",
+       "      <th>ALAND10</th>\n",
+       "      <th>AWATER10</th>\n",
+       "      <th>INTPTLAT10</th>\n",
+       "      <th>INTPTLON10</th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>water_only</th>\n",
+       "      <th>tract_code</th>\n",
+       "      <th>tract_code_num</th>\n",
+       "      <th>tract_in_water_range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>36</td>\n",
+       "      <td>085</td>\n",
+       "      <td>008900</td>\n",
+       "      <td>36085008900</td>\n",
+       "      <td>89</td>\n",
+       "      <td>Census Tract 89</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>100710</td>\n",
+       "      <td>+40.6469814</td>\n",
+       "      <td>-074.1024106</td>\n",
+       "      <td>POLYGON ((-74.10504 40.64808, -74.10025 40.648...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>008900</td>\n",
+       "      <td>8900</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1149</th>\n",
+       "      <td>36</td>\n",
+       "      <td>085</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>36085990100</td>\n",
+       "      <td>9901</td>\n",
+       "      <td>Census Tract 9901</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80255151</td>\n",
+       "      <td>+40.5255512</td>\n",
+       "      <td>-074.1085829</td>\n",
+       "      <td>POLYGON ((-74.25482 40.49390, -74.25308 40.491...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2840</th>\n",
+       "      <td>36</td>\n",
+       "      <td>047</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>36047990100</td>\n",
+       "      <td>9901</td>\n",
+       "      <td>Census Tract 9901</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17793514</td>\n",
+       "      <td>+40.5649933</td>\n",
+       "      <td>-074.0148865</td>\n",
+       "      <td>POLYGON ((-74.00620 40.59437, -74.00619 40.594...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3095</th>\n",
+       "      <td>36</td>\n",
+       "      <td>059</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>36059990100</td>\n",
+       "      <td>9901</td>\n",
+       "      <td>Census Tract 9901</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13672168</td>\n",
+       "      <td>+40.8790568</td>\n",
+       "      <td>-073.7020503</td>\n",
+       "      <td>POLYGON ((-73.76050 40.84184, -73.76102 40.841...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3096</th>\n",
+       "      <td>36</td>\n",
+       "      <td>059</td>\n",
+       "      <td>990200</td>\n",
+       "      <td>36059990200</td>\n",
+       "      <td>9902</td>\n",
+       "      <td>Census Tract 9902</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27086403</td>\n",
+       "      <td>+40.9078457</td>\n",
+       "      <td>-073.6526612</td>\n",
+       "      <td>POLYGON ((-73.65151 40.88524, -73.66481 40.875...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>990200</td>\n",
+       "      <td>990200</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68946</th>\n",
+       "      <td>15</td>\n",
+       "      <td>009</td>\n",
+       "      <td>990200</td>\n",
+       "      <td>15009990200</td>\n",
+       "      <td>9902</td>\n",
+       "      <td>Census Tract 9902</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1643042883</td>\n",
+       "      <td>+20.6128423</td>\n",
+       "      <td>-156.5486800</td>\n",
+       "      <td>POLYGON ((-156.70541 20.82632, -156.70658 20.8...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>990200</td>\n",
+       "      <td>990200</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>68949</th>\n",
+       "      <td>15</td>\n",
+       "      <td>009</td>\n",
+       "      <td>991200</td>\n",
+       "      <td>15009991200</td>\n",
+       "      <td>9912</td>\n",
+       "      <td>Census Tract 9912</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>482573725</td>\n",
+       "      <td>+20.9476085</td>\n",
+       "      <td>-156.9795251</td>\n",
+       "      <td>POLYGON ((-157.11121 20.87500, -157.11725 20.8...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>991200</td>\n",
+       "      <td>991200</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70339</th>\n",
+       "      <td>39</td>\n",
+       "      <td>093</td>\n",
+       "      <td>990200</td>\n",
+       "      <td>39093990200</td>\n",
+       "      <td>9902</td>\n",
+       "      <td>Census Tract 9902</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1094173093</td>\n",
+       "      <td>+41.6347746</td>\n",
+       "      <td>-082.1526215</td>\n",
+       "      <td>POLYGON ((-82.16712 41.47534, -82.16943 41.474...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>990200</td>\n",
+       "      <td>990200</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70979</th>\n",
+       "      <td>39</td>\n",
+       "      <td>043</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>39043990100</td>\n",
+       "      <td>9901</td>\n",
+       "      <td>Census Tract 9901</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>853315204</td>\n",
+       "      <td>+41.5559059</td>\n",
+       "      <td>-082.5241364</td>\n",
+       "      <td>POLYGON ((-82.51199 41.39475, -82.51472 41.395...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73399</th>\n",
+       "      <td>23</td>\n",
+       "      <td>031</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>23031990100</td>\n",
+       "      <td>9901</td>\n",
+       "      <td>Census Tract 9901</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>567248819</td>\n",
+       "      <td>+43.1483531</td>\n",
+       "      <td>-070.4998364</td>\n",
+       "      <td>POLYGON ((-70.22209 43.46688, -70.22432 43.464...</td>\n",
+       "      <td>True</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>990100</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>140 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      STATEFP10 COUNTYFP10 TRACTCE10 GEOID10_TRACT NAME10         NAMELSAD10  \\\n",
+       "1015         36        085    008900   36085008900     89    Census Tract 89   \n",
+       "1149         36        085    990100   36085990100   9901  Census Tract 9901   \n",
+       "2840         36        047    990100   36047990100   9901  Census Tract 9901   \n",
+       "3095         36        059    990100   36059990100   9901  Census Tract 9901   \n",
+       "3096         36        059    990200   36059990200   9902  Census Tract 9902   \n",
+       "...         ...        ...       ...           ...    ...                ...   \n",
+       "68946        15        009    990200   15009990200   9902  Census Tract 9902   \n",
+       "68949        15        009    991200   15009991200   9912  Census Tract 9912   \n",
+       "70339        39        093    990200   39093990200   9902  Census Tract 9902   \n",
+       "70979        39        043    990100   39043990100   9901  Census Tract 9901   \n",
+       "73399        23        031    990100   23031990100   9901  Census Tract 9901   \n",
+       "\n",
+       "      MTFCC10 FUNCSTAT10  ALAND10    AWATER10   INTPTLAT10    INTPTLON10  \\\n",
+       "1015    G5020          S        0      100710  +40.6469814  -074.1024106   \n",
+       "1149    G5020          S        0    80255151  +40.5255512  -074.1085829   \n",
+       "2840    G5020          S        0    17793514  +40.5649933  -074.0148865   \n",
+       "3095    G5020          S        0    13672168  +40.8790568  -073.7020503   \n",
+       "3096    G5020          S        0    27086403  +40.9078457  -073.6526612   \n",
+       "...       ...        ...      ...         ...          ...           ...   \n",
+       "68946   G5020          S        0  1643042883  +20.6128423  -156.5486800   \n",
+       "68949   G5020          S        0   482573725  +20.9476085  -156.9795251   \n",
+       "70339   G5020          S        0  1094173093  +41.6347746  -082.1526215   \n",
+       "70979   G5020          S        0   853315204  +41.5559059  -082.5241364   \n",
+       "73399   G5020          S        0   567248819  +43.1483531  -070.4998364   \n",
+       "\n",
+       "                                                geometry  water_only  \\\n",
+       "1015   POLYGON ((-74.10504 40.64808, -74.10025 40.648...        True   \n",
+       "1149   POLYGON ((-74.25482 40.49390, -74.25308 40.491...        True   \n",
+       "2840   POLYGON ((-74.00620 40.59437, -74.00619 40.594...        True   \n",
+       "3095   POLYGON ((-73.76050 40.84184, -73.76102 40.841...        True   \n",
+       "3096   POLYGON ((-73.65151 40.88524, -73.66481 40.875...        True   \n",
+       "...                                                  ...         ...   \n",
+       "68946  POLYGON ((-156.70541 20.82632, -156.70658 20.8...        True   \n",
+       "68949  POLYGON ((-157.11121 20.87500, -157.11725 20.8...        True   \n",
+       "70339  POLYGON ((-82.16712 41.47534, -82.16943 41.474...        True   \n",
+       "70979  POLYGON ((-82.51199 41.39475, -82.51472 41.395...        True   \n",
+       "73399  POLYGON ((-70.22209 43.46688, -70.22432 43.464...        True   \n",
+       "\n",
+       "      tract_code  tract_code_num  tract_in_water_range  \n",
+       "1015      008900            8900                 False  \n",
+       "1149      990100          990100                 False  \n",
+       "2840      990100          990100                 False  \n",
+       "3095      990100          990100                 False  \n",
+       "3096      990200          990200                 False  \n",
+       "...          ...             ...                   ...  \n",
+       "68946     990200          990200                 False  \n",
+       "68949     991200          991200                 False  \n",
+       "70339     990200          990200                 False  \n",
+       "70979     990100          990100                 False  \n",
+       "73399     990100          990100                 False  \n",
+       "\n",
+       "[140 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# view the exceptions (no land area, but tract code not in water range)\n",
+    "water_exceptions = tract_explore[(tract_explore.water_only==True) & (tract_explore.tract_in_water_range==False)]\n",
+    "water_exceptions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b36fdc0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "36063990000, 36013990000, 36075990000, 36055990000, 36045990001, 36029990000, 36073990000, 41007990000, 41039990000, 41019990000, 01003990000, 01097990000, 17031990000, 17097990000, 10001990000, 10005990000, 06083990000, 06053990000, 06061990000, 06015990000, 06079990000, 06013990000, 06017990000, 06001990000, 51710990000, 26157990000, 26011990000, 26001990000, 26141990000, 26019990000, 26069990000, 26053990000, 26147990000, 26153990000, 26063990000, 26159990000, 26033990000, 26005990000, 26103990000, 26061990000, 26003990000, 26095990000, 26041990000, 26031990000, 26009990000, 26139990000, 26047990000, 26109990000, 26007990000, 26021990000, 26089990000, 26017990000, 26115990000, 26151990000, 26055990000, 26127990000, 26097990000, 26121990000, 26105990000, 26101990000, 26029990000, 66010990000, 27031990000, 69100990000, 69110990000, 69120990000, 28045990000, 28059990000, 28047990000, 53031990000, 53061990002, 53027990000, 12009990000, 12017990000, 12037990000, 12011990000, 12087990000, 12081990000, 12093990000, 12029990000, 12031990000, 12071990000, 12065990000, 12033990000, 12045990000, 12086990000, 12129990000, 12103990000, 12057990000, 12115990000, 12101990000, 12123990000, 12085990000, 12051990000, 12005990000, 12035990000, 12053990000, 12043990000, 12131990000, 12099990000, 12075990000, 12015990000, 12061990000, 12095990000, 12021990000, 12113990000, 12089990000, 12111990000, 12127990000, 32031990000, 32005990000, 32510990000, 24029990000, 24017990000, 24047990000, 24041990000, 24037990000, 24035990000, 24019990000, 24003990000, 48039990000, 48489990000, 48261990000, 48355990000, 48273990000, 48321990000, 48167990000, 48061990000, 48071990000, 48007990000, 48245990000, 48057990000, 09001990000, 09009990000, 25001990000, 25023990003, 25005990000, 25007990000, 25019990000, 72059990001, 72027990000, 72005990000, 72091990025, 72037990000, 72095990000, 72145990000, 72147990000, 72111990000, 72097990000, 72031990000, 72115990000, 72133990000, 72089990001, 72011990000, 72137990000, 72065990016, 72071990000, 72103990013, 72087990000, 72151990000, 72003990000, 72017990000, 72075990001, 72023990000, 72143990000, 72055990000, 72051990021, 72109990000, 72127990000, 33015990000, 44005990000, 13039990000, 13127990000, 13051990000, 13179990000, 13191990000, 34001990000, 34029990000, 34033990000, 34011990000, 34025990000, 22071990000, 22087990000, 22109990000, 22045990000, 22023990000, 22101990000, 22113990000, 22057990000, 22075990000, 22103990000, 22051990000, 18091990000, 18089990000, 18127990000, 55031990000, 55061990000, 55051990000, 55083990000, 55059990000, 55101990000, 55007990000, 55075990000, 55003990000, 55029990000, 55079990000, 55071990000, 55117990000, 55089990000, 42049990000, 15003990001, 15001990000, 15005990000, 15009990000, 78010990000, 78020990000, 78030990000, 39007990000, 39085990000, 39095990000, 39035990000, 23029990000, 23009990000, 23015990000, 23023990000, 23013990000, 23005990000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print list of \"normal\" water-only IDs to put into compare tool\n",
+    "water_normals = tract_explore[(tract_explore.water_only==True) & (tract_explore.tract_in_water_range==True)]\n",
+    "print(', '.join(water_normals.GEOID10_TRACT.values))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e9380f21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "36063990000, 36085008900, 36085990100, 36013990000, 36075990000, 36055990000, 36045990001, 36047990100, 36059990100, 36059990200, 36059990301, 36059990302, 36059990400, 36081990100, 36029990000, 36073990000, 36103990100, 36117990100, 36011990200, 41007990000, 41057990100, 41039990000, 41041990100, 41015990101, 41011990101, 41019990000, 01003990000, 01097990000, 17031990000, 17097990000, 37031990100, 37031990200, 37053990100, 37019990100, 37055990100, 37055990200, 37129990100, 37095990100, 37095990200, 37141990100, 37137990100, 37133990100, 10001990000, 10005990000, 10003990100, 06083990000, 06053990000, 06061990000, 06059990100, 06097990100, 06045990100, 06073990100, 06041990100, 06015990000, 06081990100, 06079990000, 06013990000, 06111990100, 06017990000, 06001990000, 06075990100, 06087990100, 06023990100, 06037990200, 06037990300, 06037990100, 51103990100, 51115990100, 51650990100, 51131990100, 51735990100, 51810990100, 51133990100, 51001990100, 51001990200, 51119990100, 51199990100, 51710990000, 26157990000, 26011990000, 26163990100, 26163990200, 26001990000, 26141990000, 26011990100, 26019990000, 26069990000, 26053990000, 26147990000, 26153990000, 26063990000, 26159990000, 26033990000, 26033990100, 26131990100, 26005990000, 26103990000, 26099990100, 26061990000, 26061990100, 26003990000, 26095990000, 26041990000, 26031990000, 26009990000, 26083990100, 26139990000, 26041990100, 26047990000, 26109990000, 26007990000, 26021990000, 26089990000, 26017990000, 26115990000, 26151990000, 26013990100, 26055990000, 26127990000, 26097990000, 26121990000, 26105990000, 26101990000, 26029990000, 66010990000, 27137990100, 27031990000, 27075990100, 69100990000, 69110990000, 69120990000, 28045990000, 28059990000, 28047990000, 53061990100, 53035990100, 53057990100, 53009990100, 53067990100, 53049990100, 53031990000, 53033990100, 53061990002, 53029992201, 53027990000, 53055990100, 12009990000, 12017990000, 12037990000, 12037990100, 12011990000, 12087990000, 12081990000, 12093990000, 12029990000, 12031990000, 12071990000, 12065990000, 12033990000, 12045990000, 12103990100, 12086990000, 12129990000, 12103990000, 12057990000, 12057990100, 12115990000, 12101990000, 12109990200, 12109990100, 12123990000, 12085990000, 12085990100, 12051990000, 12005990000, 12035990000, 12053990000, 12043990000, 12131990000, 12099990000, 12075990000, 12015990000, 12099990100, 12061990000, 12095990000, 12021990000, 12113990000, 12089990000, 12111990000, 12127990000, 12091990200, 12091990100, 45013990100, 45053990100, 45019990100, 45029990100, 45043990100, 45051990100, 32031990000, 32031990100, 32005990000, 32510990000, 24029990000, 24017990000, 24047990000, 24041990000, 24039990100, 24037990000, 24035990200, 24035990100, 24035990000, 24019990000, 24003990000, 24009990100, 48039990000, 48489990000, 48261990000, 48355990000, 48273990000, 48321990000, 48167990000, 48061990000, 48071990000, 48007990000, 48245990000, 48057990000, 09001990000, 09011990100, 09007990100, 09009990000, 25001990000, 25023990003, 25005990000, 25025990101, 25009990100, 25007990000, 25019990000, 72049990501, 72059990001, 72027990000, 72033990201, 72053990103, 72113993000, 72015991500, 72005990000, 72091990025, 72013992900, 72037990000, 72095990000, 72145990000, 72117990400, 72147990000, 72111990000, 72097990000, 72079991100, 72031990000, 72115990000, 72133990000, 72089990001, 72011990000, 72123992800, 72137990000, 72065990016, 72071990000, 72103990013, 72087990000, 72069991800, 72151990000, 72003990000, 72057992600, 72017990000, 72075990001, 72119992700, 72023990000, 72143990000, 72055990000, 72051990021, 72109990000, 72127990000, 33015990000, 44009990200, 44009990100, 44005990000, 13039990000, 13127990000, 13051990000, 13179990000, 13191990000, 34001990000, 34029990000, 34033990000, 34011990000, 34009990100, 34025990000, 22071990000, 22087990000, 22109990000, 22045990000, 22023990000, 22101990000, 22113990000, 22057990000, 22051990100, 22075990000, 22103990000, 22051990000, 18091990000, 18089990000, 18127990000, 55031990000, 55061990000, 55051990000, 55083990000, 55059990000, 55101990000, 55007990000, 55025991703, 55025991702, 55075990000, 55003990000, 55029990000, 55079990000, 55071990000, 55117990000, 55089990000, 42049990000, 15003990001, 15001991700, 15001990400, 15001990000, 15001990300, 15001991100, 15001991200, 15001991500, 15001991300, 15001991400, 15001991600, 15001991000, 15001990100, 15001990500, 15001990600, 15001990900, 15001990800, 15001990700, 15005990000, 15007990100, 15007990300, 15007990200, 15009990200, 15009991200, 15009990000, 78010990000, 78020990000, 78030990000, 39007990000, 39085990000, 39093990200, 39043990100, 39095990000, 39035990000, 23029990000, 23009990000, 23015990000, 23031990100, 23023990000, 23013990000, 23005990000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print list of ALAND10=0 IDs to put into compare tool\n",
+    "print(', '.join(tract_explore[tract_explore.water_only==True].GEOID10_TRACT.values))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d82e594",
+   "metadata": {},
+   "source": [
+    "Tract comparison tool shoes that 21 of the 140 \"water exception\" tracts are in Hawaii. California next highest with 13. Puerto Rico has 12. (No other territories have \"water exception\" tracts.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74544236",
+   "metadata": {},
+   "source": [
+    "## Simulate calculate_tract_adjacency_scores()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3703ffb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_tract_adjacency_scores(\n",
+    "    df: pd.DataFrame, score_column: str, tract_data\n",
+    ") -> pd.DataFrame:\n",
+    "    \"\"\"Calculate the mean score of each tract in df based on its neighbors\n",
+    "\n",
+    "    Args:\n",
+    "        df (pandas.DataFrame): A dataframe with at least the following columns:\n",
+    "          * field_names.GEOID_TRACT_FIELD\n",
+    "          * score_column\n",
+    "\n",
+    "        score_column (str): The name of the column that contains the scores\n",
+    "                            to average\n",
+    "                            \n",
+    "        tract_data (GeoDataFrame): tract data normally loaded in first line of \n",
+    "                                    function: tract_data = get_tract_geojson()\n",
+    "    Returns :\n",
+    "        tuple containing final returned df in actual function, as well as intermediates:\n",
+    "            - returned_donut_bools (pandas.DataFrame): A dataframe with two columns:\n",
+    "              * field_names.GEOID_TRACT_FIELD\n",
+    "              * {score_column}_ADJACENT_MEAN, which is the average of score_column for\n",
+    "                each tract that touches the tract identified\n",
+    "                in field_names.GEOID_TRACT_FIELD\n",
+    "              NB: this is the df that gets returned in the actual function\n",
+    "            - df (pandas.DataFrame): input df after merging with Census data\n",
+    "            - adjacent_tracts (pandas.DataFrame): adjacency df\n",
+    "    \"\"\"\n",
+    "\n",
+    "    df: gpd.GeoDataFrame = tract_data.merge(\n",
+    "        df, on=GEOID_TRACT_FIELD\n",
+    "    )\n",
+    "    df = df.rename(columns={GEOID_TRACT_FIELD: ORIGINAL_TRACT})\n",
+    "\n",
+    "\n",
+    "    adjacent_tracts: gpd.GeoDataFrame = df.sjoin(\n",
+    "        tract_data, predicate=\"touches\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    returned_donut_bools = (\n",
+    "        adjacent_tracts.groupby(GEOID_TRACT_FIELD)[[score_column]]\n",
+    "        .mean()\n",
+    "        .reset_index()\n",
+    "        .rename(\n",
+    "            columns={\n",
+    "                score_column: f\"{score_column}{ADJACENCY_INDEX_SUFFIX}\",\n",
+    "            }\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "    return (returned_donut_bools, df, adjacent_tracts)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d4f50253",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['48061010100',\n",
+       " '48489950700',\n",
+       " '48489990000',\n",
+       " '48061012700',\n",
+       " '48061012304',\n",
+       " '48061012301',\n",
+       " '48061990000']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# save tract ids that are adjacent to cameron so we can simulate \n",
+    "# passing them into calculate_tract_adjacency_scores() via df arg\n",
+    "adjacent_track_id_list_cam = list(cam_adjacent_tracts.GEOID10_TRACT.unique())\n",
+    "adjacent_track_id_list_cam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "4bac089c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>Definition N (communities)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT  Definition N (communities)\n",
+       "0   48061012305                       False\n",
+       "1   48061010100                        True\n",
+       "2   48489950700                        True\n",
+       "3   48489990000                       False\n",
+       "4   48061012700                        True\n",
+       "5   48061012304                        True\n",
+       "6   48061012301                        True\n",
+       "7   48061990000                       False"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# df is passed into the function, must contain the score column we're looking at\n",
+    "# (in this case, SCORE_N_COMMUNITIES)\n",
+    "# here we pass in our water boundary issue example in Cameron TX pluss all its adjacencies.\n",
+    "# initialize disadvantaged bool as FALSE for our example, and TRUE for others.\n",
+    "df_cam_plus = pd.DataFrame({GEOID_TRACT_FIELD:[CAM_ID]+adjacent_track_id_list_cam, \n",
+    "                   SCORE_N_COMMUNITIES:[False]+([True]*len(adjacent_track_id_list_cam))})\n",
+    "\n",
+    "# set disadvantaged bool to False for water tracts\n",
+    "df_cam_plus.loc[df_cam_plus[GEOID_TRACT_FIELD].isin(['48489990000', '48061990000']), SCORE_N_COMMUNITIES]=False\n",
+    "\n",
+    "df_cam_plus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad9570b0",
+   "metadata": {},
+   "source": [
+    "### Simulate v1 run of of calculate_tract_adjacency_scores()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "74826fc9",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "returned_donut_bools__v1, df_census__v1, adjacent_tracts__v1 = calculate_tract_adjacency_scores(\n",
+    "    df=df_cam_plus, score_column=SCORE_N_COMMUNITIES, tract_data=tract_data\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "8e69447f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>STATEFP10_left</th>\n",
+       "      <th>COUNTYFP10_left</th>\n",
+       "      <th>TRACTCE10_left</th>\n",
+       "      <th>ORIGINAL_TRACT</th>\n",
+       "      <th>NAME10_left</th>\n",
+       "      <th>NAMELSAD10_left</th>\n",
+       "      <th>MTFCC10_left</th>\n",
+       "      <th>FUNCSTAT10_left</th>\n",
+       "      <th>ALAND10_left</th>\n",
+       "      <th>AWATER10_left</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TRACTCE10_right</th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>NAME10_right</th>\n",
+       "      <th>NAMELSAD10_right</th>\n",
+       "      <th>MTFCC10_right</th>\n",
+       "      <th>FUNCSTAT10_right</th>\n",
+       "      <th>ALAND10_right</th>\n",
+       "      <th>AWATER10_right</th>\n",
+       "      <th>INTPTLAT10_right</th>\n",
+       "      <th>INTPTLON10_right</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>+26.2732070</td>\n",
+       "      <td>-097.2763703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>+26.2732070</td>\n",
+       "      <td>-097.2763703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>268272237</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>+26.2732070</td>\n",
+       "      <td>-097.2763703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012304</td>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>123.04</td>\n",
+       "      <td>Census Tract 123.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5761299</td>\n",
+       "      <td>6286773</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>+26.2732070</td>\n",
+       "      <td>-097.2763703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>+26.2732070</td>\n",
+       "      <td>-097.2763703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>+26.2732070</td>\n",
+       "      <td>-097.2763703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012301</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>123.01</td>\n",
+       "      <td>Census Tract 123.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>137622651</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>+26.2732070</td>\n",
+       "      <td>-097.2763703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>+26.5154317</td>\n",
+       "      <td>-097.5790835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>+26.5154317</td>\n",
+       "      <td>-097.5790835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>+26.5154317</td>\n",
+       "      <td>-097.5790835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48261990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>394659578</td>\n",
+       "      <td>+26.9389899</td>\n",
+       "      <td>-097.3234546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48261990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>394659578</td>\n",
+       "      <td>+26.9389899</td>\n",
+       "      <td>-097.3234546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950100</td>\n",
+       "      <td>48261950100</td>\n",
+       "      <td>9501</td>\n",
+       "      <td>Census Tract 9501</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3777053964</td>\n",
+       "      <td>867877948</td>\n",
+       "      <td>+26.9241932</td>\n",
+       "      <td>-097.6694694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950100</td>\n",
+       "      <td>48261950100</td>\n",
+       "      <td>9501</td>\n",
+       "      <td>Census Tract 9501</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3777053964</td>\n",
+       "      <td>867877948</td>\n",
+       "      <td>+26.9241932</td>\n",
+       "      <td>-097.6694694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>268272237</td>\n",
+       "      <td>+26.1902408</td>\n",
+       "      <td>-097.1473235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>268272237</td>\n",
+       "      <td>+26.1902408</td>\n",
+       "      <td>-097.1473235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>268272237</td>\n",
+       "      <td>+26.1902408</td>\n",
+       "      <td>-097.1473235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>010201</td>\n",
+       "      <td>48061010201</td>\n",
+       "      <td>102.01</td>\n",
+       "      <td>Census Tract 102.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>138787038</td>\n",
+       "      <td>773532</td>\n",
+       "      <td>+26.2865300</td>\n",
+       "      <td>-097.6765589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>010201</td>\n",
+       "      <td>48061010201</td>\n",
+       "      <td>102.01</td>\n",
+       "      <td>Census Tract 102.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>138787038</td>\n",
+       "      <td>773532</td>\n",
+       "      <td>+26.2865300</td>\n",
+       "      <td>-097.6765589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>+26.2713946</td>\n",
+       "      <td>-097.4414470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>+26.2713946</td>\n",
+       "      <td>-097.4414470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012301</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>123.01</td>\n",
+       "      <td>Census Tract 123.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>137622651</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>...</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>+26.2713946</td>\n",
+       "      <td>-097.4414470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950600</td>\n",
+       "      <td>48489950600</td>\n",
+       "      <td>9506</td>\n",
+       "      <td>Census Tract 9506</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>142823217</td>\n",
+       "      <td>867417</td>\n",
+       "      <td>+26.3413027</td>\n",
+       "      <td>-097.6980863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950600</td>\n",
+       "      <td>48489950600</td>\n",
+       "      <td>9506</td>\n",
+       "      <td>Census Tract 9506</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>142823217</td>\n",
+       "      <td>867417</td>\n",
+       "      <td>+26.3413027</td>\n",
+       "      <td>-097.6980863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950500</td>\n",
+       "      <td>48489950500</td>\n",
+       "      <td>9505</td>\n",
+       "      <td>Census Tract 9505</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>198663041</td>\n",
+       "      <td>1916760</td>\n",
+       "      <td>+26.4046271</td>\n",
+       "      <td>-097.7275193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950400</td>\n",
+       "      <td>48489950400</td>\n",
+       "      <td>9504</td>\n",
+       "      <td>Census Tract 9504</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>95793488</td>\n",
+       "      <td>440761</td>\n",
+       "      <td>+26.4857271</td>\n",
+       "      <td>-097.7274534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>+26.5064260</td>\n",
+       "      <td>-097.2240134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>268272237</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>+26.5064260</td>\n",
+       "      <td>-097.2240134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121414926</td>\n",
+       "      <td>+26.5064260</td>\n",
+       "      <td>-097.2240134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>024302</td>\n",
+       "      <td>48215024302</td>\n",
+       "      <td>243.02</td>\n",
+       "      <td>Census Tract 243.02</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>152233383</td>\n",
+       "      <td>10002679</td>\n",
+       "      <td>+26.4228640</td>\n",
+       "      <td>-097.9710985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>950300</td>\n",
+       "      <td>48489950300</td>\n",
+       "      <td>9503</td>\n",
+       "      <td>Census Tract 9503</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>66833755</td>\n",
+       "      <td>75864</td>\n",
+       "      <td>+26.4851686</td>\n",
+       "      <td>-097.8236397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48</td>\n",
+       "      <td>489</td>\n",
+       "      <td>950700</td>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>9507</td>\n",
+       "      <td>Census Tract 9507</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1025415174</td>\n",
+       "      <td>377000048</td>\n",
+       "      <td>...</td>\n",
+       "      <td>024301</td>\n",
+       "      <td>48215024301</td>\n",
+       "      <td>243.01</td>\n",
+       "      <td>Census Tract 243.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1426811393</td>\n",
+       "      <td>527958</td>\n",
+       "      <td>+26.6175624</td>\n",
+       "      <td>-098.1948738</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>990000</td>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>9900</td>\n",
+       "      <td>Census Tract 9900</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>268272237</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>+25.9786218</td>\n",
+       "      <td>-097.2580863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>+25.9786218</td>\n",
+       "      <td>-097.2580863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012304</td>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>123.04</td>\n",
+       "      <td>Census Tract 123.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5761299</td>\n",
+       "      <td>6286773</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>+25.9786218</td>\n",
+       "      <td>-097.2580863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012304</td>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>123.04</td>\n",
+       "      <td>Census Tract 123.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5761299</td>\n",
+       "      <td>6286773</td>\n",
+       "      <td>+26.0631407</td>\n",
+       "      <td>-097.2184868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012304</td>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>123.04</td>\n",
+       "      <td>Census Tract 123.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5761299</td>\n",
+       "      <td>6286773</td>\n",
+       "      <td>+26.0631407</td>\n",
+       "      <td>-097.2184868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012301</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>123.01</td>\n",
+       "      <td>Census Tract 123.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>137622651</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012304</td>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>123.04</td>\n",
+       "      <td>Census Tract 123.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5761299</td>\n",
+       "      <td>6286773</td>\n",
+       "      <td>+26.0631407</td>\n",
+       "      <td>-097.2184868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012305</td>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>123.05</td>\n",
+       "      <td>Census Tract 123.05</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>25920881</td>\n",
+       "      <td>324996211</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012301</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>123.01</td>\n",
+       "      <td>Census Tract 123.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>137622651</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>+26.1581170</td>\n",
+       "      <td>-097.3180348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012304</td>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>123.04</td>\n",
+       "      <td>Census Tract 123.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5761299</td>\n",
+       "      <td>6286773</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012301</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>123.01</td>\n",
+       "      <td>Census Tract 123.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>137622651</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>+26.1581170</td>\n",
+       "      <td>-097.3180348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012301</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>123.01</td>\n",
+       "      <td>Census Tract 123.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>137622651</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>+26.1581170</td>\n",
+       "      <td>-097.3180348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012304</td>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>123.04</td>\n",
+       "      <td>Census Tract 123.04</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>5761299</td>\n",
+       "      <td>6286773</td>\n",
+       "      <td>...</td>\n",
+       "      <td>014200</td>\n",
+       "      <td>48061014200</td>\n",
+       "      <td>142</td>\n",
+       "      <td>Census Tract 142</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>229485446</td>\n",
+       "      <td>52743725</td>\n",
+       "      <td>+26.0321232</td>\n",
+       "      <td>-097.3592760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>014200</td>\n",
+       "      <td>48061014200</td>\n",
+       "      <td>142</td>\n",
+       "      <td>Census Tract 142</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>229485446</td>\n",
+       "      <td>52743725</td>\n",
+       "      <td>+26.0321232</td>\n",
+       "      <td>-097.3592760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012301</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>123.01</td>\n",
+       "      <td>Census Tract 123.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>137622651</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>...</td>\n",
+       "      <td>014200</td>\n",
+       "      <td>48061014200</td>\n",
+       "      <td>142</td>\n",
+       "      <td>Census Tract 142</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>229485446</td>\n",
+       "      <td>52743725</td>\n",
+       "      <td>+26.0321232</td>\n",
+       "      <td>-097.3592760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>010800</td>\n",
+       "      <td>48061010800</td>\n",
+       "      <td>108</td>\n",
+       "      <td>Census Tract 108</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>24214554</td>\n",
+       "      <td>372874</td>\n",
+       "      <td>+26.2018913</td>\n",
+       "      <td>-097.6365382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>010203</td>\n",
+       "      <td>48061010203</td>\n",
+       "      <td>102.03</td>\n",
+       "      <td>Census Tract 102.03</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>36355923</td>\n",
+       "      <td>529676</td>\n",
+       "      <td>+26.2392001</td>\n",
+       "      <td>-097.7004164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012200</td>\n",
+       "      <td>48061012200</td>\n",
+       "      <td>122</td>\n",
+       "      <td>Census Tract 122</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>181087087</td>\n",
+       "      <td>9690866</td>\n",
+       "      <td>+26.1431884</td>\n",
+       "      <td>-097.4883500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012301</td>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>123.01</td>\n",
+       "      <td>Census Tract 123.01</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>137622651</td>\n",
+       "      <td>119430273</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012200</td>\n",
+       "      <td>48061012200</td>\n",
+       "      <td>122</td>\n",
+       "      <td>Census Tract 122</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>181087087</td>\n",
+       "      <td>9690866</td>\n",
+       "      <td>+26.1431884</td>\n",
+       "      <td>-097.4883500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>010100</td>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>101</td>\n",
+       "      <td>Census Tract 101</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>365458278</td>\n",
+       "      <td>113041841</td>\n",
+       "      <td>...</td>\n",
+       "      <td>011400</td>\n",
+       "      <td>48061011400</td>\n",
+       "      <td>114</td>\n",
+       "      <td>Census Tract 114</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>52595069</td>\n",
+       "      <td>1716432</td>\n",
+       "      <td>+26.1576649</td>\n",
+       "      <td>-097.6030070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>014100</td>\n",
+       "      <td>48061014100</td>\n",
+       "      <td>141</td>\n",
+       "      <td>Census Tract 141</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>58750542</td>\n",
+       "      <td>1786306</td>\n",
+       "      <td>+25.8820153</td>\n",
+       "      <td>-097.4007135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>013207</td>\n",
+       "      <td>48061013207</td>\n",
+       "      <td>132.07</td>\n",
+       "      <td>Census Tract 132.07</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3884436</td>\n",
+       "      <td>409892</td>\n",
+       "      <td>+25.9278897</td>\n",
+       "      <td>-097.4159201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>013203</td>\n",
+       "      <td>48061013203</td>\n",
+       "      <td>132.03</td>\n",
+       "      <td>Census Tract 132.03</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>1103957</td>\n",
+       "      <td>3034</td>\n",
+       "      <td>+25.9335830</td>\n",
+       "      <td>-097.4379216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48</td>\n",
+       "      <td>061</td>\n",
+       "      <td>012700</td>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>127</td>\n",
+       "      <td>Census Tract 127</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>129622799</td>\n",
+       "      <td>67725054</td>\n",
+       "      <td>...</td>\n",
+       "      <td>012607</td>\n",
+       "      <td>48061012607</td>\n",
+       "      <td>126.07</td>\n",
+       "      <td>Census Tract 126.07</td>\n",
+       "      <td>G5020</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3230089</td>\n",
+       "      <td>151343</td>\n",
+       "      <td>+25.9479994</td>\n",
+       "      <td>-097.4340712</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>53 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  STATEFP10_left COUNTYFP10_left TRACTCE10_left ORIGINAL_TRACT NAME10_left  \\\n",
+       "0             48             489         990000    48489990000        9900   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "2             48             061         990000    48061990000        9900   \n",
+       "4             48             061         012304    48061012304      123.04   \n",
+       "5             48             061         010100    48061010100         101   \n",
+       "6             48             061         012700    48061012700         127   \n",
+       "7             48             061         012301    48061012301      123.01   \n",
+       "0             48             489         990000    48489990000        9900   \n",
+       "3             48             061         012305    48061012305      123.05   \n",
+       "5             48             061         010100    48061010100         101   \n",
+       "0             48             489         990000    48489990000        9900   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "0             48             489         990000    48489990000        9900   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "0             48             489         990000    48489990000        9900   \n",
+       "3             48             061         012305    48061012305      123.05   \n",
+       "6             48             061         012700    48061012700         127   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "5             48             061         010100    48061010100         101   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "3             48             061         012305    48061012305      123.05   \n",
+       "7             48             061         012301    48061012301      123.01   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "5             48             061         010100    48061010100         101   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "2             48             061         990000    48061990000        9900   \n",
+       "3             48             061         012305    48061012305      123.05   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "1             48             489         950700    48489950700        9507   \n",
+       "2             48             061         990000    48061990000        9900   \n",
+       "3             48             061         012305    48061012305      123.05   \n",
+       "4             48             061         012304    48061012304      123.04   \n",
+       "3             48             061         012305    48061012305      123.05   \n",
+       "6             48             061         012700    48061012700         127   \n",
+       "7             48             061         012301    48061012301      123.01   \n",
+       "3             48             061         012305    48061012305      123.05   \n",
+       "4             48             061         012304    48061012304      123.04   \n",
+       "5             48             061         010100    48061010100         101   \n",
+       "4             48             061         012304    48061012304      123.04   \n",
+       "6             48             061         012700    48061012700         127   \n",
+       "7             48             061         012301    48061012301      123.01   \n",
+       "5             48             061         010100    48061010100         101   \n",
+       "5             48             061         010100    48061010100         101   \n",
+       "5             48             061         010100    48061010100         101   \n",
+       "7             48             061         012301    48061012301      123.01   \n",
+       "5             48             061         010100    48061010100         101   \n",
+       "6             48             061         012700    48061012700         127   \n",
+       "6             48             061         012700    48061012700         127   \n",
+       "6             48             061         012700    48061012700         127   \n",
+       "6             48             061         012700    48061012700         127   \n",
+       "\n",
+       "       NAMELSAD10_left MTFCC10_left FUNCSTAT10_left  ALAND10_left  \\\n",
+       "0    Census Tract 9900        G5020               S             0   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "2    Census Tract 9900        G5020               S             0   \n",
+       "4  Census Tract 123.04        G5020               S       5761299   \n",
+       "5     Census Tract 101        G5020               S     365458278   \n",
+       "6     Census Tract 127        G5020               S     129622799   \n",
+       "7  Census Tract 123.01        G5020               S     137622651   \n",
+       "0    Census Tract 9900        G5020               S             0   \n",
+       "3  Census Tract 123.05        G5020               S      25920881   \n",
+       "5     Census Tract 101        G5020               S     365458278   \n",
+       "0    Census Tract 9900        G5020               S             0   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "0    Census Tract 9900        G5020               S             0   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "0    Census Tract 9900        G5020               S             0   \n",
+       "3  Census Tract 123.05        G5020               S      25920881   \n",
+       "6     Census Tract 127        G5020               S     129622799   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "5     Census Tract 101        G5020               S     365458278   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "3  Census Tract 123.05        G5020               S      25920881   \n",
+       "7  Census Tract 123.01        G5020               S     137622651   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "5     Census Tract 101        G5020               S     365458278   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "2    Census Tract 9900        G5020               S             0   \n",
+       "3  Census Tract 123.05        G5020               S      25920881   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "1    Census Tract 9507        G5020               S    1025415174   \n",
+       "2    Census Tract 9900        G5020               S             0   \n",
+       "3  Census Tract 123.05        G5020               S      25920881   \n",
+       "4  Census Tract 123.04        G5020               S       5761299   \n",
+       "3  Census Tract 123.05        G5020               S      25920881   \n",
+       "6     Census Tract 127        G5020               S     129622799   \n",
+       "7  Census Tract 123.01        G5020               S     137622651   \n",
+       "3  Census Tract 123.05        G5020               S      25920881   \n",
+       "4  Census Tract 123.04        G5020               S       5761299   \n",
+       "5     Census Tract 101        G5020               S     365458278   \n",
+       "4  Census Tract 123.04        G5020               S       5761299   \n",
+       "6     Census Tract 127        G5020               S     129622799   \n",
+       "7  Census Tract 123.01        G5020               S     137622651   \n",
+       "5     Census Tract 101        G5020               S     365458278   \n",
+       "5     Census Tract 101        G5020               S     365458278   \n",
+       "5     Census Tract 101        G5020               S     365458278   \n",
+       "7  Census Tract 123.01        G5020               S     137622651   \n",
+       "5     Census Tract 101        G5020               S     365458278   \n",
+       "6     Census Tract 127        G5020               S     129622799   \n",
+       "6     Census Tract 127        G5020               S     129622799   \n",
+       "6     Census Tract 127        G5020               S     129622799   \n",
+       "6     Census Tract 127        G5020               S     129622799   \n",
+       "\n",
+       "   AWATER10_left  ... TRACTCE10_right GEOID10_TRACT NAME10_right  \\\n",
+       "0      121414926  ...          012305   48061012305       123.05   \n",
+       "1      377000048  ...          012305   48061012305       123.05   \n",
+       "2      268272237  ...          012305   48061012305       123.05   \n",
+       "4        6286773  ...          012305   48061012305       123.05   \n",
+       "5      113041841  ...          012305   48061012305       123.05   \n",
+       "6       67725054  ...          012305   48061012305       123.05   \n",
+       "7      119430273  ...          012305   48061012305       123.05   \n",
+       "0      121414926  ...          950700   48489950700         9507   \n",
+       "3      324996211  ...          950700   48489950700         9507   \n",
+       "5      113041841  ...          950700   48489950700         9507   \n",
+       "0      121414926  ...          990000   48261990000         9900   \n",
+       "1      377000048  ...          990000   48261990000         9900   \n",
+       "0      121414926  ...          950100   48261950100         9501   \n",
+       "1      377000048  ...          950100   48261950100         9501   \n",
+       "0      121414926  ...          990000   48061990000         9900   \n",
+       "3      324996211  ...          990000   48061990000         9900   \n",
+       "6       67725054  ...          990000   48061990000         9900   \n",
+       "1      377000048  ...          010201   48061010201       102.01   \n",
+       "5      113041841  ...          010201   48061010201       102.01   \n",
+       "1      377000048  ...          010100   48061010100          101   \n",
+       "3      324996211  ...          010100   48061010100          101   \n",
+       "7      119430273  ...          010100   48061010100          101   \n",
+       "1      377000048  ...          950600   48489950600         9506   \n",
+       "5      113041841  ...          950600   48489950600         9506   \n",
+       "1      377000048  ...          950500   48489950500         9505   \n",
+       "1      377000048  ...          950400   48489950400         9504   \n",
+       "1      377000048  ...          990000   48489990000         9900   \n",
+       "2      268272237  ...          990000   48489990000         9900   \n",
+       "3      324996211  ...          990000   48489990000         9900   \n",
+       "1      377000048  ...          024302   48215024302       243.02   \n",
+       "1      377000048  ...          950300   48489950300         9503   \n",
+       "1      377000048  ...          024301   48215024301       243.01   \n",
+       "2      268272237  ...          012700   48061012700          127   \n",
+       "3      324996211  ...          012700   48061012700          127   \n",
+       "4        6286773  ...          012700   48061012700          127   \n",
+       "3      324996211  ...          012304   48061012304       123.04   \n",
+       "6       67725054  ...          012304   48061012304       123.04   \n",
+       "7      119430273  ...          012304   48061012304       123.04   \n",
+       "3      324996211  ...          012301   48061012301       123.01   \n",
+       "4        6286773  ...          012301   48061012301       123.01   \n",
+       "5      113041841  ...          012301   48061012301       123.01   \n",
+       "4        6286773  ...          014200   48061014200          142   \n",
+       "6       67725054  ...          014200   48061014200          142   \n",
+       "7      119430273  ...          014200   48061014200          142   \n",
+       "5      113041841  ...          010800   48061010800          108   \n",
+       "5      113041841  ...          010203   48061010203       102.03   \n",
+       "5      113041841  ...          012200   48061012200          122   \n",
+       "7      119430273  ...          012200   48061012200          122   \n",
+       "5      113041841  ...          011400   48061011400          114   \n",
+       "6       67725054  ...          014100   48061014100          141   \n",
+       "6       67725054  ...          013207   48061013207       132.07   \n",
+       "6       67725054  ...          013203   48061013203       132.03   \n",
+       "6       67725054  ...          012607   48061012607       126.07   \n",
+       "\n",
+       "      NAMELSAD10_right  MTFCC10_right FUNCSTAT10_right ALAND10_right  \\\n",
+       "0  Census Tract 123.05          G5020                S      25920881   \n",
+       "1  Census Tract 123.05          G5020                S      25920881   \n",
+       "2  Census Tract 123.05          G5020                S      25920881   \n",
+       "4  Census Tract 123.05          G5020                S      25920881   \n",
+       "5  Census Tract 123.05          G5020                S      25920881   \n",
+       "6  Census Tract 123.05          G5020                S      25920881   \n",
+       "7  Census Tract 123.05          G5020                S      25920881   \n",
+       "0    Census Tract 9507          G5020                S    1025415174   \n",
+       "3    Census Tract 9507          G5020                S    1025415174   \n",
+       "5    Census Tract 9507          G5020                S    1025415174   \n",
+       "0    Census Tract 9900          G5020                S             0   \n",
+       "1    Census Tract 9900          G5020                S             0   \n",
+       "0    Census Tract 9501          G5020                S    3777053964   \n",
+       "1    Census Tract 9501          G5020                S    3777053964   \n",
+       "0    Census Tract 9900          G5020                S             0   \n",
+       "3    Census Tract 9900          G5020                S             0   \n",
+       "6    Census Tract 9900          G5020                S             0   \n",
+       "1  Census Tract 102.01          G5020                S     138787038   \n",
+       "5  Census Tract 102.01          G5020                S     138787038   \n",
+       "1     Census Tract 101          G5020                S     365458278   \n",
+       "3     Census Tract 101          G5020                S     365458278   \n",
+       "7     Census Tract 101          G5020                S     365458278   \n",
+       "1    Census Tract 9506          G5020                S     142823217   \n",
+       "5    Census Tract 9506          G5020                S     142823217   \n",
+       "1    Census Tract 9505          G5020                S     198663041   \n",
+       "1    Census Tract 9504          G5020                S      95793488   \n",
+       "1    Census Tract 9900          G5020                S             0   \n",
+       "2    Census Tract 9900          G5020                S             0   \n",
+       "3    Census Tract 9900          G5020                S             0   \n",
+       "1  Census Tract 243.02          G5020                S     152233383   \n",
+       "1    Census Tract 9503          G5020                S      66833755   \n",
+       "1  Census Tract 243.01          G5020                S    1426811393   \n",
+       "2     Census Tract 127          G5020                S     129622799   \n",
+       "3     Census Tract 127          G5020                S     129622799   \n",
+       "4     Census Tract 127          G5020                S     129622799   \n",
+       "3  Census Tract 123.04          G5020                S       5761299   \n",
+       "6  Census Tract 123.04          G5020                S       5761299   \n",
+       "7  Census Tract 123.04          G5020                S       5761299   \n",
+       "3  Census Tract 123.01          G5020                S     137622651   \n",
+       "4  Census Tract 123.01          G5020                S     137622651   \n",
+       "5  Census Tract 123.01          G5020                S     137622651   \n",
+       "4     Census Tract 142          G5020                S     229485446   \n",
+       "6     Census Tract 142          G5020                S     229485446   \n",
+       "7     Census Tract 142          G5020                S     229485446   \n",
+       "5     Census Tract 108          G5020                S      24214554   \n",
+       "5  Census Tract 102.03          G5020                S      36355923   \n",
+       "5     Census Tract 122          G5020                S     181087087   \n",
+       "7     Census Tract 122          G5020                S     181087087   \n",
+       "5     Census Tract 114          G5020                S      52595069   \n",
+       "6     Census Tract 141          G5020                S      58750542   \n",
+       "6  Census Tract 132.07          G5020                S       3884436   \n",
+       "6  Census Tract 132.03          G5020                S       1103957   \n",
+       "6  Census Tract 126.07          G5020                S       3230089   \n",
+       "\n",
+       "  AWATER10_right INTPTLAT10_right INTPTLON10_right  \n",
+       "0      324996211      +26.2732070     -097.2763703  \n",
+       "1      324996211      +26.2732070     -097.2763703  \n",
+       "2      324996211      +26.2732070     -097.2763703  \n",
+       "4      324996211      +26.2732070     -097.2763703  \n",
+       "5      324996211      +26.2732070     -097.2763703  \n",
+       "6      324996211      +26.2732070     -097.2763703  \n",
+       "7      324996211      +26.2732070     -097.2763703  \n",
+       "0      377000048      +26.5154317     -097.5790835  \n",
+       "3      377000048      +26.5154317     -097.5790835  \n",
+       "5      377000048      +26.5154317     -097.5790835  \n",
+       "0      394659578      +26.9389899     -097.3234546  \n",
+       "1      394659578      +26.9389899     -097.3234546  \n",
+       "0      867877948      +26.9241932     -097.6694694  \n",
+       "1      867877948      +26.9241932     -097.6694694  \n",
+       "0      268272237      +26.1902408     -097.1473235  \n",
+       "3      268272237      +26.1902408     -097.1473235  \n",
+       "6      268272237      +26.1902408     -097.1473235  \n",
+       "1         773532      +26.2865300     -097.6765589  \n",
+       "5         773532      +26.2865300     -097.6765589  \n",
+       "1      113041841      +26.2713946     -097.4414470  \n",
+       "3      113041841      +26.2713946     -097.4414470  \n",
+       "7      113041841      +26.2713946     -097.4414470  \n",
+       "1         867417      +26.3413027     -097.6980863  \n",
+       "5         867417      +26.3413027     -097.6980863  \n",
+       "1        1916760      +26.4046271     -097.7275193  \n",
+       "1         440761      +26.4857271     -097.7274534  \n",
+       "1      121414926      +26.5064260     -097.2240134  \n",
+       "2      121414926      +26.5064260     -097.2240134  \n",
+       "3      121414926      +26.5064260     -097.2240134  \n",
+       "1       10002679      +26.4228640     -097.9710985  \n",
+       "1          75864      +26.4851686     -097.8236397  \n",
+       "1         527958      +26.6175624     -098.1948738  \n",
+       "2       67725054      +25.9786218     -097.2580863  \n",
+       "3       67725054      +25.9786218     -097.2580863  \n",
+       "4       67725054      +25.9786218     -097.2580863  \n",
+       "3        6286773      +26.0631407     -097.2184868  \n",
+       "6        6286773      +26.0631407     -097.2184868  \n",
+       "7        6286773      +26.0631407     -097.2184868  \n",
+       "3      119430273      +26.1581170     -097.3180348  \n",
+       "4      119430273      +26.1581170     -097.3180348  \n",
+       "5      119430273      +26.1581170     -097.3180348  \n",
+       "4       52743725      +26.0321232     -097.3592760  \n",
+       "6       52743725      +26.0321232     -097.3592760  \n",
+       "7       52743725      +26.0321232     -097.3592760  \n",
+       "5         372874      +26.2018913     -097.6365382  \n",
+       "5         529676      +26.2392001     -097.7004164  \n",
+       "5        9690866      +26.1431884     -097.4883500  \n",
+       "7        9690866      +26.1431884     -097.4883500  \n",
+       "5        1716432      +26.1576649     -097.6030070  \n",
+       "6        1786306      +25.8820153     -097.4007135  \n",
+       "6         409892      +25.9278897     -097.4159201  \n",
+       "6           3034      +25.9335830     -097.4379216  \n",
+       "6         151343      +25.9479994     -097.4340712  \n",
+       "\n",
+       "[53 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adjacent_tracts__v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f3033c5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### check for duplicates\n",
+    "assert len(adjacent_tracts__v1[['ORIGINAL_TRACT', 'GEOID10_TRACT']].drop_duplicates()) == len(adjacent_tracts__v1[['ORIGINAL_TRACT', 'GEOID10_TRACT']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "4b1770de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>Definition N (communities) (average of neighbors)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>48061010100</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>48061010201</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>48061010203</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>48061010800</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48061011400</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>48061012200</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>48061012301</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>48061012304</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>0.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>48061012607</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>48061012700</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>48061013203</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>48061013207</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>48061014100</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>48061014200</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>48061990000</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>48215024301</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>48215024302</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>48261950100</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>48261990000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>48489950300</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>48489950400</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>48489950500</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>48489950600</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>48489950700</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>48489990000</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   GEOID10_TRACT  Definition N (communities) (average of neighbors)\n",
+       "0    48061010100                                           0.666667\n",
+       "1    48061010201                                           1.000000\n",
+       "2    48061010203                                           1.000000\n",
+       "3    48061010800                                           1.000000\n",
+       "4    48061011400                                           1.000000\n",
+       "5    48061012200                                           1.000000\n",
+       "6    48061012301                                           0.666667\n",
+       "7    48061012304                                           0.666667\n",
+       "8    48061012305                                           0.714286\n",
+       "9    48061012607                                           1.000000\n",
+       "10   48061012700                                           0.333333\n",
+       "11   48061013203                                           1.000000\n",
+       "12   48061013207                                           1.000000\n",
+       "13   48061014100                                           1.000000\n",
+       "14   48061014200                                           1.000000\n",
+       "15   48061990000                                           0.333333\n",
+       "16   48215024301                                           1.000000\n",
+       "17   48215024302                                           1.000000\n",
+       "18   48261950100                                           0.500000\n",
+       "19   48261990000                                           0.500000\n",
+       "20   48489950300                                           1.000000\n",
+       "21   48489950400                                           1.000000\n",
+       "22   48489950500                                           1.000000\n",
+       "23   48489950600                                           1.000000\n",
+       "24   48489950700                                           0.333333\n",
+       "25   48489990000                                           0.333333"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "returned_donut_bools__v1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "c2aa4682",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>Definition N (communities) (average of neighbors)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>0.714286</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT  Definition N (communities) (average of neighbors)\n",
+       "8   48061012305                                           0.714286"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check result for our example tract\n",
+    "returned_donut_bools__v1[returned_donut_bools__v1.GEOID10_TRACT==CAM_ID]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "936d0ebc",
+   "metadata": {},
+   "source": [
+    "Demonstrates that because 2 of the 7 adjacent tracts have meaningless water-only non-disadvantaged status, the average of neighbors is less than 1. This is why our example tract isn't being classified as disadvantaged in v1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a02b6194",
+   "metadata": {},
+   "source": [
+    "### Simulate proposed v2 implementations of calculate_tract_adjacency_scores()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5688387a",
+   "metadata": {},
+   "source": [
+    "Testing three ways to do this: filtering tract_data, filtering df, and filtering both. For the first two ways, we'll test both land area and tract ID range methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "72963a39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def view_results(returned_donut_bools, post_merge_df, adjacent_tracts):\n",
+    "    # function to print what we need to see after each experiment\n",
+    "    \n",
+    "    print(f'Length of input df after Census merge: {len(post_merge_df)}') \n",
+    "    print(f'That is {len(df_census__v1) - len(post_merge_df)} less than in v1')\n",
+    "    \n",
+    "    print(f'\\nLength of adjacency frame: {len(adjacent_tracts)}')\n",
+    "    print(f'That is {len(adjacent_tracts__v1) - len(adjacent_tracts)} less than in v1')\n",
+    "    \n",
+    "    n_dupes = len(adjacent_tracts[['ORIGINAL_TRACT', 'GEOID10_TRACT']])\\\n",
+    "                - len(adjacent_tracts[['ORIGINAL_TRACT', 'GEOID10_TRACT']].drop_duplicates())\n",
+    "    if n_dupes>0:\n",
+    "        print(\"ALERT: duplicates present in adjacency frame!\")\n",
+    "\n",
+    "    \n",
+    "    print(f'\\nLength of returned frame with donut bools: {len(returned_donut_bools)}')\n",
+    "    if len(returned_donut_bools__v1) == len(returned_donut_bools):\n",
+    "        print('Returning same number of final bools as in v1')\n",
+    "    else:\n",
+    "        print(f'ALERT: returning {len(returned_donut_bools__v1) - len(returned_donut_bools)} less than in v1')\n",
+    "\n",
+    "    cam_return = returned_donut_bools[returned_donut_bools.GEOID10_TRACT==CAM_ID]\n",
+    "    display(cam_return)\n",
+    "\n",
+    "    if cam_return['Definition N (communities) (average of neighbors)'].values[0]==1:\n",
+    "        print('SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS')\n",
+    "    else:\n",
+    "        print('ALERT: CAMERON, TX EXAMPLE IS STILL BEING MARKED NON-DISADVANTAGED')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1712378",
+   "metadata": {},
+   "source": [
+    "#### Implementation 1a: Filter water-only tracts out of tract_data using ALAND10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9e0401e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length of input df after Census merge: 6\n",
+      "That is 2 less than in v1\n",
+      "\n",
+      "Length of adjacency frame: 40\n",
+      "That is 13 less than in v1\n",
+      "\n",
+      "Length of returned frame with donut bools: 23\n",
+      "ALERT: returning 3 less than in v1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>Definition N (communities) (average of neighbors)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT  Definition N (communities) (average of neighbors)\n",
+       "8   48061012305                                                1.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n"
+     ]
+    }
+   ],
+   "source": [
+    "tract_data_land_area_only = tract_data.copy()\n",
+    "tract_data_land_area_only = tract_data_land_area_only[tract_data_land_area_only.ALAND10>0]\n",
+    "\n",
+    "returned_donut_bools__filter_tracts_aland10,\\\n",
+    "df_census__filter_tracts_aland10,\\\n",
+    "adjacent_tracts__filter_tracts_aland10 =calculate_tract_adjacency_scores(\n",
+    "        df=df_cam_plus, \n",
+    "        score_column=SCORE_N_COMMUNITIES, \n",
+    "        tract_data=tract_data_land_area_only\n",
+    ")\n",
+    "\n",
+    "view_results(returned_donut_bools=returned_donut_bools__filter_tracts_aland10, \n",
+    "             post_merge_df=df_census__filter_tracts_aland10, \n",
+    "             adjacent_tracts=adjacent_tracts__filter_tracts_aland10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f277cc9a",
+   "metadata": {},
+   "source": [
+    "#### Implementation 1b: Filter water-only tracts out of df using tract range"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "0ea48370",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def full_geo_id_to_water_range_bool(x:str):\n",
+    "    num_x = int(x[-6:])\n",
+    "    return(in_water_range(num_x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "a2ef8844",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length of input df after Census merge: 6\n",
+      "That is 2 less than in v1\n",
+      "\n",
+      "Length of adjacency frame: 40\n",
+      "That is 13 less than in v1\n",
+      "\n",
+      "Length of returned frame with donut bools: 23\n",
+      "ALERT: returning 3 less than in v1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>Definition N (communities) (average of neighbors)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT  Definition N (communities) (average of neighbors)\n",
+       "8   48061012305                                                1.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n"
+     ]
+    }
+   ],
+   "source": [
+    "tract_data_non_water_range_only = tract_data.copy()\n",
+    "tract_data_non_water_range_only = tract_data_non_water_range_only[tract_data_non_water_range_only.GEOID10_TRACT\\\n",
+    "                                            .apply(full_geo_id_to_water_range_bool)\\\n",
+    "                                                ==False]\n",
+    "\n",
+    "returned_donut_bools__filter_tracts_id_range,\\\n",
+    "df_census__filter_tracts_id_range,\\\n",
+    "adjacent_tracts__filter_tracts_id_range =calculate_tract_adjacency_scores(\n",
+    "        df=df_cam_plus, \n",
+    "        score_column=SCORE_N_COMMUNITIES, \n",
+    "        tract_data=tract_data_non_water_range_only\n",
+    ")\n",
+    "\n",
+    "view_results(returned_donut_bools=returned_donut_bools__filter_tracts_id_range, \n",
+    "             post_merge_df=df_census__filter_tracts_id_range, \n",
+    "             adjacent_tracts=adjacent_tracts__filter_tracts_id_range)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1fcee2e2",
+   "metadata": {},
+   "source": [
+    "#### Implementation 2a: Filter water-only tracts out of df using ALAND10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "477a886c",
+   "metadata": {},
+   "source": [
+    "Note: we can't filter the input df based on ALAND10 using the standard calculate_tract_adjacency_scores() function, because it doesn't have the ALAND10 column until after it is merged with tract data. Need to write a new test function for this method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "9ad212d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_tract_adjacency_scores__filter_df_aland10(\n",
+    "    df: pd.DataFrame, score_column: str, tract_data\n",
+    ") -> pd.DataFrame:\n",
+    "    \"\"\"Calculate the mean score of each tract in df based on its neighbors\n",
+    "\n",
+    "    Args:\n",
+    "        df (pandas.DataFrame): A dataframe with at least the following columns:\n",
+    "          * field_names.GEOID_TRACT_FIELD\n",
+    "          * score_column\n",
+    "\n",
+    "        score_column (str): The name of the column that contains the scores\n",
+    "                            to average\n",
+    "                            \n",
+    "        tract_data (GeoDataFrame): tract data normally loaded in first line of \n",
+    "                                    function: tract_data = get_tract_geojson()\n",
+    "    Returns :\n",
+    "        tuple containing final returned df in actual function, as well as intermediates:\n",
+    "            - returned_donut_bools (pandas.DataFrame): A dataframe with two columns:\n",
+    "              * field_names.GEOID_TRACT_FIELD\n",
+    "              * {score_column}_ADJACENT_MEAN, which is the average of score_column for\n",
+    "                each tract that touches the tract identified\n",
+    "                in field_names.GEOID_TRACT_FIELD\n",
+    "              NB: this is the df that gets returned in the actual function\n",
+    "            - df (pandas.DataFrame): input df after merging with Census data\n",
+    "            - adjacent_tracts (pandas.DataFrame): adjacency df\n",
+    "    \"\"\"\n",
+    "\n",
+    "    df: gpd.GeoDataFrame = tract_data.merge(\n",
+    "        df, on=GEOID_TRACT_FIELD\n",
+    "    )\n",
+    "    df = df.rename(columns={GEOID_TRACT_FIELD: ORIGINAL_TRACT})\n",
+    "    \n",
+    "    \n",
+    "    # remove water areas from input frame\n",
+    "    df = df[df['ALAND10']>0]\n",
+    "\n",
+    "\n",
+    "    adjacent_tracts: gpd.GeoDataFrame = df.sjoin(\n",
+    "        tract_data, predicate=\"touches\"\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "    returned_donut_bools = (\n",
+    "        adjacent_tracts.groupby(GEOID_TRACT_FIELD)[[score_column]]\n",
+    "        .mean()\n",
+    "        .reset_index()\n",
+    "        .rename(\n",
+    "            columns={\n",
+    "                score_column: f\"{score_column}{ADJACENCY_INDEX_SUFFIX}\",\n",
+    "            }\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "    return (returned_donut_bools, df, adjacent_tracts)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "ff9d13cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length of input df after Census merge: 6\n",
+      "That is 2 less than in v1\n",
+      "\n",
+      "Length of adjacency frame: 45\n",
+      "That is 8 less than in v1\n",
+      "\n",
+      "Length of returned frame with donut bools: 26\n",
+      "Returning same number of final bools as in v1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>Definition N (communities) (average of neighbors)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT  Definition N (communities) (average of neighbors)\n",
+       "8   48061012305                                                1.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n"
+     ]
+    }
+   ],
+   "source": [
+    "returned_donut_bools__filter_df_aland10,\\\n",
+    "df_census__filter_df_aland10,\\\n",
+    "adjacent_tracts__filter_df_aland10 = calculate_tract_adjacency_scores__filter_df_aland10(\n",
+    "        df=df_cam_plus, \n",
+    "        score_column=SCORE_N_COMMUNITIES, \n",
+    "        tract_data=tract_data\n",
+    ")\n",
+    "\n",
+    "view_results(returned_donut_bools=returned_donut_bools__filter_df_aland10, \n",
+    "             post_merge_df=df_census__filter_df_aland10, \n",
+    "             adjacent_tracts=adjacent_tracts__filter_df_aland10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7401f01",
+   "metadata": {},
+   "source": [
+    "#### Implementation 2b: Filter water-only tracts out of df using tract range"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "3cb027ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length of input df after Census merge: 6\n",
+      "That is 2 less than in v1\n",
+      "\n",
+      "Length of adjacency frame: 45\n",
+      "That is 8 less than in v1\n",
+      "\n",
+      "Length of returned frame with donut bools: 26\n",
+      "Returning same number of final bools as in v1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>Definition N (communities) (average of neighbors)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT  Definition N (communities) (average of neighbors)\n",
+       "8   48061012305                                                1.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_cam_plus_no_water = df_cam_plus.copy()\n",
+    "df_cam_plus_no_water = df_cam_plus_no_water[df_cam_plus_no_water.GEOID10_TRACT\\\n",
+    "                                            .apply(full_geo_id_to_water_range_bool)\\\n",
+    "                                                ==False]\n",
+    "\n",
+    "returned_donut_bools__filter_df_id_range,\\\n",
+    "df_census__filter_df_id_range,\\\n",
+    "adjacent_tracts__filter_df_id_range = calculate_tract_adjacency_scores(\n",
+    "        df=df_cam_plus_no_water, \n",
+    "        score_column=SCORE_N_COMMUNITIES, \n",
+    "        tract_data=tract_data\n",
+    ")\n",
+    "\n",
+    "view_results(returned_donut_bools=returned_donut_bools__filter_df_id_range, \n",
+    "             post_merge_df=df_census__filter_df_id_range, \n",
+    "             adjacent_tracts=adjacent_tracts__filter_df_id_range)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0f94a03",
+   "metadata": {},
+   "source": [
+    "#### Implementation 3: Filter water-only tracts out of both tract_data and df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e96c981f",
+   "metadata": {},
+   "source": [
+    "Note: test id method for simplicity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "8e385e22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Length of input df after Census merge: 6\n",
+      "That is 2 less than in v1\n",
+      "\n",
+      "Length of adjacency frame: 40\n",
+      "That is 13 less than in v1\n",
+      "\n",
+      "Length of returned frame with donut bools: 23\n",
+      "ALERT: returning 3 less than in v1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>Definition N (communities) (average of neighbors)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  GEOID10_TRACT  Definition N (communities) (average of neighbors)\n",
+       "8   48061012305                                                1.0"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SUCCESSFULLY RE-ASSIGNING CAMERON, TX EXAMPLE TO DISADVANTAGED STATUS\n"
+     ]
+    }
+   ],
+   "source": [
+    "returned_donut_bools__filter_both,\\\n",
+    "df_census__filter_both,\\\n",
+    "adjacent_tracts__filter_both =calculate_tract_adjacency_scores(\n",
+    "        df=df_cam_plus_no_water, \n",
+    "        score_column=SCORE_N_COMMUNITIES, \n",
+    "        tract_data=tract_data_non_water_range_only\n",
+    ")\n",
+    "\n",
+    "view_results(returned_donut_bools=returned_donut_bools__filter_both, \n",
+    "             post_merge_df=df_census__filter_both, \n",
+    "             adjacent_tracts=adjacent_tracts__filter_both)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dde21850",
+   "metadata": {},
+   "source": [
+    "#### Summary of results: All solutions successfully re-assigned our example tract to disadvantaged status. Only implementations 2a & 2b (filtering on the dataframe) also preserved the number of tracts that the function returns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04d07bdb",
+   "metadata": {},
+   "source": [
+    "## Compare full scoring runs from different methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "aa462404",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>dac_adj_avg_prod</th>\n",
+       "      <th>dac_adj_avg_local</th>\n",
+       "      <th>is_donut_hole_prod</th>\n",
+       "      <th>is_donut_hole_local</th>\n",
+       "      <th>final_dac_prod</th>\n",
+       "      <th>final_dac_local</th>\n",
+       "      <th>class_change</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>12087971001</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>370</th>\n",
+       "      <td>12101030202</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>376</th>\n",
+       "      <td>12101030303</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>419</th>\n",
+       "      <td>12127081102</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>806</th>\n",
+       "      <td>26069000200</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>812</th>\n",
+       "      <td>26083000100</td>\n",
+       "      <td>0.33</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>912</th>\n",
+       "      <td>28047001400</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1138</th>\n",
+       "      <td>39007000602</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1654</th>\n",
+       "      <td>41041950303</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1766</th>\n",
+       "      <td>51001090600</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1780</th>\n",
+       "      <td>51131930200</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1944</th>\n",
+       "      <td>55101000100</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012</th>\n",
+       "      <td>72013301000</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2037</th>\n",
+       "      <td>72037160100</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     GEOID10_TRACT dac_adj_avg_prod dac_adj_avg_local is_donut_hole_prod  \\\n",
+       "332    12087971001             0.67               1.0              False   \n",
+       "370    12101030202             0.83               1.0              False   \n",
+       "376    12101030303             0.83               1.0              False   \n",
+       "419    12127081102             0.75               1.0              False   \n",
+       "806    26069000200             0.86               1.0              False   \n",
+       "812    26083000100             0.33               1.0              False   \n",
+       "912    28047001400             0.88               1.0              False   \n",
+       "1138   39007000602             0.75               1.0              False   \n",
+       "1654   41041950303             0.75               1.0              False   \n",
+       "1739   48061012305             0.71               1.0              False   \n",
+       "1766   51001090600             0.83               1.0              False   \n",
+       "1780   51131930200             0.67               1.0              False   \n",
+       "1944   55101000100             0.75               1.0              False   \n",
+       "2012   72013301000             0.71               1.0              False   \n",
+       "2037   72037160100             0.83               1.0              False   \n",
+       "\n",
+       "     is_donut_hole_local final_dac_prod final_dac_local  class_change  \n",
+       "332                 True          False            True          True  \n",
+       "370                 True          False            True          True  \n",
+       "376                 True          False            True          True  \n",
+       "419                 True          False            True          True  \n",
+       "806                 True          False            True          True  \n",
+       "812                 True          False            True          True  \n",
+       "912                 True          False            True          True  \n",
+       "1138                True          False            True          True  \n",
+       "1654                True          False            True          True  \n",
+       "1739                True          False            True          True  \n",
+       "1766                True          False            True          True  \n",
+       "1780                True          False            True          True  \n",
+       "1944                True          False            True          True  \n",
+       "2012                True          False            True          True  \n",
+       "2037                True          False            True          True  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read in CSV with deltas from implementation 1a \n",
+    "# (water areas filtered from tract_data via ALAND10)\n",
+    "deltas__tract_area = pd.read_csv('../data/tmp/Comparator/Score/deltas__tract_area.csv')\n",
+    "\n",
+    "# rename key columns\n",
+    "deltas__tract_area.rename(columns = {'Unnamed: 0': 'GEOID10_TRACT',\n",
+    "                                'Definition N community, including adjacency index tracts': 'final_dac_prod',\n",
+    "                                'Definition N community, including adjacency index tracts.1': 'final_dac_local',\n",
+    "                                'Definition N (communities) (average of neighbors)': 'dac_adj_avg_prod',\n",
+    "                                'Definition N (communities) (average of neighbors).1': 'dac_adj_avg_local',\n",
+    "                                'Is the tract surrounded by disadvantaged communities?': 'is_donut_hole_prod',\n",
+    "                                'Is the tract surrounded by disadvantaged communities?.1': 'is_donut_hole_local'\n",
+    "                                },\n",
+    "                    inplace=True) \n",
+    "\n",
+    "# drop first two rows, which old column information\n",
+    "deltas__tract_area.drop(index=[0,1], inplace=True)\n",
+    "\n",
+    "# create bool to store whether final DAC designation was updated\n",
+    "deltas__tract_area['class_change'] = deltas__tract_area.final_dac_prod!=deltas__tract_area.final_dac_local\n",
+    "\n",
+    "# set class change bool to false where both designations are null\n",
+    "deltas__tract_area.loc[((deltas__tract_area.final_dac_prod.isna()==True)\\\n",
+    "                       & (deltas__tract_area.final_dac_local.isna()==True)), \n",
+    "                    'class_change'] = False\n",
+    "\n",
+    "# view tracts that had their status updated with this method\n",
+    "deltas__tract_area[deltas__tract_area.class_change][['GEOID10_TRACT', \n",
+    "                                                     'dac_adj_avg_prod', 'dac_adj_avg_local',\n",
+    "                                                     'is_donut_hole_prod', 'is_donut_hole_local',\n",
+    "                                                     'final_dac_prod', 'final_dac_local',\n",
+    "                                                     'class_change']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3dc9500a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>dac_adj_avg_prod</th>\n",
+       "      <th>dac_adj_avg_local</th>\n",
+       "      <th>is_donut_hole_prod</th>\n",
+       "      <th>is_donut_hole_local</th>\n",
+       "      <th>final_dac_prod</th>\n",
+       "      <th>final_dac_local</th>\n",
+       "      <th>class_change</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>312</th>\n",
+       "      <td>12087971001</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>346</th>\n",
+       "      <td>12101030202</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>12101030303</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>12127081102</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>741</th>\n",
+       "      <td>26069000200</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>747</th>\n",
+       "      <td>26083000100</td>\n",
+       "      <td>0.33</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>836</th>\n",
+       "      <td>28047001400</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1044</th>\n",
+       "      <td>39007000602</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1559</th>\n",
+       "      <td>41041950303</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1643</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1668</th>\n",
+       "      <td>51001090600</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1679</th>\n",
+       "      <td>51131930200</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1828</th>\n",
+       "      <td>55101000100</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1894</th>\n",
+       "      <td>72013301000</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1919</th>\n",
+       "      <td>72037160100</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     GEOID10_TRACT dac_adj_avg_prod dac_adj_avg_local is_donut_hole_prod  \\\n",
+       "312    12087971001             0.67               1.0              False   \n",
+       "346    12101030202             0.83               1.0              False   \n",
+       "352    12101030303             0.83               1.0              False   \n",
+       "388    12127081102             0.75               1.0              False   \n",
+       "741    26069000200             0.86               1.0              False   \n",
+       "747    26083000100             0.33               1.0              False   \n",
+       "836    28047001400             0.88               1.0              False   \n",
+       "1044   39007000602             0.75               1.0              False   \n",
+       "1559   41041950303             0.75               1.0              False   \n",
+       "1643   48061012305             0.71               1.0              False   \n",
+       "1668   51001090600             0.83               1.0              False   \n",
+       "1679   51131930200             0.67               1.0              False   \n",
+       "1828   55101000100             0.75               1.0              False   \n",
+       "1894   72013301000             0.71               1.0              False   \n",
+       "1919   72037160100             0.83               1.0              False   \n",
+       "\n",
+       "     is_donut_hole_local final_dac_prod final_dac_local  class_change  \n",
+       "312                 True          False            True          True  \n",
+       "346                 True          False            True          True  \n",
+       "352                 True          False            True          True  \n",
+       "388                 True          False            True          True  \n",
+       "741                 True          False            True          True  \n",
+       "747                 True          False            True          True  \n",
+       "836                 True          False            True          True  \n",
+       "1044                True          False            True          True  \n",
+       "1559                True          False            True          True  \n",
+       "1643                True          False            True          True  \n",
+       "1668                True          False            True          True  \n",
+       "1679                True          False            True          True  \n",
+       "1828                True          False            True          True  \n",
+       "1894                True          False            True          True  \n",
+       "1919                True          False            True          True  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read in CSV with deltas from implementation 2a \n",
+    "# (water areas filtered from input df via ALAND10)\n",
+    "deltas__df_area = pd.read_csv('../data/tmp/Comparator/Score/deltas__df_area.csv')\n",
+    "\n",
+    "# rename key columns\n",
+    "deltas__df_area.rename(columns = {'Unnamed: 0': 'GEOID10_TRACT',\n",
+    "                                'Definition N community, including adjacency index tracts': 'final_dac_prod',\n",
+    "                                'Definition N community, including adjacency index tracts.1': 'final_dac_local',\n",
+    "                                'Definition N (communities) (average of neighbors)': 'dac_adj_avg_prod',\n",
+    "                                'Definition N (communities) (average of neighbors).1': 'dac_adj_avg_local',\n",
+    "                                'Is the tract surrounded by disadvantaged communities?': 'is_donut_hole_prod',\n",
+    "                                'Is the tract surrounded by disadvantaged communities?.1': 'is_donut_hole_local'\n",
+    "                                },\n",
+    "                    inplace=True) \n",
+    "\n",
+    "# drop first two rows, which old column information\n",
+    "deltas__df_area.drop(index=[0,1], inplace=True)\n",
+    "\n",
+    "# create bool to store whether final DAC designation was updated\n",
+    "deltas__df_area['class_change'] = deltas__df_area.final_dac_prod!=deltas__df_area.final_dac_local\n",
+    "\n",
+    "# set class change bool to false where both designations are null\n",
+    "deltas__df_area.loc[((deltas__df_area.final_dac_prod.isna()==True)\\\n",
+    "                       & (deltas__df_area.final_dac_local.isna()==True)), \n",
+    "                    'class_change'] = False\n",
+    "\n",
+    "# view tracts that had their status updated with this method\n",
+    "deltas__df_area[deltas__df_area.class_change][['GEOID10_TRACT', \n",
+    "                                           'dac_adj_avg_prod', 'dac_adj_avg_local',\n",
+    "                                           'is_donut_hole_prod', 'is_donut_hole_local',\n",
+    "                                            'final_dac_prod', 'final_dac_local',\n",
+    "                                            'class_change']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "5187abcb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GEOID10_TRACT</th>\n",
+       "      <th>dac_adj_avg_prod</th>\n",
+       "      <th>dac_adj_avg_local</th>\n",
+       "      <th>is_donut_hole_prod</th>\n",
+       "      <th>is_donut_hole_local</th>\n",
+       "      <th>final_dac_prod</th>\n",
+       "      <th>final_dac_local</th>\n",
+       "      <th>class_change</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>12087971001</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>12101030202</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>12101030303</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>12127081102</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>570</th>\n",
+       "      <td>26069000200</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>648</th>\n",
+       "      <td>28047001400</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>39007000602</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1281</th>\n",
+       "      <td>48061012305</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1388</th>\n",
+       "      <td>55101000100</td>\n",
+       "      <td>0.75</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1470</th>\n",
+       "      <td>72037160100</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     GEOID10_TRACT dac_adj_avg_prod dac_adj_avg_local is_donut_hole_prod  \\\n",
+       "228    12087971001             0.67               1.0              False   \n",
+       "261    12101030202             0.83               1.0              False   \n",
+       "267    12101030303             0.83               1.0              False   \n",
+       "292    12127081102             0.75               1.0              False   \n",
+       "570    26069000200             0.86               1.0              False   \n",
+       "648    28047001400             0.88               1.0              False   \n",
+       "755    39007000602             0.75               1.0              False   \n",
+       "1281   48061012305             0.71               1.0              False   \n",
+       "1388   55101000100             0.75               1.0              False   \n",
+       "1470   72037160100             0.83               1.0              False   \n",
+       "\n",
+       "     is_donut_hole_local final_dac_prod final_dac_local  class_change  \n",
+       "228                 True          False            True          True  \n",
+       "261                 True          False            True          True  \n",
+       "267                 True          False            True          True  \n",
+       "292                 True          False            True          True  \n",
+       "570                 True          False            True          True  \n",
+       "648                 True          False            True          True  \n",
+       "755                 True          False            True          True  \n",
+       "1281                True          False            True          True  \n",
+       "1388                True          False            True          True  \n",
+       "1470                True          False            True          True  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read in CSV with deltas from implementation 2b \n",
+    "# (water areas filtered from input df via IDs)\n",
+    "deltas__df_id = pd.read_csv('../data/tmp/Comparator/Score/deltas__df_id.csv')\n",
+    "\n",
+    "# rename key columns\n",
+    "deltas__df_id.rename(columns = {'Unnamed: 0': 'GEOID10_TRACT',\n",
+    "                                'Definition N community, including adjacency index tracts': 'final_dac_prod',\n",
+    "                                'Definition N community, including adjacency index tracts.1': 'final_dac_local',\n",
+    "                                'Definition N (communities) (average of neighbors)': 'dac_adj_avg_prod',\n",
+    "                                'Definition N (communities) (average of neighbors).1': 'dac_adj_avg_local',\n",
+    "                                'Is the tract surrounded by disadvantaged communities?': 'is_donut_hole_prod',\n",
+    "                                'Is the tract surrounded by disadvantaged communities?.1': 'is_donut_hole_local'\n",
+    "                                },\n",
+    "                    inplace=True) \n",
+    "\n",
+    "# drop first two rows, which old column information\n",
+    "deltas__df_id.drop(index=[0,1], inplace=True)\n",
+    "\n",
+    "# create bool to store whether final DAC designation was updated\n",
+    "deltas__df_id['class_change'] = deltas__df_id.final_dac_prod!=deltas__df_id.final_dac_local\n",
+    "\n",
+    "# set class change bool to false where both designations are null\n",
+    "deltas__df_id.loc[((deltas__df_id.final_dac_prod.isna()==True)\\\n",
+    "                       & (deltas__df_id.final_dac_local.isna()==True)), \n",
+    "                    'class_change'] = False\n",
+    "\n",
+    "# view tracts that had their status updated with this method\n",
+    "deltas__df_id[deltas__df_id.class_change][['GEOID10_TRACT', \n",
+    "                                           'dac_adj_avg_prod', 'dac_adj_avg_local',\n",
+    "                                           'is_donut_hole_prod', 'is_donut_hole_local',\n",
+    "                                            'final_dac_prod', 'final_dac_local',\n",
+    "                                            'class_change']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "6d1c7b0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12087971001, 12101030202, 12101030303, 12127081102, 26069000200, 26083000100, 28047001400, 39007000602, 41041950303, 48061012305, 51001090600, 51131930200, 55101000100, 72013301000, 72037160100\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print list of tract IDs where status was updated by method 1a\n",
+    "updated_ids__tract_area = deltas__tract_area[deltas__tract_area.class_change]['GEOID10_TRACT'].values\n",
+    "print(', '.join(updated_ids__tract_area))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a1ea1384",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12087971001, 12101030202, 12101030303, 12127081102, 26069000200, 26083000100, 28047001400, 39007000602, 41041950303, 48061012305, 51001090600, 51131930200, 55101000100, 72013301000, 72037160100\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print list of tract IDs where status was updated by method 2a\n",
+    "updated_ids__df_area = deltas__df_area[deltas__df_area.class_change]['GEOID10_TRACT'].values\n",
+    "print(', '.join(updated_ids__df_area))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "f6516263",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12087971001, 12101030202, 12101030303, 12127081102, 26069000200, 28047001400, 39007000602, 48061012305, 55101000100, 72037160100\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print list of tract IDs where status was updated by method 2b\n",
+    "updated_ids__df_id = deltas__df_id[deltas__df_id.class_change]['GEOID10_TRACT'].values\n",
+    "print(', '.join(updated_ids__df_id))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "f540133f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many tracts updated by method 1a?\n",
+    "len(updated_ids__tract_area)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "ffb41759",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many tracts updated by method 2a?\n",
+    "len(updated_ids__df_area)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "4e5099ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many tracts updated by method 2b?\n",
+    "len(updated_ids__df_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "8534563c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many tracts are changed by both 1a and 2a?\n",
+    "len(set(updated_ids__tract_area) & set(updated_ids__df_area))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "b8270c9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many tracts are changed by all 3?\n",
+    "len(set(updated_ids__tract_area) & set(updated_ids__df_id))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6494e4c3",
+   "metadata": {},
+   "source": [
+    "Implementations 1a & 2a result in the same designations. They makes all the same changes as implementation 2b, plus 5 more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "8c80b1d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'26083000100', '41041950303', '51001090600', '51131930200', '72013301000'}"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# view the tracts that are changed with 1a/2a but not with 2b\n",
+    "\n",
+    "set(updated_ids__tract_area) - set(updated_ids__df_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "bb4a3c4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### VA:\n",
+    "# 51001090600\n",
+    "# https://screeningtool.geoplatform.gov/en/#9.26/37.7027/-75.8891\n",
+    "# 51131930200\n",
+    "# https://screeningtool.geoplatform.gov/en/#9.84/37.3186/-75.9043\n",
+    "\n",
+    "### MI: \n",
+    "# 26083000100\n",
+    "# https://screeningtool.geoplatform.gov/en/#8.87/47.3424/-88.1965\n",
+    "\n",
+    "### OR:\n",
+    "# 41041950303\n",
+    "# https://screeningtool.geoplatform.gov/en/#11.94/45.015/-123.98189\n",
+    "\n",
+    "### PR:\n",
+    "# 72013301000\n",
+    "# https://screeningtool.geoplatform.gov/en/#13.45/18.47558/-66.75585"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c3e7297",
+   "metadata": {},
+   "source": [
+    "These all look like they should be re-classified. Indicates we should not go with 2b."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "67bbb703",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'12087971001',\n",
+       " '12101030202',\n",
+       " '12101030303',\n",
+       " '12127081102',\n",
+       " '26069000200',\n",
+       " '28047001400',\n",
+       " '39007000602',\n",
+       " '48061012305',\n",
+       " '55101000100',\n",
+       " '72037160100'}"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check the rest of the changed tracts\n",
+    "set(updated_ids__tract_area) & set(updated_ids__df_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "b51cfa18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### FL:\n",
+    "# 12087971001\n",
+    "# https://screeningtool.geoplatform.gov/en/#12.3/24.73756/-81.0061\n",
+    "# 12101030202\n",
+    "# https://screeningtool.geoplatform.gov/en/#13.28/28.34631/-82.71162\n",
+    "# 12101030303\n",
+    "# https://screeningtool.geoplatform.gov/en/#12.8/28.22897/-82.75138\n",
+    "# 12127081102\n",
+    "# https://screeningtool.geoplatform.gov/en/#13.54/29.24789/-81.02269\n",
+    "\n",
+    "### MI:\n",
+    "# 26069000200\n",
+    "# https://screeningtool.geoplatform.gov/en/#9.71/44.3623/-83.5055\n",
+    "\n",
+    "### MS:\n",
+    "# 28047001400\n",
+    "# https://screeningtool.geoplatform.gov/en/#11.54/30.3705/-89.0511\n",
+    "\n",
+    "### OH:\n",
+    "# 39007000602\n",
+    "# https://screeningtool.geoplatform.gov/en/#12.33/41.89305/-80.82678\n",
+    "\n",
+    "### TX:\n",
+    "# 48061012305\n",
+    "# https://screeningtool.geoplatform.gov/en/#9.76/26.2291/-97.2455\n",
+    "\n",
+    "### WI:\n",
+    "# 55101000100\n",
+    "# https://screeningtool.geoplatform.gov/en/#13.44/42.7299/-87.77935\n",
+    "\n",
+    "### PR:\n",
+    "# 72037160100\n",
+    "# https://screeningtool.geoplatform.gov/en/#11.59/18.2405/-65.6222"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3410f026",
+   "metadata": {},
+   "source": [
+    "These all look like legitimate donut holes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "327894d4",
+   "metadata": {},
+   "source": [
+    "#### Final recommendation: Use implementation 2a (filter the input df using ALAND10). This will allow us to update the status of all 15 of the above Census tracts, and will also let the function continue to return rows for the water tracts (as we currently do in prod)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4455847a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/data-pipeline/data_pipeline/score/field_names.py b/data/data-pipeline/data_pipeline/score/field_names.py
index aed44c48..9b548f47 100644
--- a/data/data-pipeline/data_pipeline/score/field_names.py
+++ b/data/data-pipeline/data_pipeline/score/field_names.py
@@ -4,12 +4,16 @@ ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD = " for island areas"
 ADJACENT_MEAN_SUFFIX = " (based on adjacency index and low income alone)"
 ADJACENCY_INDEX_SUFFIX = " (average of neighbors)"
 ISLAND_AREA_BACKFILL_SUFFIX = " in 2009"
+V1_0_RESULTS_SUFFIX = " v1.0"
 
 # Geographic field names
 GEOID_TRACT_FIELD = "GEOID10_TRACT"
 STATE_FIELD = "State/Territory"
 COUNTY_FIELD = "County Name"
 
+# Census per-tract land area field name
+LAND_AREA_FIELD = "ALAND10"
+
 # Definition Narwhal fields
 SCORE_N_COMMUNITIES = "Definition N (communities)"
 N_CLIMATE = "Climate Factor (Definition N)"
@@ -24,6 +28,10 @@ N_NON_WORKFORCE = "Any Non-Workforce Factor (Definition N)"
 FINAL_SCORE_N_BOOLEAN = (
     "Definition N community, including adjacency index tracts"
 )
+FINAL_SCORE_N_BOOLEAN_V1_0 = f"{FINAL_SCORE_N_BOOLEAN}{V1_0_RESULTS_SUFFIX}"
+GRANDFATHERED_N_COMMUNITIES_V1_0 = (
+    f"Grandfathered {SCORE_N_COMMUNITIES} from v1.0"
+)
 
 PERCENTILE = 90
 MEDIAN_HOUSE_VALUE_PERCENTILE = 90
@@ -182,19 +190,27 @@ AGGREGATION_POPULATION_FIELD = "Population Characteristics"
 UNDER_5_FIELD = "Individuals under 5 years old"
 OVER_64_FIELD = "Individuals over 64 years old"
 
-# Fields from 2010 decennial census (generally only loaded for the territories)
-CENSUS_DECENNIAL_MEDIAN_INCOME_2009 = "Median household income in 2009 ($)"
-CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009 = (
-    "Percentage households below 100% of federal poverty line in 2009"
+# Fields from 2020 decennial census (generally only loaded for the territories)
+# 2010 vs 2020 WARNING
+# We must keep the old 2009 date to make it compatible with all the other 2010 data
+DEC_DATA_YEAR = "2009"
+CENSUS_DECENNIAL_MEDIAN_INCOME_2019 = (
+    f"Median household income in {DEC_DATA_YEAR} ($)"
 )
-CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009 = "Percent individuals age 25 or over with less than high school degree in 2009"
-CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009 = "Unemployment (percent) in 2009"
-CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2009 = "Total population in 2009"
-CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009 = (
-    "Median household income as a percent of territory median income in 2009"
+CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019 = f"Percentage households below 100% of federal poverty line in {DEC_DATA_YEAR}"
+CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019 = f"Percentage households below 200% of federal poverty line in {DEC_DATA_YEAR}"
+CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019 = f"{CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019}, adjusted and imputed"
+CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019 = f"Percent individuals age 25 or over with less than high school degree in {DEC_DATA_YEAR}"
+CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019 = (
+    f"Unemployment (percent) in {DEC_DATA_YEAR}"
 )
-LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009 = "Low median household income as a percent of territory median income in 2009"
-# Fields from 2010 ACS (loaded for comparison with the territories)
+CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019 = (
+    f"Total population in {DEC_DATA_YEAR}"
+)
+CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019 = f"Median household income as a percent of territory median income in {DEC_DATA_YEAR}"
+LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019 = f"Low median household income as a percent of territory median income in {DEC_DATA_YEAR}"
+
+# # Fields from 2010 ACS (loaded for comparison with the territories)
 CENSUS_UNEMPLOYMENT_FIELD_2010 = "Unemployment (percent) in 2010"
 CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010 = (
     "Percent of individuals less than 100% Federal Poverty Line in 2010"
@@ -226,6 +242,10 @@ COMBINED_POVERTY_LESS_THAN_100_FPL_FIELD_2010 = (
     "Percentage households below 100% of federal poverty line in 2009 (island areas) "
     "and 2010 (states and PR)"
 )
+COMBINED_POVERTY_LESS_THAN_200_FPL_FIELD_2010 = (
+    "Percentage households below 200% of federal poverty line in 2009 (island areas) "
+    "and 2010 (states and PR)"
+)
 
 # Urban Rural Map
 URBAN_HEURISTIC_FIELD = "Urban Heuristic Flag"
@@ -691,11 +711,13 @@ LOW_MEDIAN_INCOME_PCTILE_THRESHOLD = (
     f"percent of area median income"
 )
 ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD = (
-    f"{LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009} exceeds "
+    f"{LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019} exceeds "
     f"{PERCENTILE}th percentile"
 )
-ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009} exceeds {PERCENTILE}th percentile"
-ISLAND_POVERTY_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009} exceeds {PERCENTILE}th percentile"
+ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019} exceeds {PERCENTILE}th percentile"
+ISLAND_POVERTY_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019} exceeds {PERCENTILE}th percentile"
+# Low Income Island Areas
+ISLAND_AREAS_IMPUTED_INCOME_FLAG_FIELD = f"Income data has been estimated based on neighbor income{ISLAND_AREAS_SUFFIX}"
 
 # Not currently used in a factor
 EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
diff --git a/data/data-pipeline/data_pipeline/score/score_a.py b/data/data-pipeline/data_pipeline/score/score_a.py
deleted file mode 100644
index 0788f094..00000000
--- a/data/data-pipeline/data_pipeline/score/score_a.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import data_pipeline.score.field_names as field_names
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreA(Score):
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score A")
-        self.df[field_names.SCORE_A] = self.df[
-            [
-                field_names.POVERTY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.HIGH_SCHOOL_ED_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-            ]
-        ].mean(axis=1)
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_b.py b/data/data-pipeline/data_pipeline/score/score_b.py
deleted file mode 100644
index 2e980b3d..00000000
--- a/data/data-pipeline/data_pipeline/score/score_b.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import data_pipeline.score.field_names as field_names
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreB(Score):
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score B")
-        self.df[field_names.SCORE_B] = (
-            self.df[
-                field_names.POVERTY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            * self.df[
-                field_names.HIGH_SCHOOL_ED_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-        )
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_c.py b/data/data-pipeline/data_pipeline/score/score_c.py
deleted file mode 100644
index b91b7417..00000000
--- a/data/data-pipeline/data_pipeline/score/score_c.py
+++ /dev/null
@@ -1,102 +0,0 @@
-from collections import namedtuple
-
-import data_pipeline.score.field_names as field_names
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreC(Score):
-    def __init__(self, df: pd.DataFrame) -> None:
-        Bucket = namedtuple(typename="Bucket", field_names=["name", "fields"])
-
-        # Note: we use percentiles for every field below.
-        # To do so, we add the percentile suffix to all the field names.
-        self.BUCKET_SOCIOECONOMIC = Bucket(
-            field_names.C_SOCIOECONOMIC,
-            [
-                field_names.HOUSEHOLDS_LINGUISTIC_ISO_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.POVERTY_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.HIGH_SCHOOL_ED_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.UNEMPLOYMENT_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.HOUSING_BURDEN_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-            ],
-        )
-        self.BUCKET_SENSITIVE = Bucket(
-            field_names.C_SENSITIVE,
-            [
-                field_names.UNDER_5_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.OVER_64_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.LINGUISTIC_ISO_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-            ],
-        )
-        self.BUCKET_ENVIRONMENTAL = Bucket(
-            field_names.C_ENVIRONMENTAL,
-            [
-                field_names.RMP_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.TSDF_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.NPL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.WASTEWATER_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.LEAD_PAINT_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-            ],
-        )
-        self.BUCKET_EXPOSURES = Bucket(
-            field_names.C_EXPOSURES,
-            [
-                field_names.AIR_TOXICS_CANCER_RISK_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.RESPIRATORY_HAZARD_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.OZONE_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-                field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX,
-            ],
-        )
-        self.BUCKETS = [
-            self.BUCKET_SOCIOECONOMIC,
-            self.BUCKET_SENSITIVE,
-            self.BUCKET_ENVIRONMENTAL,
-            self.BUCKET_EXPOSURES,
-        ]
-        super().__init__(df)
-
-    # "CalEnviroScreen for the US" score
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score C")
-        # Average all the percentile values in each bucket into a single score for each of the four buckets.
-        for bucket in self.BUCKETS:
-            self.df[bucket.name] = self.df[bucket.fields].mean(axis=1)
-
-        # Combine the score from the two Exposures and Environmental Effects buckets
-        # into a single score called "Pollution Burden".
-        # The math for this score is:
-        # (1.0 * Exposures Score + 0.5 * Environment Effects score) / 1.5.
-        self.df[field_names.AGGREGATION_POLLUTION_FIELD] = (
-            1.0 * self.df[self.BUCKET_EXPOSURES.name]
-            + 0.5 * self.df[self.BUCKET_ENVIRONMENTAL.name]
-        ) / 1.5
-
-        # Average the score from the two Sensitive populations and
-        # Socioeconomic factors buckets into a single score called
-        # "Population Characteristics".
-        self.df[field_names.AGGREGATION_POPULATION_FIELD] = self.df[
-            [self.BUCKET_SENSITIVE.name, self.BUCKET_SOCIOECONOMIC.name]
-        ].mean(axis=1)
-
-        # Multiply the "Pollution Burden" score and the "Population Characteristics"
-        # together to produce the cumulative impact score.
-        self.df[field_names.SCORE_C] = (
-            self.df[field_names.AGGREGATION_POLLUTION_FIELD]
-            * self.df[field_names.AGGREGATION_POPULATION_FIELD]
-        )
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_d.py b/data/data-pipeline/data_pipeline/score/score_d.py
deleted file mode 100644
index 08e55f01..00000000
--- a/data/data-pipeline/data_pipeline/score/score_d.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import data_pipeline.score.field_names as field_names
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreD(Score):
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Scores D and E")
-        fields_to_use_in_score = [
-            field_names.UNEMPLOYMENT_FIELD,
-            field_names.LINGUISTIC_ISO_FIELD,
-            field_names.HOUSING_BURDEN_FIELD,
-            field_names.POVERTY_FIELD,
-            field_names.HIGH_SCHOOL_ED_FIELD,
-        ]
-
-        fields_min_max = [
-            f"{field}{field_names.MIN_MAX_FIELD_SUFFIX}"
-            for field in fields_to_use_in_score
-        ]
-        fields_percentile = [
-            f"{field}{field_names.PERCENTILE_FIELD_SUFFIX}"
-            for field in fields_to_use_in_score
-        ]
-
-        # Calculate "Score D", which uses min-max normalization
-        # and calculate "Score E", which uses percentile normalization for the same fields
-        self.df[field_names.SCORE_D] = self.df[fields_min_max].mean(axis=1)
-        self.df[field_names.SCORE_E] = self.df[fields_percentile].mean(axis=1)
-
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_f.py b/data/data-pipeline/data_pipeline/score/score_f.py
deleted file mode 100644
index be38e514..00000000
--- a/data/data-pipeline/data_pipeline/score/score_f.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import data_pipeline.score.field_names as field_names
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreF(Score):
-    # TODO Make variables and constants clearer (meaning and type)
-
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score F")
-        ami_and_high_school_field = "Low AMI, Low HS graduation"
-        meets_socio_field = "Meets socioeconomic criteria"
-        meets_burden_field = "Meets burden criteria"
-
-        self.df[ami_and_high_school_field] = (
-            self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_STATE_FIELD] < 0.80
-        ) & (self.df[field_names.HIGH_SCHOOL_ED_FIELD] > 0.2)
-
-        self.df[meets_socio_field] = (
-            self.df[ami_and_high_school_field]
-            | (self.df[field_names.POVERTY_FIELD] > 0.40)
-            | (self.df[field_names.LINGUISTIC_ISO_FIELD] > 0.10)
-            | (self.df[field_names.HIGH_SCHOOL_ED_FIELD] > 0.4)
-        )
-
-        self.df[meets_burden_field] = (
-            (
-                self.df[
-                    field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-                ]
-                > 0.9
-            )
-            | (
-                self.df[
-                    field_names.RESPIRATORY_HAZARD_FIELD
-                    + field_names.PERCENTILE_FIELD_SUFFIX
-                ]
-                > 0.9
-            )
-            | (
-                self.df[
-                    field_names.TRAFFIC_FIELD
-                    + field_names.PERCENTILE_FIELD_SUFFIX
-                ]
-                > 0.9
-            )
-            | (
-                self.df[
-                    field_names.LEAD_PAINT_FIELD
-                    + field_names.PERCENTILE_FIELD_SUFFIX
-                ]
-                > 0.9
-            )
-            | (
-                self.df[
-                    field_names.RMP_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-                ]
-                > 0.9
-            )
-            | (
-                self.df[
-                    field_names.ASTHMA_FIELD
-                    + field_names.PERCENTILE_FIELD_SUFFIX
-                ]
-                > 0.9
-            )
-            | (
-                self.df[
-                    field_names.HEART_DISEASE_FIELD
-                    + field_names.PERCENTILE_FIELD_SUFFIX
-                ]
-                > 0.9
-            )
-            | (
-                self.df[
-                    field_names.CANCER_FIELD
-                    + field_names.PERCENTILE_FIELD_SUFFIX
-                ]
-                > 0.9
-            )
-            | (
-                self.df[
-                    field_names.DIABETES_FIELD
-                    + field_names.PERCENTILE_FIELD_SUFFIX
-                ]
-                > 0.9
-            )
-        )
-
-        self.df[field_names.SCORE_F_COMMUNITIES] = (
-            self.df[meets_socio_field] & self.df[meets_burden_field]
-        )
-
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_g.py b/data/data-pipeline/data_pipeline/score/score_g.py
deleted file mode 100644
index d4e10726..00000000
--- a/data/data-pipeline/data_pipeline/score/score_g.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import data_pipeline.score.field_names as field_names
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreG(Score):
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score G")
-
-        high_school_cutoff_threshold = 0.05
-
-        # Score G is now modified NMTC
-        self.df[field_names.SCORE_G_COMMUNITIES] = (
-            (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.8)
-            & (
-                self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-                > high_school_cutoff_threshold
-            )
-        ) | (
-            (self.df[field_names.POVERTY_LESS_THAN_100_FPL_FIELD] > 0.20)
-            & (
-                self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-                > high_school_cutoff_threshold
-            )
-        )
-        self.df[field_names.SCORE_G] = self.df[
-            field_names.SCORE_G_COMMUNITIES
-        ].astype(int)
-        self.df["Score G (percentile)"] = self.df[field_names.SCORE_G]
-
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_h.py b/data/data-pipeline/data_pipeline/score/score_h.py
deleted file mode 100644
index 56168c53..00000000
--- a/data/data-pipeline/data_pipeline/score/score_h.py
+++ /dev/null
@@ -1,32 +0,0 @@
-import data_pipeline.score.field_names as field_names
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreH(Score):
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score H")
-
-        high_school_cutoff_threshold = 0.06
-
-        self.df[field_names.SCORE_H_COMMUNITIES] = (
-            (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.8)
-            & (
-                self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-                > high_school_cutoff_threshold
-            )
-        ) | (
-            (self.df[field_names.POVERTY_LESS_THAN_200_FPL_FIELD] > 0.40)
-            & (
-                self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-                > high_school_cutoff_threshold
-            )
-        )
-        self.df[field_names.SCORE_H] = self.df[
-            field_names.SCORE_H_COMMUNITIES
-        ].astype(int)
-
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_i.py b/data/data-pipeline/data_pipeline/score/score_i.py
deleted file mode 100644
index 25f2c098..00000000
--- a/data/data-pipeline/data_pipeline/score/score_i.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import data_pipeline.score.field_names as field_names
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreI(Score):
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score I")
-
-        high_school_cutoff_threshold = 0.05
-
-        self.df[field_names.SCORE_I_COMMUNITIES] = (
-            (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.7)
-            & (
-                self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-                > high_school_cutoff_threshold
-            )
-        ) | (
-            (self.df[field_names.POVERTY_LESS_THAN_200_FPL_FIELD] > 0.50)
-            & (
-                self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-                > high_school_cutoff_threshold
-            )
-        )
-        self.df[field_names.SCORE_I] = self.df[
-            field_names.SCORE_I_COMMUNITIES
-        ].astype(int)
-        self.df["Score I (percentile)"] = self.df[field_names.SCORE_I]
-
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_k.py b/data/data-pipeline/data_pipeline/score/score_k.py
deleted file mode 100644
index db05344c..00000000
--- a/data/data-pipeline/data_pipeline/score/score_k.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import data_pipeline.score.field_names as field_names
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreK(Score):
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score K")
-
-        high_school_cutoff_threshold = 0.06
-
-        self.df[field_names.SCORE_K] = (
-            (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.8)
-        ) | (self.df[field_names.POVERTY_LESS_THAN_100_FPL_FIELD] > 0.20)
-
-        self.df[field_names.SCORE_K_COMMUNITIES] = (
-            (self.df[field_names.MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD] < 0.8)
-            & (
-                self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-                > high_school_cutoff_threshold
-            )
-        ) | (
-            (self.df[field_names.POVERTY_LESS_THAN_100_FPL_FIELD] > 0.20)
-            & (
-                self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-                > high_school_cutoff_threshold
-            )
-        )
-
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_l.py b/data/data-pipeline/data_pipeline/score/score_l.py
deleted file mode 100644
index 322b9129..00000000
--- a/data/data-pipeline/data_pipeline/score/score_l.py
+++ /dev/null
@@ -1,690 +0,0 @@
-import data_pipeline.score.field_names as field_names
-import numpy as np
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreL(Score):
-    def __init__(self, df: pd.DataFrame) -> None:
-        self.LOW_INCOME_THRESHOLD: float = 0.65
-        self.ENVIRONMENTAL_BURDEN_THRESHOLD: float = 0.90
-        self.MEDIAN_HOUSE_VALUE_THRESHOLD: float = 0.90
-        self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD: float = 0.10
-
-        super().__init__(df)
-
-    def _combine_island_areas_with_states_and_set_thresholds(
-        self,
-        df: pd.DataFrame,
-        column_from_island_areas: str,
-        column_from_decennial_census: str,
-        combined_column_name: str,
-        threshold_cutoff_for_island_areas: float,
-    ) -> (pd.DataFrame, str):
-        """Steps to set thresholds for island areas.
-
-        This function is fairly logically complicated. It takes the following steps:
-
-            1. Combine the two different fields into a single field.
-            2. Calculate the 90th percentile cutoff raw value for the combined field.
-            3. Create a boolean series that is true for any census tract in the island
-                areas (and only the island areas) that exceeds this cutoff.
-
-        For step one, it combines data that is either the island area's Decennial Census
-        value in 2009 or the state's value in 5-year ACS ending in 2010.
-
-        This will be used to generate the percentile cutoff for the 90th percentile.
-
-        The stateside decennial census stopped asking economic comparisons,
-        so this is as close to apples-to-apples as we get. We use 5-year ACS for data
-        robustness over 1-year ACS.
-        """
-        # Create the combined field.
-        # TODO: move this combined field percentile calculation to `etl_score`,
-        #  since most other percentile logic is there.
-        # There should only be one entry in either 2009 or 2019 fields, not one in both.
-        # But just to be safe, we take the mean and ignore null values so if there
-        # *were* entries in both, this result would make sense.
-        df[combined_column_name] = df[
-            [column_from_island_areas, column_from_decennial_census]
-        ].mean(axis=1, skipna=True)
-
-        logger.debug(
-            f"Combined field `{combined_column_name}` has "
-            f"{df[combined_column_name].isnull().sum()} "
-            f"({df[combined_column_name].isnull().sum() * 100 / len(df):.2f}%) "
-            f"missing values for census tracts. "
-        )
-
-        # Calculate the percentile threshold raw value.
-        raw_threshold = np.nanquantile(
-            a=df[combined_column_name], q=threshold_cutoff_for_island_areas
-        )
-
-        logger.debug(
-            f"For combined field `{combined_column_name}`, "
-            f"the {threshold_cutoff_for_island_areas*100:.0f} percentile cutoff is a "
-            f"raw value of {raw_threshold:.3f}."
-        )
-
-        threshold_column_name = (
-            f"{column_from_island_areas} exceeds "
-            f"{threshold_cutoff_for_island_areas*100:.0f}th percentile"
-        )
-
-        df[threshold_column_name] = (
-            df[column_from_island_areas] >= raw_threshold
-        )
-
-        percent_of_tracts_highlighted = (
-            100
-            * df[threshold_column_name].sum()
-            / df[column_from_island_areas].notnull().sum()
-        )
-
-        logger.info(
-            f"For `{threshold_column_name}`, "
-            f"{df[threshold_column_name].sum()} ("
-            f"{percent_of_tracts_highlighted:.2f}% of tracts that have non-null data "
-            f"in the column) have a value of TRUE."
-        )
-
-        return df, threshold_column_name
-
-    def _create_low_income_threshold(self, df: pd.DataFrame) -> pd.Series:
-        """
-        Returns a pandas series (really a numpy array)
-        of booleans based on the condition of the FPL at 200%
-        is at or more than some established threshold
-        """
-        return (
-            df[
-                field_names.POVERTY_LESS_THAN_200_FPL_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.LOW_INCOME_THRESHOLD
-        )
-
-    def _increment_total_eligibility_exceeded(
-        self, columns_for_subset: list
-    ) -> None:
-        """
-        Increments the total eligible factors for a given tract
-        """
-
-        self.df[field_names.THRESHOLD_COUNT] += self.df[columns_for_subset].sum(
-            axis=1, skipna=True
-        )
-
-    def _climate_factor(self) -> bool:
-        # In Xth percentile or above for FEMA’s Risk Index (Source: FEMA
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level. Source: Census's American Community Survey]
-
-        climate_eligibility_columns = [
-            field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD,
-            field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD,
-            field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD,
-        ]
-
-        expected_population_loss_threshold = (
-            self.df[
-                field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        expected_agriculture_loss_threshold = (
-            self.df[
-                field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        expected_building_loss_threshold = (
-            self.df[
-                field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_FIELD] = (
-            expected_population_loss_threshold
-            & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self.df[field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_FIELD] = (
-            expected_agriculture_loss_threshold
-            & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self.df[field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD] = (
-            expected_building_loss_threshold
-            & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(climate_eligibility_columns)
-
-        return self.df[climate_eligibility_columns].any(axis="columns")
-
-    def _energy_factor(self) -> bool:
-        # In Xth percentile or above for DOE’s energy cost burden score (Source: LEAD Score)
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level. Source: Census's American Community Survey]
-
-        energy_eligibility_columns = [
-            field_names.PM25_EXPOSURE_LOW_INCOME_FIELD,
-            field_names.ENERGY_BURDEN_LOW_INCOME_FIELD,
-        ]
-
-        energy_burden_threshold = (
-            self.df[
-                field_names.ENERGY_BURDEN_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        pm25_threshold = (
-            self.df[
-                field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.PM25_EXPOSURE_LOW_INCOME_FIELD] = (
-            pm25_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self.df[field_names.ENERGY_BURDEN_LOW_INCOME_FIELD] = (
-            energy_burden_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(energy_eligibility_columns)
-
-        return self.df[energy_eligibility_columns].any(axis="columns")
-
-    def _transportation_factor(self) -> bool:
-        # In Xth percentile or above for diesel particulate matter (Source: EPA National Air Toxics Assessment (NATA)
-        # or
-        # In Xth percentile or above for PM 2.5 (Source: EPA, Office of Air and Radiation (OAR) fusion of model and monitor data)]
-        # or
-        # In Xth percentile or above traffic proximity and volume (Source: 2017 U.S. Department of Transportation (DOT) traffic data
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level. Source: Census's American Community Survey]
-
-        transportion_eligibility_columns = [
-            field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD,
-            field_names.TRAFFIC_PROXIMITY_LOW_INCOME_FIELD,
-        ]
-
-        diesel_threshold = (
-            self.df[
-                field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        traffic_threshold = (
-            self.df[
-                field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD] = (
-            diesel_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self.df[field_names.TRAFFIC_PROXIMITY_LOW_INCOME_FIELD] = (
-            traffic_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            transportion_eligibility_columns
-        )
-
-        return self.df[transportion_eligibility_columns].any(axis="columns")
-
-    def _housing_factor(self) -> bool:
-        # (
-        # In Xth percentile or above for lead paint (Source: Census's American Community Survey’s
-        # percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in homes)
-        # AND
-        # In Yth percentile or below for Median House Value (Source: Census's American Community Survey)
-        # )
-        # or
-        # In Xth percentile or above for housing cost burden (Source: HUD's Comprehensive Housing Affordability Strategy dataset
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level. Source: Census's American Community Survey]
-
-        housing_eligibility_columns = [
-            field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD,
-            field_names.HOUSING_BURDEN_LOW_INCOME_FIELD,
-        ]
-
-        lead_paint_median_home_value_threshold = (
-            self.df[
-                field_names.LEAD_PAINT_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        ) & (
-            self.df[
-                field_names.MEDIAN_HOUSE_VALUE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            <= self.MEDIAN_HOUSE_VALUE_THRESHOLD
-        )
-
-        housing_burden_threshold = (
-            self.df[
-                field_names.HOUSING_BURDEN_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        # series by series indicators
-        self.df[field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD] = (
-            lead_paint_median_home_value_threshold
-            & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self.df[field_names.HOUSING_BURDEN_LOW_INCOME_FIELD] = (
-            housing_burden_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(housing_eligibility_columns)
-
-        return self.df[housing_eligibility_columns].any(axis="columns")
-
-    def _pollution_factor(self) -> bool:
-        # Proximity to Risk Management Plan sites is > X
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level. Source: Census's American Community Survey]
-
-        pollution_eligibility_columns = [
-            field_names.RMP_LOW_INCOME_FIELD,
-            field_names.SUPERFUND_LOW_INCOME_FIELD,
-            field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD,
-        ]
-
-        rmp_sites_threshold = (
-            self.df[field_names.RMP_FIELD + field_names.PERCENTILE_FIELD_SUFFIX]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        npl_sites_threshold = (
-            self.df[field_names.NPL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        tsdf_sites_threshold = (
-            self.df[
-                field_names.TSDF_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        # individual series-by-series
-        self.df[field_names.RMP_LOW_INCOME_FIELD] = (
-            rmp_sites_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-        self.df[field_names.SUPERFUND_LOW_INCOME_FIELD] = (
-            npl_sites_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-        self.df[field_names.HAZARDOUS_WASTE_LOW_INCOME_FIELD] = (
-            tsdf_sites_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            pollution_eligibility_columns
-        )
-
-        return self.df[pollution_eligibility_columns].any(axis="columns")
-
-    def _water_factor(self) -> bool:
-        # In Xth percentile or above for wastewater discharge (Source: EPA Risk-Screening Environmental Indicators (RSEI) Model)
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level. Source: Census's American Community Survey]
-
-        wastewater_threshold = (
-            self.df[
-                field_names.WASTEWATER_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.WASTEWATER_DISCHARGE_LOW_INCOME_FIELD] = (
-            wastewater_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            [field_names.WASTEWATER_DISCHARGE_LOW_INCOME_FIELD]
-        )
-
-        return self.df[field_names.WASTEWATER_DISCHARGE_LOW_INCOME_FIELD]
-
-    def _health_factor(self) -> bool:
-        # In Xth percentile or above for diabetes (Source: CDC Places)
-        # or
-        # In Xth percentile or above for asthma (Source: CDC Places)
-        # or
-        # In Xth percentile or above for heart disease
-        # or
-        # In Xth percentile or above for low life expectancy (Source: CDC Places)
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level. Source: Census's American Community Survey]
-
-        health_eligibility_columns = [
-            field_names.DIABETES_LOW_INCOME_FIELD,
-            field_names.ASTHMA_LOW_INCOME_FIELD,
-            field_names.HEART_DISEASE_LOW_INCOME_FIELD,
-            field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_FIELD,
-        ]
-
-        diabetes_threshold = (
-            self.df[
-                field_names.DIABETES_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        asthma_threshold = (
-            self.df[
-                field_names.ASTHMA_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        heart_disease_threshold = (
-            self.df[
-                field_names.HEART_DISEASE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        low_life_expectancy_threshold = (
-            self.df[
-                field_names.LOW_LIFE_EXPECTANCY_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.DIABETES_LOW_INCOME_FIELD] = (
-            diabetes_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-        self.df[field_names.ASTHMA_LOW_INCOME_FIELD] = (
-            asthma_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-        self.df[field_names.HEART_DISEASE_LOW_INCOME_FIELD] = (
-            heart_disease_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-        self.df[field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_FIELD] = (
-            low_life_expectancy_threshold & self.df[field_names.FPL_200_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(health_eligibility_columns)
-
-        return self.df[health_eligibility_columns].any(axis="columns")
-
-    def _workforce_factor(self) -> bool:
-        # Where unemployment is above Xth percentile
-        # or
-        # Where median income as a percent of area median income is above Xth percentile
-        # or
-        # Where the percent of households at or below 100% of the federal poverty level
-        # is above Xth percentile
-        # or
-        # Where linguistic isolation is above Xth percentile
-        # AND
-        # Where the high school degree achievement rates for adults 25 years and older
-        # is less than Y%
-        # (necessary to screen out university tracts)
-
-        # Workforce criteria for states fields.
-        workforce_eligibility_columns = [
-            field_names.UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD,
-            field_names.POVERTY_LOW_HS_EDUCATION_FIELD,
-            field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD,
-            field_names.LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD,
-        ]
-
-        self.df[field_names.LOW_HS_EDUCATION_FIELD] = (
-            self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-            >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD
-        )
-
-        unemployment_threshold = (
-            self.df[
-                field_names.UNEMPLOYMENT_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        low_median_income_threshold = (
-            self.df[
-                field_names.LOW_MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        linguistic_isolation_threshold = (
-            self.df[
-                field_names.LINGUISTIC_ISO_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        poverty_threshold = (
-            self.df[
-                field_names.POVERTY_LESS_THAN_100_FPL_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD] = (
-            linguistic_isolation_threshold
-            & self.df[field_names.LOW_HS_EDUCATION_FIELD]
-        )
-
-        self.df[field_names.POVERTY_LOW_HS_EDUCATION_FIELD] = (
-            poverty_threshold & self.df[field_names.LOW_HS_EDUCATION_FIELD]
-        )
-
-        self.df[field_names.LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD] = (
-            low_median_income_threshold
-            & self.df[field_names.LOW_HS_EDUCATION_FIELD]
-        )
-
-        self.df[field_names.UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD] = (
-            unemployment_threshold & self.df[field_names.LOW_HS_EDUCATION_FIELD]
-        )
-
-        workforce_combined_criteria_for_states = self.df[
-            workforce_eligibility_columns
-        ].any(axis="columns")
-
-        self._increment_total_eligibility_exceeded(
-            workforce_eligibility_columns
-        )
-
-        # Now, calculate workforce criteria for island territories.
-        island_areas_workforce_eligibility_columns = [
-            field_names.ISLAND_AREAS_UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD,
-            field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD,
-            field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD,
-        ]
-
-        # First, combine unemployment.
-        (
-            self.df,
-            island_areas_unemployment_criteria_field_name,
-        ) = self._combine_island_areas_with_states_and_set_thresholds(
-            df=self.df,
-            column_from_island_areas=field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009,
-            column_from_decennial_census=field_names.CENSUS_UNEMPLOYMENT_FIELD_2010,
-            combined_column_name=field_names.COMBINED_UNEMPLOYMENT_2010,
-            threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD,
-        )
-
-        # Next, combine poverty.
-        (
-            self.df,
-            island_areas_poverty_criteria_field_name,
-        ) = self._combine_island_areas_with_states_and_set_thresholds(
-            df=self.df,
-            column_from_island_areas=field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009,
-            column_from_decennial_census=field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
-            combined_column_name=field_names.COMBINED_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
-            threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD,
-        )
-
-        # Also check whether low area median income is 90th percentile or higher
-        # within the islands.
-        island_areas_low_median_income_as_a_percent_of_ami_criteria_field_name = (
-            f"{field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009} exceeds "
-            f"{field_names.PERCENTILE}th percentile"
-        )
-        self.df[
-            island_areas_low_median_income_as_a_percent_of_ami_criteria_field_name
-        ] = (
-            self.df[
-                field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] = (
-            self.df[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009]
-            >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD
-        )
-
-        self.df[
-            field_names.ISLAND_AREAS_UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD
-        ] = (
-            self.df[island_areas_unemployment_criteria_field_name]
-            & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD]
-        )
-
-        self.df[field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD] = (
-            self.df[island_areas_poverty_criteria_field_name]
-            & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD]
-        )
-
-        self.df[
-            field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD
-        ] = (
-            self.df[
-                island_areas_low_median_income_as_a_percent_of_ami_criteria_field_name
-            ]
-            & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD]
-        )
-
-        workforce_combined_criteria_for_island_areas = self.df[
-            island_areas_workforce_eligibility_columns
-        ].any(axis="columns")
-
-        self._increment_total_eligibility_exceeded(
-            island_areas_workforce_eligibility_columns
-        )
-
-        percent_of_island_tracts_highlighted = (
-            100
-            * workforce_combined_criteria_for_island_areas.sum()
-            # Choosing a random column from island areas to calculate the denominator.
-            / self.df[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009]
-            .notnull()
-            .sum()
-        )
-
-        logger.debug(
-            f"For workforce criteria in island areas, "
-            f"{workforce_combined_criteria_for_island_areas.sum()} ("
-            f"{percent_of_island_tracts_highlighted:.2f}% of tracts that have non-null data "
-            f"in the column) have a value of TRUE."
-        )
-
-        # A tract is included if it meets either the states tract criteria or the
-        # island areas tract criteria.
-        return (
-            workforce_combined_criteria_for_states
-            | workforce_combined_criteria_for_island_areas
-        )
-
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score L")
-
-        self.df[field_names.THRESHOLD_COUNT] = 0
-        self.df[field_names.FPL_200_SERIES] = self._create_low_income_threshold(
-            self.df
-        )
-        self.df[field_names.L_CLIMATE] = self._climate_factor()
-        self.df[field_names.L_ENERGY] = self._energy_factor()
-        self.df[field_names.L_TRANSPORTATION] = self._transportation_factor()
-        self.df[field_names.L_HOUSING] = self._housing_factor()
-        self.df[field_names.L_POLLUTION] = self._pollution_factor()
-        self.df[field_names.L_WATER] = self._water_factor()
-        self.df[field_names.L_HEALTH] = self._health_factor()
-        self.df[field_names.L_WORKFORCE] = self._workforce_factor()
-
-        factors = [
-            field_names.L_CLIMATE,
-            field_names.L_ENERGY,
-            field_names.L_TRANSPORTATION,
-            field_names.L_HOUSING,
-            field_names.L_POLLUTION,
-            field_names.L_WATER,
-            field_names.L_HEALTH,
-            field_names.L_WORKFORCE,
-        ]
-        self.df[field_names.SCORE_L_COMMUNITIES] = self.df[factors].any(axis=1)
-
-        # Note: this is purely used for comparison tool analysis, and can be removed at a later date. - LMB.
-        non_workforce_factors = [
-            field_names.L_CLIMATE,
-            field_names.L_ENERGY,
-            field_names.L_TRANSPORTATION,
-            field_names.L_HOUSING,
-            field_names.L_POLLUTION,
-            field_names.L_WATER,
-            field_names.L_HEALTH,
-        ]
-        self.df[field_names.L_NON_WORKFORCE] = self.df[
-            non_workforce_factors
-        ].any(axis=1)
-
-        self.df[
-            field_names.SCORE_L + field_names.PERCENTILE_FIELD_SUFFIX
-        ] = self.df[field_names.SCORE_L_COMMUNITIES].astype(int)
-
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_m.py b/data/data-pipeline/data_pipeline/score/score_m.py
deleted file mode 100644
index a8751c2a..00000000
--- a/data/data-pipeline/data_pipeline/score/score_m.py
+++ /dev/null
@@ -1,888 +0,0 @@
-from typing import Tuple
-
-import data_pipeline.etl.score.constants as constants
-import data_pipeline.score.field_names as field_names
-import numpy as np
-import pandas as pd
-from data_pipeline.score.score import Score
-from data_pipeline.utils import get_module_logger
-
-logger = get_module_logger(__name__)
-
-
-class ScoreM(Score):
-    """Very similar to Score L, with a few minor modifications."""
-
-    def __init__(self, df: pd.DataFrame) -> None:
-        self.LOW_INCOME_THRESHOLD: float = 0.65
-        self.MAX_COLLEGE_ATTENDANCE_THRESHOLD: float = 0.20
-        self.ENVIRONMENTAL_BURDEN_THRESHOLD: float = 0.90
-        self.MEDIAN_HOUSE_VALUE_THRESHOLD: float = 0.90
-        self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD: float = 0.10
-
-        super().__init__(df)
-
-    def _combine_island_areas_with_states_and_set_thresholds(
-        self,
-        df: pd.DataFrame,
-        column_from_island_areas: str,
-        column_from_decennial_census: str,
-        combined_column_name: str,
-        threshold_cutoff_for_island_areas: float,
-    ) -> Tuple[pd.DataFrame, str]:
-        """Steps to set thresholds for island areas.
-
-        This function is fairly logically complicated. It takes the following steps:
-
-            1. Combine the two different fields into a single field.
-            2. Calculate the 90th percentile for the combined field.
-            3. Create a boolean series that is true for any census tract in the island
-                areas (and only the island areas) that exceeds this percentile.
-
-        For step one, it combines data that is either the island area's Decennial Census
-        value in 2009 or the state's value in 5-year ACS ending in 2010.
-
-        This will be used to generate the percentile cutoff for the 90th percentile.
-
-        The stateside decennial census stopped asking economic comparisons,
-        so this is as close to apples-to-apples as we get. We use 5-year ACS for data
-        robustness over 1-year ACS.
-        """
-        # Create the combined field.
-        # TODO: move this combined field percentile calculation to `etl_score`,
-        #  since most other percentile logic is there.
-        # There should only be one entry in either 2009 or 2019 fields, not one in both.
-        # But just to be safe, we take the mean and ignore null values so if there
-        # *were* entries in both, this result would make sense.
-        df[combined_column_name] = df[
-            [column_from_island_areas, column_from_decennial_census]
-        ].mean(axis=1, skipna=True)
-
-        # Create a percentile field for use in the Islands / PR visualization
-        # TODO: move this code
-        # In the code below, percentiles are constructed based on the combined column
-        # of census and island data, but only reported for the island areas (where there
-        # is no other comprehensive percentile information)
-        return_series_name = (
-            column_from_island_areas
-            + field_names.ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD
-            + field_names.PERCENTILE_FIELD_SUFFIX
-        )
-        df[return_series_name] = np.where(
-            df[column_from_decennial_census].isna(),
-            df[combined_column_name].rank(pct=True),
-            np.nan,
-        )
-
-        threshold_column_name = (
-            f"{column_from_island_areas} exceeds "
-            f"{threshold_cutoff_for_island_areas*100:.0f}th percentile"
-        )
-
-        df[threshold_column_name] = (
-            df[return_series_name] >= threshold_cutoff_for_island_areas
-        )
-
-        return df, threshold_column_name
-
-    def _create_low_income_and_low_college_attendance_threshold(
-        self, df: pd.DataFrame
-    ) -> pd.Series:
-        """
-        Returns a pandas series (really a numpy array)
-        of booleans based on the condition of the FPL at 200%
-        is at or more than some established threshold
-        """
-
-        return (df[field_names.LOW_INCOME_THRESHOLD]) & (
-            df[field_names.COLLEGE_ATTENDANCE_LESS_THAN_20_FIELD]
-            | (
-                # If college attendance data is null for this tract, just rely on the
-                # poverty data
-                df[field_names.COLLEGE_ATTENDANCE_FIELD].isna()
-            )
-        )
-
-    def _increment_total_eligibility_exceeded(
-        self, columns_for_subset: list, skip_fips: tuple = ()
-    ) -> None:
-        """
-        Increments the total eligible factors for a given tract
-
-        The new skip_fips argument specifies which (if any) fips codes to
-        skip over for incrementing.
-        This allows us to essentially skip data we think is of limited veracity,
-        without overriding any values in the data.
-        THIS IS A TEMPORARY FIX.
-        """
-        if skip_fips:
-            self.df[field_names.THRESHOLD_COUNT] += np.where(
-                self.df[field_names.GEOID_TRACT_FIELD].str.startswith(
-                    skip_fips
-                ),
-                0,
-                self.df[columns_for_subset].sum(axis=1, skipna=True),
-            )
-        else:
-            self.df[field_names.THRESHOLD_COUNT] += self.df[
-                columns_for_subset
-            ].sum(axis=1, skipna=True)
-
-    def _climate_factor(self) -> bool:
-        # In Xth percentile or above for FEMA’s Risk Index (Source: FEMA
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level and there is low higher ed attendance
-        # Source: Census's American Community Survey
-
-        climate_eligibility_columns = [
-            field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-        ]
-
-        self.df[
-            field_names.EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD
-        ] = (
-            self.df[
-                field_names.EXPECTED_POPULATION_LOSS_RATE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[
-            field_names.EXPECTED_AGRICULTURAL_LOSS_EXCEEDS_PCTILE_THRESHOLD
-        ] = (
-            self.df[
-                field_names.EXPECTED_AGRICULTURE_LOSS_RATE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.EXPECTED_BUILDING_LOSS_EXCEEDS_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.EXPECTED_BUILDING_LOSS_RATE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.CLIMATE_THRESHOLD_EXCEEDED] = (
-            self.df[
-                field_names.EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD
-            ]
-            | self.df[
-                field_names.EXPECTED_AGRICULTURAL_LOSS_EXCEEDS_PCTILE_THRESHOLD
-            ]
-            | self.df[
-                field_names.EXPECTED_BUILDING_LOSS_EXCEEDS_PCTILE_THRESHOLD
-            ]
-        )
-
-        self.df[
-            field_names.EXPECTED_POPULATION_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD
-        ] = (
-            self.df[
-                field_names.EXPECTED_POPULATION_LOSS_EXCEEDS_PCTILE_THRESHOLD
-            ]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self.df[
-            field_names.EXPECTED_AGRICULTURE_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD
-        ] = (
-            self.df[
-                field_names.EXPECTED_AGRICULTURAL_LOSS_EXCEEDS_PCTILE_THRESHOLD
-            ]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self.df[
-            field_names.EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_LOW_HIGHER_ED_FIELD
-        ] = (
-            self.df[field_names.EXPECTED_BUILDING_LOSS_EXCEEDS_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            climate_eligibility_columns,
-            skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS,
-        )
-
-        return self.df[climate_eligibility_columns].any(axis="columns")
-
-    def _energy_factor(self) -> bool:
-        # In Xth percentile or above for DOE’s energy cost burden score (Source: LEAD Score)
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level and has low higher ed attendance.
-        # Source: Census's American Community Survey
-
-        energy_eligibility_columns = [
-            field_names.PM25_EXPOSURE_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.ENERGY_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-        ]
-
-        self.df[field_names.ENERGY_BURDEN_EXCEEDS_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.ENERGY_BURDEN_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.PM25_EXCEEDS_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.PM25_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.ENERGY_THRESHOLD_EXCEEDED] = (
-            self.df[field_names.ENERGY_BURDEN_EXCEEDS_PCTILE_THRESHOLD]
-            | self.df[field_names.PM25_EXCEEDS_PCTILE_THRESHOLD]
-        )
-
-        self.df[field_names.PM25_EXPOSURE_LOW_INCOME_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.PM25_EXCEEDS_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self.df[field_names.ENERGY_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.ENERGY_BURDEN_EXCEEDS_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            energy_eligibility_columns,
-            skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS,
-        )
-
-        return self.df[energy_eligibility_columns].any(axis="columns")
-
-    def _transportation_factor(self) -> bool:
-        # In Xth percentile or above for diesel particulate matter (Source: EPA National Air Toxics Assessment (NATA)
-        # or
-        # In Xth percentile or above for PM 2.5 (Source: EPA, Office of Air and Radiation (OAR) fusion of model and monitor data)]
-        # or
-        # In Xth percentile or above traffic proximity and volume (Source: 2017 U.S. Department of Transportation (DOT) traffic data
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level and has a low percent of higher ed students.
-        # Source: Census's American Community Survey
-
-        transportion_eligibility_columns = [
-            field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.TRAFFIC_PROXIMITY_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-        ]
-
-        self.df[field_names.DIESEL_EXCEEDS_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.DIESEL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.TRAFFIC_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.TRAFFIC_THRESHOLD_EXCEEDED] = (
-            self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD]
-            | self.df[field_names.DIESEL_EXCEEDS_PCTILE_THRESHOLD]
-        )
-
-        self.df[
-            field_names.DIESEL_PARTICULATE_MATTER_LOW_INCOME_LOW_HIGHER_ED_FIELD
-        ] = (
-            self.df[field_names.DIESEL_EXCEEDS_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self.df[
-            field_names.TRAFFIC_PROXIMITY_LOW_INCOME_LOW_HIGHER_ED_FIELD
-        ] = (
-            self.df[field_names.TRAFFIC_PROXIMITY_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            transportion_eligibility_columns,
-            skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS,
-        )
-
-        return self.df[transportion_eligibility_columns].any(axis="columns")
-
-    def _housing_factor(self) -> bool:
-        # (
-        # In Xth percentile or above for lead paint (Source: Census's American Community Survey’s
-        # percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in homes)
-        # AND
-        # In Yth percentile or below for Median House Value (Source: Census's American Community Survey)
-        # )
-        # or
-        # In Xth percentile or above for housing cost burden (Source: HUD's Comprehensive Housing Affordability Strategy dataset
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level and has a low percent of higher ed students.
-        # Source: Census's American Community Survey
-
-        housing_eligibility_columns = [
-            field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.HOUSING_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-        ]
-
-        self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.LEAD_PAINT_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        ) & (
-            self.df[
-                field_names.MEDIAN_HOUSE_VALUE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            <= self.MEDIAN_HOUSE_VALUE_THRESHOLD
-        )
-
-        self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.HOUSING_BURDEN_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.HOUSING_THREHSOLD_EXCEEDED] = (
-            self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD]
-            | self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD]
-        )
-
-        # series by series indicators
-        self.df[
-            field_names.LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_LOW_HIGHER_ED_FIELD
-        ] = (
-            self.df[field_names.LEAD_PAINT_PROXY_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self.df[field_names.HOUSING_BURDEN_LOW_INCOME_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.HOUSING_BURDEN_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            housing_eligibility_columns,
-            skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS,
-        )
-
-        return self.df[housing_eligibility_columns].any(axis="columns")
-
-    def _pollution_factor(self) -> bool:
-        # Proximity to Risk Management Plan sites is > X
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level and has a low percent of higher ed students.
-        # Source: Census's American Community Survey
-
-        pollution_eligibility_columns = [
-            field_names.RMP_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.SUPERFUND_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.HAZARDOUS_WASTE_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-        ]
-
-        self.df[field_names.RMP_PCTILE_THRESHOLD] = (
-            self.df[field_names.RMP_FIELD + field_names.PERCENTILE_FIELD_SUFFIX]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.NPL_PCTILE_THRESHOLD] = (
-            self.df[field_names.NPL_FIELD + field_names.PERCENTILE_FIELD_SUFFIX]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.TSDF_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.TSDF_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.POLLUTION_THRESHOLD_EXCEEDED] = (
-            self.df[field_names.RMP_PCTILE_THRESHOLD]
-            | self.df[field_names.NPL_PCTILE_THRESHOLD]
-        ) | self.df[field_names.TSDF_PCTILE_THRESHOLD]
-
-        # individual series-by-series
-        self.df[field_names.RMP_LOW_INCOME_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.RMP_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-        self.df[field_names.SUPERFUND_LOW_INCOME_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.NPL_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-        self.df[field_names.HAZARDOUS_WASTE_LOW_INCOME_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.TSDF_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            pollution_eligibility_columns,
-            skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS,
-        )
-
-        return self.df[pollution_eligibility_columns].any(axis="columns")
-
-    def _water_factor(self) -> bool:
-        # In Xth percentile or above for wastewater discharge (Source: EPA Risk-Screening Environmental Indicators (RSEI) Model)
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level and has a low percent of higher ed students
-        # Source: Census's American Community Survey
-
-        self.df[field_names.WASTEWATER_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.WASTEWATER_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        # Straight copy here in case we add additional water fields.
-        self.df[field_names.WATER_THRESHOLD_EXCEEDED] = self.df[
-            field_names.WASTEWATER_PCTILE_THRESHOLD
-        ].copy()
-
-        self.df[
-            field_names.WASTEWATER_DISCHARGE_LOW_INCOME_LOW_HIGHER_ED_FIELD
-        ] = (
-            self.df[field_names.WASTEWATER_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            [field_names.WASTEWATER_DISCHARGE_LOW_INCOME_LOW_HIGHER_ED_FIELD],
-            skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS,
-        )
-
-        return self.df[
-            field_names.WASTEWATER_DISCHARGE_LOW_INCOME_LOW_HIGHER_ED_FIELD
-        ]
-
-    def _health_factor(self) -> bool:
-        # In Xth percentile or above for diabetes (Source: CDC Places)
-        # or
-        # In Xth percentile or above for asthma (Source: CDC Places)
-        # or
-        # In Xth percentile or above for heart disease
-        # or
-        # In Xth percentile or above for low life expectancy (Source: CDC Places)
-        # AND
-        # Low income: In Nth percentile or above for percent of block group population
-        # of households where household income is less than or equal to twice the federal
-        # poverty level and has a low percent of higher ed students
-        # Source: Census's American Community Survey
-
-        health_eligibility_columns = [
-            field_names.DIABETES_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.ASTHMA_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.HEART_DISEASE_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-            field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_LOW_HIGHER_ED_FIELD,
-        ]
-
-        self.df[field_names.DIABETES_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.DIABETES_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.ASTHMA_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.ASTHMA_FIELD + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.HEART_DISEASE_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.HEART_DISEASE_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.LOW_LIFE_EXPECTANCY_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.HEALTH_THRESHOLD_EXCEEDED] = (
-            (
-                self.df[field_names.DIABETES_PCTILE_THRESHOLD]
-                | self.df[field_names.ASTHMA_PCTILE_THRESHOLD]
-            )
-            | self.df[field_names.HEART_DISEASE_PCTILE_THRESHOLD]
-        ) | self.df[field_names.LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD]
-
-        self.df[field_names.DIABETES_LOW_INCOME_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.DIABETES_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-        self.df[field_names.ASTHMA_LOW_INCOME_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.ASTHMA_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-        self.df[field_names.HEART_DISEASE_LOW_INCOME_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.HEART_DISEASE_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-        self.df[
-            field_names.LOW_LIFE_EXPECTANCY_LOW_INCOME_LOW_HIGHER_ED_FIELD
-        ] = (
-            self.df[field_names.LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD]
-            & self.df[field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES]
-        )
-
-        self._increment_total_eligibility_exceeded(
-            health_eligibility_columns,
-            skip_fips=constants.DROP_FIPS_FROM_NON_WTD_THRESHOLDS,
-        )
-
-        return self.df[health_eligibility_columns].any(axis="columns")
-
-    def _workforce_factor(self) -> bool:
-        # Where unemployment is above Xth percentile
-        # or
-        # Where median income as a percent of area median income is above Xth percentile
-        # or
-        # Where the percent of households at or below 100% of the federal poverty level
-        # is above Xth percentile
-        # or
-        # Where linguistic isolation is above Xth percentile
-        # AND
-        # Where the high school degree achievement rates for adults 25 years and older
-        # is less than Y%
-        # AND the higher ed attendance rates are under Z%
-        # (necessary to screen out university tracts)
-
-        # Workforce criteria for states fields.
-        workforce_eligibility_columns = [
-            field_names.UNEMPLOYMENT_LOW_HS_LOW_HIGHER_ED_FIELD,
-            field_names.POVERTY_LOW_HS_LOW_HIGHER_ED_FIELD,
-            field_names.LINGUISTIC_ISOLATION_LOW_HS_LOW_HIGHER_ED_FIELD,
-            field_names.LOW_MEDIAN_INCOME_LOW_HS_LOW_HIGHER_ED_FIELD,
-        ]
-
-        self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.HIGH_SCHOOL_ED_FIELD]
-            >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD
-        ) & (
-            (
-                self.df[field_names.COLLEGE_ATTENDANCE_FIELD]
-                <= self.MAX_COLLEGE_ATTENDANCE_THRESHOLD
-            )
-            | (
-                # If college attendance data is null for this tract, just rely on the
-                # poverty/AMI data
-                self.df[field_names.COLLEGE_ATTENDANCE_FIELD].isna()
-            )
-        )
-
-        self.df[field_names.UNEMPLOYMENT_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.UNEMPLOYMENT_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.LOW_MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.LINGUISTIC_ISOLATION_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.LINGUISTIC_ISO_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.POVERTY_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.POVERTY_LESS_THAN_100_FPL_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.LINGUISTIC_ISOLATION_LOW_HS_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.LINGUISTIC_ISOLATION_PCTILE_THRESHOLD]
-            & self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD]
-        )
-
-        self.df[field_names.POVERTY_LOW_HS_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.POVERTY_PCTILE_THRESHOLD]
-            & self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD]
-        )
-
-        self.df[field_names.LOW_MEDIAN_INCOME_LOW_HS_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.LOW_MEDIAN_INCOME_PCTILE_THRESHOLD]
-            & self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD]
-        )
-
-        self.df[field_names.UNEMPLOYMENT_LOW_HS_LOW_HIGHER_ED_FIELD] = (
-            self.df[field_names.UNEMPLOYMENT_PCTILE_THRESHOLD]
-            & self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD]
-        )
-
-        workforce_combined_criteria_for_states = self.df[
-            workforce_eligibility_columns
-        ].any(axis="columns")
-
-        self._increment_total_eligibility_exceeded(
-            workforce_eligibility_columns
-        )
-
-        # Now, calculate workforce criteria for island territories.
-        island_areas_workforce_eligibility_columns = [
-            field_names.ISLAND_AREAS_UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD,
-            field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD,
-            field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD,
-        ]
-
-        # First, combine unemployment.
-        # This will include an adjusted percentile column for the island areas
-        # to be used by the front end.
-        (
-            self.df,
-            island_areas_unemployment_criteria_field_name,
-        ) = self._combine_island_areas_with_states_and_set_thresholds(
-            df=self.df,
-            column_from_island_areas=field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009,
-            column_from_decennial_census=field_names.CENSUS_UNEMPLOYMENT_FIELD_2010,
-            combined_column_name=field_names.COMBINED_UNEMPLOYMENT_2010,
-            threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD,
-        )
-
-        # TODO: Remove this, it's for checking only
-        assert (
-            island_areas_unemployment_criteria_field_name
-            == field_names.ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD
-        ), "Error combining island columns"
-
-        # Next, combine poverty.
-        # This will include an adjusted percentile column for the island areas
-        # to be used by the front end.
-        (
-            self.df,
-            island_areas_poverty_criteria_field_name,
-        ) = self._combine_island_areas_with_states_and_set_thresholds(
-            df=self.df,
-            column_from_island_areas=field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009,
-            column_from_decennial_census=field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
-            combined_column_name=field_names.COMBINED_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
-            threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD,
-        )
-
-        # TODO: Remove this, it's for checking only
-        assert (
-            island_areas_poverty_criteria_field_name
-            == field_names.ISLAND_POVERTY_PCTILE_THRESHOLD
-        ), "Error combining island columns"
-
-        # Also check whether low area median income is 90th percentile or higher
-        # within the islands.
-
-        # Note that because the field for low median does not have to be combined,
-        # unlike the other fields, we do not need to create a new percentile
-        # column. This code should probably be refactored when (TODO) we do the big
-        # refactor.
-        self.df[field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] = (
-            self.df[
-                field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
-        )
-
-        self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] = (
-            self.df[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009]
-            >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD
-        )
-
-        self.df[
-            field_names.ISLAND_AREAS_UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD
-        ] = (
-            self.df[island_areas_unemployment_criteria_field_name]
-            & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD]
-        )
-
-        self.df[field_names.ISLAND_AREAS_POVERTY_LOW_HS_EDUCATION_FIELD] = (
-            self.df[island_areas_poverty_criteria_field_name]
-            & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD]
-        )
-
-        self.df[
-            field_names.ISLAND_AREAS_LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD
-        ] = (
-            self.df[field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD]
-            & self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD]
-        )
-
-        workforce_combined_criteria_for_island_areas = self.df[
-            island_areas_workforce_eligibility_columns
-        ].any(axis="columns")
-
-        self._increment_total_eligibility_exceeded(
-            island_areas_workforce_eligibility_columns
-        )
-
-        percent_of_island_tracts_highlighted = (
-            100
-            * workforce_combined_criteria_for_island_areas.sum()
-            # Choosing a random column from island areas to calculate the denominator.
-            / self.df[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009]
-            .notnull()
-            .sum()
-        )
-
-        logger.debug(
-            f"For workforce criteria in island areas, "
-            f"{workforce_combined_criteria_for_island_areas.sum()} ("
-            f"{percent_of_island_tracts_highlighted:.2f}% of tracts that have non-null data "
-            f"in the column) have a value of TRUE."
-        )
-
-        # Because these criteria are calculated differently for the islands, we also calculate the
-        # thresholds to pass to the FE slightly differently
-
-        self.df[field_names.WORKFORCE_THRESHOLD_EXCEEDED] = (
-            ## First we calculate for the non-island areas
-            (
-                (
-                    self.df[field_names.POVERTY_PCTILE_THRESHOLD]
-                    | self.df[field_names.LINGUISTIC_ISOLATION_PCTILE_THRESHOLD]
-                )
-                | self.df[field_names.LOW_MEDIAN_INCOME_PCTILE_THRESHOLD]
-            )
-            | self.df[field_names.UNEMPLOYMENT_PCTILE_THRESHOLD]
-        ) | (
-            ## then we calculate just for the island areas
-            (
-                self.df[field_names.ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD]
-                | self.df[field_names.ISLAND_POVERTY_PCTILE_THRESHOLD]
-            )
-            | self.df[field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD]
-        )
-
-        # Because of the island complications, we also have to separately calculate the threshold for
-        # socioeconomic thresholds
-        self.df[field_names.WORKFORCE_SOCIO_INDICATORS_EXCEEDED] = (
-            self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD]
-            | self.df[field_names.LOW_HS_EDUCATION_LOW_HIGHER_ED_FIELD]
-        )
-
-        # A tract is included if it meets either the states tract criteria or the
-        # island areas tract criteria.
-        return (
-            workforce_combined_criteria_for_states
-            | workforce_combined_criteria_for_island_areas
-        )
-
-    def add_columns(self) -> pd.DataFrame:
-        logger.debug("Adding Score M")
-
-        self.df[field_names.THRESHOLD_COUNT] = 0
-
-        # TODO: move this inside of
-        #  `_create_low_income_and_low_college_attendance_threshold`
-        # and change the return signature of that method.
-        # Create a standalone field that captures the college attendance boolean
-        # threshold.
-        self.df[field_names.LOW_INCOME_THRESHOLD] = (
-            self.df[
-                field_names.POVERTY_LESS_THAN_200_FPL_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.LOW_INCOME_THRESHOLD
-        )
-
-        # Because we are moving this variable to be in the same direction as all
-        # other variables, we change this to be < rather than <=. This translates
-        # to "80% or more of residents are not college students", rather than
-        # "Strictly greater than 80% of residents are not college students."
-        # There are two tracts that are impacted by this (that is, they have exactly)
-        # 20% college students -- neither of these has been a DAC under any score.
-        self.df[field_names.COLLEGE_ATTENDANCE_LESS_THAN_20_FIELD] = (
-            self.df[field_names.COLLEGE_ATTENDANCE_FIELD]
-            < self.MAX_COLLEGE_ATTENDANCE_THRESHOLD
-        )
-
-        self.df[
-            field_names.FPL_200_AND_COLLEGE_ATTENDANCE_SERIES
-        ] = self._create_low_income_and_low_college_attendance_threshold(
-            self.df
-        )
-        self.df[field_names.M_CLIMATE] = self._climate_factor()
-        self.df[field_names.M_ENERGY] = self._energy_factor()
-        self.df[field_names.M_TRANSPORTATION] = self._transportation_factor()
-        self.df[field_names.M_HOUSING] = self._housing_factor()
-        self.df[field_names.M_POLLUTION] = self._pollution_factor()
-        self.df[field_names.M_WATER] = self._water_factor()
-        self.df[field_names.M_HEALTH] = self._health_factor()
-        self.df[field_names.M_WORKFORCE] = self._workforce_factor()
-
-        factors = [
-            field_names.M_CLIMATE,
-            field_names.M_ENERGY,
-            field_names.M_TRANSPORTATION,
-            field_names.M_HOUSING,
-            field_names.M_POLLUTION,
-            field_names.M_WATER,
-            field_names.M_HEALTH,
-            field_names.M_WORKFORCE,
-        ]
-        self.df[field_names.CATEGORY_COUNT] = self.df[factors].sum(axis=1)
-        self.df[field_names.SCORE_M_COMMUNITIES] = self.df[factors].any(axis=1)
-
-        # Note: this is purely used for comparison tool analysis, and can be removed at a later date. - LMB.
-        non_workforce_factors = [
-            field_names.M_CLIMATE,
-            field_names.M_ENERGY,
-            field_names.M_TRANSPORTATION,
-            field_names.M_HOUSING,
-            field_names.M_POLLUTION,
-            field_names.M_WATER,
-            field_names.M_HEALTH,
-        ]
-        self.df[field_names.M_NON_WORKFORCE] = self.df[
-            non_workforce_factors
-        ].any(axis=1)
-
-        self.df[
-            field_names.SCORE_M + field_names.PERCENTILE_FIELD_SUFFIX
-        ] = self.df[field_names.SCORE_M_COMMUNITIES].astype(int)
-
-        return self.df
diff --git a/data/data-pipeline/data_pipeline/score/score_narwhal.py b/data/data-pipeline/data_pipeline/score/score_narwhal.py
index 88473ca5..3a42879e 100644
--- a/data/data-pipeline/data_pipeline/score/score_narwhal.py
+++ b/data/data-pipeline/data_pipeline/score/score_narwhal.py
@@ -807,7 +807,7 @@ class ScoreNarwhal(Score):
             island_areas_unemployment_criteria_field_name,
         ) = self._combine_island_areas_with_states_and_set_thresholds(
             df=self.df,
-            column_from_island_areas=field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009,
+            column_from_island_areas=field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019,
             column_from_decennial_census=field_names.CENSUS_UNEMPLOYMENT_FIELD_2010,
             combined_column_name=field_names.COMBINED_UNEMPLOYMENT_2010,
             threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD,
@@ -827,7 +827,7 @@ class ScoreNarwhal(Score):
             island_areas_poverty_criteria_field_name,
         ) = self._combine_island_areas_with_states_and_set_thresholds(
             df=self.df,
-            column_from_island_areas=field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009,
+            column_from_island_areas=field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019,
             column_from_decennial_census=field_names.CENSUS_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
             combined_column_name=field_names.COMBINED_POVERTY_LESS_THAN_100_FPL_FIELD_2010,
             threshold_cutoff_for_island_areas=self.ENVIRONMENTAL_BURDEN_THRESHOLD,
@@ -848,14 +848,14 @@ class ScoreNarwhal(Score):
         # refactor.
         self.df[field_names.ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD] = (
             self.df[
-                field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2009
+                field_names.LOW_CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019
                 + field_names.PERCENTILE_FIELD_SUFFIX
             ]
             >= self.ENVIRONMENTAL_BURDEN_THRESHOLD
         )
 
         self.df[field_names.ISLAND_AREAS_LOW_HS_EDUCATION_FIELD] = (
-            self.df[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2009]
+            self.df[field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019]
             >= self.LACK_OF_HIGH_SCHOOL_MINIMUM_THRESHOLD
         )
 
@@ -890,7 +890,7 @@ class ScoreNarwhal(Score):
             100
             * workforce_combined_criteria_for_island_areas.sum()
             # Choosing a random column from island areas to calculate the denominator.
-            / self.df[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009]
+            / self.df[field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019]
             .notnull()
             .sum()
         )
@@ -1013,6 +1013,66 @@ class ScoreNarwhal(Score):
             self.df[field_names.SCORE_N_COMMUNITIES],
         )
 
+    def _mark_territory_dacs(self) -> None:
+        """Territory tracts that are flagged as low income are Score N communities."""
+        self.df[field_names.SCORE_N_COMMUNITIES] = np.where(
+            self.df[field_names.GEOID_TRACT_FIELD].str.startswith(
+                tuple(constants.TILES_ISLAND_AREA_FIPS_CODES)
+            )
+            & self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED],
+            True,
+            self.df[field_names.SCORE_N_COMMUNITIES],
+        )
+
+    def _mark_grandfathered_dacs(self) -> None:
+        """Territory tracts that are flagged as DACS in the V1.0 score are also marked."""
+        self.df[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0] = np.where(
+            self.df[field_names.FINAL_SCORE_N_BOOLEAN_V1_0]
+            & ~self.df[field_names.FINAL_SCORE_N_BOOLEAN],
+            True,
+            False,
+        )
+        self.df[field_names.FINAL_SCORE_N_BOOLEAN] = np.where(
+            self.df[field_names.FINAL_SCORE_N_BOOLEAN_V1_0],
+            True,
+            self.df[field_names.FINAL_SCORE_N_BOOLEAN],
+        )
+
+    def _mark_poverty_flag(self) -> None:
+        """Combine poverty less than 200% for territories and update the income flag."""
+        # First we set the low income flag for non-territories by themselves, this
+        # way we don't change the original outcome if we include territories.
+        self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED] = (
+            self.df[
+                # UPDATE: Pull the imputed poverty statistic
+                field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD
+                + field_names.PERCENTILE_FIELD_SUFFIX
+            ]
+            >= self.LOW_INCOME_THRESHOLD
+        )
+
+        # Now we set the low income flag only for territories, but we need to rank them
+        # with all other tracts.
+        (
+            self.df,
+            island_areas_poverty_200_criteria_field_name,
+        ) = self._combine_island_areas_with_states_and_set_thresholds(
+            df=self.df,
+            column_from_island_areas=field_names.CENSUS_DECENNIAL_ADJUSTED_POVERTY_LESS_THAN_200_FPL_FIELD_2019,
+            column_from_decennial_census=field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD,
+            combined_column_name=field_names.COMBINED_POVERTY_LESS_THAN_200_FPL_FIELD_2010,
+            threshold_cutoff_for_island_areas=self.LOW_INCOME_THRESHOLD,
+        )
+        self.df.loc[
+            self.df[field_names.GEOID_TRACT_FIELD].str.startswith(
+                tuple(constants.TILES_ISLAND_AREA_FIPS_CODES)
+            ),
+            field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED,
+        ] = (
+            self.df[island_areas_poverty_200_criteria_field_name]
+            >= self.LOW_INCOME_THRESHOLD
+        )
+
     def _get_percent_of_tract_that_is_dac(self) -> float:
         """Per the October 7th compromise (#1988),
         tracts can be partially DACs if some portion of the tract is tribal land.
@@ -1034,14 +1094,7 @@ class ScoreNarwhal(Score):
         logger.debug("Adding Score Narhwal")
         self.df[field_names.THRESHOLD_COUNT] = 0
 
-        self.df[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED] = (
-            self.df[
-                # UPDATE: Pull the imputed poverty statistic
-                field_names.POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD
-                + field_names.PERCENTILE_FIELD_SUFFIX
-            ]
-            >= self.LOW_INCOME_THRESHOLD
-        )
+        self._mark_poverty_flag()
 
         self.df[field_names.N_CLIMATE] = self._climate_factor()
         self.df[field_names.N_ENERGY] = self._energy_factor()
@@ -1065,12 +1118,14 @@ class ScoreNarwhal(Score):
         self.df[field_names.CATEGORY_COUNT] = self.df[factors].sum(axis=1)
         self.df[field_names.SCORE_N_COMMUNITIES] = self.df[factors].any(axis=1)
         self._mark_tribal_dacs()
+        self._mark_territory_dacs()
         self.df[
             field_names.SCORE_N_COMMUNITIES
             + field_names.PERCENTILE_FIELD_SUFFIX
         ] = self.df[field_names.SCORE_N_COMMUNITIES].astype(int)
 
         self._mark_donut_hole_tracts()
+        self._mark_grandfathered_dacs()
         self.df[
             field_names.PERCENT_OF_TRACT_IS_DAC
         ] = self._get_percent_of_tract_that_is_dac()
diff --git a/data/data-pipeline/data_pipeline/score/utils.py b/data/data-pipeline/data_pipeline/score/utils.py
index 5450faf6..092bc646 100644
--- a/data/data-pipeline/data_pipeline/score/utils.py
+++ b/data/data-pipeline/data_pipeline/score/utils.py
@@ -11,6 +11,42 @@ from data_pipeline.utils import get_module_logger
 logger = get_module_logger(__name__)
 
 
+def in_water_range(x: int):
+    """Check if a tract ID is within the water area ID range
+
+    Input must be integer representation of the 6-char census tract code
+    (equivalent to last 6 characters of full geo ID)
+
+    Returns TRUE if ID is in the water area range.
+    Returns FALSE if ID is NOT in the water area range.
+
+    NB: Not currently in use; artifact of attempt to filter
+    water areas using Census tract ID rangers.
+    """
+    if x >= 990000 and x <= 990099:
+        return True
+    return False
+
+
+def full_geo_id_to_water_range_bool(x: str):
+    """Check if a geo ID is within the water area ID range.
+
+    First, convert full ID string to int of last six digits,
+    Then check if this int is in the correct range.
+
+    Input should be the 11 character Census tract identifier,
+    the last 6 characters of which are the census tract code.
+
+    Returns TRUE if ID is in the water area range.
+    Returns FALSE if ID is NOT in the water area range.
+
+    NB: Not currently in use; artifact of attempt to filter
+    water areas using Census tract ID rangers.
+    """
+    num_x = int(x[-6:])
+    return in_water_range(num_x)
+
+
 def calculate_tract_adjacency_scores(
     df: pd.DataFrame, score_column: str
 ) -> pd.DataFrame:
@@ -33,11 +69,15 @@ def calculate_tract_adjacency_scores(
     ORIGINAL_TRACT = "ORIGINAL_TRACT"
     logger.debug("Calculating tract adjacency scores")
     tract_data = get_tract_geojson()
+
     df: gpd.GeoDataFrame = tract_data.merge(
         df, on=field_names.GEOID_TRACT_FIELD
     )
     df = df.rename(columns={field_names.GEOID_TRACT_FIELD: ORIGINAL_TRACT})
 
+    # remove water areas from input frame
+    df = df[df[field_names.LAND_AREA_FIELD] > 0]
+
     logger.debug("Perfoming spatial join to find all adjacent tracts")
     adjacent_tracts: gpd.GeoDataFrame = df.sjoin(
         tract_data, predicate="touches"
diff --git a/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py b/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py
index 3132c51a..943efeb0 100644
--- a/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py
+++ b/data/data-pipeline/data_pipeline/tests/score/test_score_narwhal_methods.py
@@ -4,20 +4,19 @@ import pytest
 from data_pipeline.config import settings
 from data_pipeline.etl.score.etl_score import ScoreETL
 from data_pipeline.score import field_names
+from data_pipeline.score.score_narwhal import ScoreNarwhal
 from data_pipeline.utils import get_module_logger
 
+
 logger = get_module_logger(__name__)
 
+TEST_DATA_FOLDER = settings.APP_ROOT / "tests" / "score" / "test_utils" / "data"
+
 
 @pytest.fixture
 def toy_score_df(scope="module"):
     return pd.read_csv(
-        settings.APP_ROOT
-        / "tests"
-        / "score"
-        / "test_utils"
-        / "data"
-        / "test_drop_tracts_from_percentile.csv",
+        TEST_DATA_FOLDER / "test_drop_tracts_from_percentile.csv",
         dtype={field_names.GEOID_TRACT_FIELD: str},
     )
 
@@ -83,3 +82,76 @@ def test_drop_all_tracts(toy_score_df):
         toy_score_df,
         drop_tracts=toy_score_df[field_names.GEOID_TRACT_FIELD].to_list(),
     ), "Percentile in score fails when we drop all tracts"
+
+
+def test_mark_territory_dacs():
+    test_data = pd.read_csv(
+        TEST_DATA_FOLDER / "test_mark_territory_dacs.csv",
+        dtype={field_names.GEOID_TRACT_FIELD: str},
+    )
+    # Sanity check on the input data
+    assert not test_data[field_names.SCORE_N_COMMUNITIES].all()
+
+    scorer = ScoreNarwhal(test_data)
+    scorer._mark_territory_dacs()
+    # Check territories are set to true
+    expected_new_dacs_filter = test_data[field_names.GEOID_TRACT_FIELD].isin(
+        ["60050951100", "66010951100", "69110001101", "78010990000"]
+    )
+    assert test_data.loc[
+        expected_new_dacs_filter, field_names.SCORE_N_COMMUNITIES
+    ].all()
+    # Non-territories are still false
+    assert not test_data.loc[
+        ~expected_new_dacs_filter, field_names.SCORE_N_COMMUNITIES
+    ].all()
+
+
+def test_mark_poverty_flag():
+    test_data = pd.read_csv(
+        TEST_DATA_FOLDER / "test_mark_poverty_flag.csv",
+        dtype={field_names.GEOID_TRACT_FIELD: str},
+    )
+    # Sanity check on the input data
+    assert not test_data[field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED].all()
+
+    scorer = ScoreNarwhal(test_data)
+    scorer._mark_poverty_flag()
+    expected_low_income_filter = test_data[field_names.GEOID_TRACT_FIELD].isin(
+        ["36087011302", "66010951100", "78010990000"]
+    )
+    # Three tracts are set to true
+    assert test_data[expected_low_income_filter][
+        field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED
+    ].all()
+    # Everything else is false
+    assert not test_data[~expected_low_income_filter][
+        field_names.FPL_200_SERIES_IMPUTED_AND_ADJUSTED
+    ].all()
+
+
+def test_mark_grandfathered_dacs():
+    data = {
+        field_names.GEOID_TRACT_FIELD: [
+            "78010971500",
+            "78010970500",
+            "66010954400",
+            "66010953400",
+        ],
+        field_names.FINAL_SCORE_N_BOOLEAN_V1_0: [False, False, True, True],
+        field_names.FINAL_SCORE_N_BOOLEAN: [False, True, False, True],
+    }
+    test_df = pd.DataFrame(data)
+    scorer = ScoreNarwhal(test_df)
+    scorer._mark_grandfathered_dacs()
+    result = scorer.df
+    assert field_names.GRANDFATHERED_N_COMMUNITIES_V1_0 in result.columns
+    assert not result[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0][0]
+    assert not result[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0][1]
+    assert result[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0][2]
+    assert not result[field_names.GRANDFATHERED_N_COMMUNITIES_V1_0][3]
+
+    assert not result[field_names.FINAL_SCORE_N_BOOLEAN][0]
+    assert result[field_names.FINAL_SCORE_N_BOOLEAN][1]
+    assert result[field_names.FINAL_SCORE_N_BOOLEAN][2]
+    assert result[field_names.FINAL_SCORE_N_BOOLEAN][3]
diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv
new file mode 100644
index 00000000..92110932
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_poverty_flag.csv
@@ -0,0 +1,8 @@
+GEOID10_TRACT,"Percentage households below 200% of federal poverty line in 2009, adjusted and imputed","Percent of individuals below 200% Federal Poverty Line, imputed and adjusted","Percent of individuals below 200% Federal Poverty Line, imputed and adjusted (percentile)",Is low income (imputed and adjusted)?
+01071950300,,0.1,0.1,False
+36087011302,,0.7,0.7,False
+72119130701,,0.5,0.5,False
+60050951100,0.1,,,False
+66010951100,0.7,,,False
+69110001100,0.5,,,False
+78010990000,0.9,,,False
\ No newline at end of file
diff --git a/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_territory_dacs.csv b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_territory_dacs.csv
new file mode 100644
index 00000000..84992e2a
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/tests/score/test_utils/data/test_mark_territory_dacs.csv
@@ -0,0 +1,9 @@
+GEOID10_TRACT,Is low income (imputed and adjusted)?,Definition N (communities)
+01071950300,True,False
+36087011302,False,False
+72119130701,True,False
+60050951100,True,False
+66010951100,True,False
+69110001100,False,False
+69110001101,True,False
+78010990000,True,False
\ No newline at end of file
diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/as/60/010/census.json b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/as/60/010/census.json
new file mode 100644
index 00000000..429c3d37
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/as/60/010/census.json
@@ -0,0 +1,8 @@
+[["NAME","P1_001N","PBG19_005N","PBG19_012N","PCT31_001N","PBG32_003N","PBG32_007N","PBG32_010N","PBG32_014N","PCT34_003N","PCT34_016N","PBG43_001N","PBG74_001N","PBG74_002N","PBG74_003N","PBG74_004N","PBG74_010N","PCT9_001N","PCT9_003N","PCT9_079N","PCT9_130N","PCT9_155N","PCT9_180N","PCT9_205N","PCT9_230N","P5_002N","state","county","tract"],
+["Census Tract 9505, Eastern District, American Samoa","2623","305","308","2044","443","42","369","44","175","164","26000","2151","538","338","329","352","2623","2021","342","77","0","0","99","84","107","60","010","950500"],
+["Census Tract 9503, Eastern District, American Samoa","2415","325","345","2293","418","35","379","53","173","174","33631","2414","448","332","320","565","2415","2259","39","6","0","0","1","110","14","60","010","950300"],
+["Census Tract 9501, Eastern District, American Samoa","1487","248","214","1404","269","38","254","39","103","106","24219","1485","448","243","146","200","1487","1449","3","1","1","0","1","32","6","60","010","950100"],
+["Census Tract 9502, Eastern District, American Samoa","1158","164","143","1096","221","23","163","18","84","75","28333","1148","236","172","175","335","1158","1092","11","7","0","0","1","47","5","60","010","950200"],
+["Census Tract 9506, Eastern District, American Samoa","3218","452","458","3031","645","57","573","56","216","242","26970","3188","774","462","439","666","3218","2781","167","9","1","0","5","255","40","60","010","950600"],
+["Census Tract 9507, Eastern District, American Samoa","2727","381","364","2489","569","60","456","48","208","183","29083","2612","593","458","315","494","2727","2366","217","29","8","2","10","95","38","60","010","950700"],
+["Census Tract 9509, Eastern District, American Samoa","3431","512","476","3239","718","76","682","89","238","244","30643","3412","724","510","573","623","3431","2988","332","28","0","0","2","81","27","60","010","950900"]]
\ No newline at end of file
diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/gu/66/010/census.json b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/gu/66/010/census.json
new file mode 100644
index 00000000..ce6dbaf4
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/extract/2020/gu/66/010/census.json
@@ -0,0 +1,58 @@
+[["NAME","P1_001N","PBG19_005N","PBG19_012N","PCT31_001N","PBG32_003N","PBG32_007N","PBG32_010N","PBG32_014N","PCT34_003N","PCT34_016N","PBG43_001N","PBG74_001N","PBG74_002N","PBG74_003N","PBG74_004N","PBG74_010N","PCT10_001N","PCT10_003N","PCT10_204N","PCT10_330N","PCT10_355N","PCT10_380N","PCT10_405N","P5_026N","PCT9_003N","state","county","tract"],
+["Census Tract 9501, Guam, Guam","1347","7","6","77","25","1","14","0","2","29","40000","80","6","5","12","32","1347","13","128","159","11","63","111","764","221","66","010","950100"],
+["Census Tract 9502, Guam, Guam","626","9","9","87","23","2","19","2","3","30","43750","102","6","0","10","44","626","29","91","56","3","32","86","296","81","66","010","950200"],
+["Census Tract 9503, Guam, Guam","629","4","1","6","1","0","1","0","0","1","53750","6","0","0","0","0","629","3","620","0","0","0","1","5","0","66","010","950300"],
+["Census Tract 9504.01, Guam, Guam","5809","669","581","5273","1447","150","1191","141","151","2096","53309","5688","455","322","376","2836","5809","1592","3775","14","9","3","359","54","70","66","010","950401"],
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\ No newline at end of file
diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/imputation/census-us-territory-geojson.json b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/imputation/census-us-territory-geojson.json
new file mode 100644
index 00000000..5f3e791c
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/imputation/census-us-territory-geojson.json
@@ -0,0 +1,138 @@
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diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/transform/usa.csv b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/transform/usa.csv
new file mode 100644
index 00000000..3addabba
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/data/transform/usa.csv
@@ -0,0 +1,134 @@
+,NAME,Total population in 2009,Total male high school graduates 25 and over,Total female high school graduates 25 and over,Total asked enrolled in college or graduate school (excludes military housing),Total males in labor force,Total males not in labor force,Total females in labor force,Total females not in labor force,Males enrolled in college or graduate school (excludes military housing),Females enrolled in college or graduate school (excludes military housing),Median household income in 2009 ($),Total Household poverty level IN 2019,Household poverty level Under 0.50 IN 2019,Household poverty level Under 0.74 IN 2019,Household poverty level Under 0.99 IN 2019,Household poverty level Over 2.0 IN 2019,Total population surveyed on racial data,Native Hawaiian or Pacific,Asian,White,Black or African American,American Indian / Alaska Native,other races,two or more races,Hispanic or Latino,state,county,tract,Territory Median Income
+0,"Census Tract 9505, Eastern District, American Samoa",2623.0,305.0,308.0,2044.0,443.0,42.0,369.0,44.0,175.0,164.0,26000.0,2151.0,538.0,338.0,329.0,352.0,2623.0,2021.0,342.0,77.0,0.0,0.0,99.0,84.0,107.0,60,010,950500,26352.0
+1,"Census Tract 9503, Eastern District, American Samoa",2415.0,325.0,345.0,2293.0,418.0,35.0,379.0,53.0,173.0,174.0,33631.0,2414.0,448.0,332.0,320.0,565.0,2415.0,2259.0,39.0,6.0,0.0,0.0,1.0,110.0,14.0,60,010,950300,26352.0
+2,"Census Tract 9501, Eastern District, American Samoa",1487.0,248.0,214.0,1404.0,269.0,38.0,254.0,39.0,103.0,106.0,24219.0,1485.0,448.0,243.0,146.0,200.0,1487.0,1449.0,3.0,1.0,1.0,0.0,1.0,32.0,6.0,60,010,950100,26352.0
+3,"Census Tract 9502, Eastern District, American Samoa",1158.0,164.0,143.0,1096.0,221.0,23.0,163.0,18.0,84.0,75.0,28333.0,1148.0,236.0,172.0,175.0,335.0,1158.0,1092.0,11.0,7.0,0.0,0.0,1.0,47.0,5.0,60,010,950200,26352.0
+4,"Census Tract 9506, Eastern District, American Samoa",3218.0,452.0,458.0,3031.0,645.0,57.0,573.0,56.0,216.0,242.0,26970.0,3188.0,774.0,462.0,439.0,666.0,3218.0,2781.0,167.0,9.0,1.0,0.0,5.0,255.0,40.0,60,010,950600,26352.0
+5,"Census Tract 9507, Eastern District, American Samoa",2727.0,381.0,364.0,2489.0,569.0,60.0,456.0,48.0,208.0,183.0,29083.0,2612.0,593.0,458.0,315.0,494.0,2727.0,2366.0,217.0,29.0,8.0,2.0,10.0,95.0,38.0,60,010,950700,26352.0
+6,"Census Tract 9509, Eastern District, American Samoa",3431.0,512.0,476.0,3239.0,718.0,76.0,682.0,89.0,238.0,244.0,30643.0,3412.0,724.0,510.0,573.0,623.0,3431.0,2988.0,332.0,28.0,0.0,0.0,2.0,81.0,27.0,60,010,950900,26352.0
+0,"Census Tract 9518, Manu'a District, American Samoa",832.0,169.0,119.0,784.0,213.0,23.0,148.0,36.0,63.0,63.0,26818.0,831.0,167.0,155.0,181.0,116.0,832.0,800.0,0.0,2.0,0.0,0.0,0.0,30.0,3.0,60,020,951800,26352.0
+0,"Census Tract 9519, Rose Island, American Samoa",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,60,030,951900,
+0,"Census Tract 9520, Swains Island, American Samoa",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,60,040,952000,
+0,"Census Tract 9510, Western District, American Samoa",3593.0,484.0,488.0,3386.0,738.0,34.0,559.0,25.0,272.0,225.0,24688.0,3572.0,1032.0,693.0,578.0,399.0,3593.0,3002.0,473.0,10.0,0.0,0.0,5.0,103.0,22.0,60,050,951000,26352.0
+1,"Census Tract 9511, Western District, American Samoa",6252.0,1007.0,961.0,5818.0,1162.0,80.0,842.0,72.0,477.0,422.0,24227.0,6201.0,2026.0,1206.0,876.0,725.0,6252.0,5765.0,212.0,50.0,1.0,0.0,1.0,223.0,40.0,60,050,951100,26352.0
+2,"Census Tract 9512.01, Western District, American Samoa",3406.0,411.0,386.0,2896.0,625.0,33.0,483.0,43.0,239.0,206.0,29107.0,3061.0,723.0,517.0,451.0,408.0,3406.0,3030.0,230.0,12.0,0.0,2.0,4.0,128.0,11.0,60,050,951201,26352.0
+3,"Census Tract 9512.02, Western District, American Samoa",4582.0,586.0,616.0,4322.0,906.0,59.0,814.0,64.0,319.0,320.0,31714.0,4535.0,784.0,687.0,577.0,1184.0,4582.0,3903.0,355.0,50.0,3.0,6.0,3.0,262.0,31.0,60,050,951202,26352.0
+4,"Census Tract 9512.03, Western District, American Samoa",4994.0,605.0,648.0,4721.0,964.0,89.0,896.0,106.0,330.0,340.0,32196.0,4948.0,1019.0,724.0,626.0,1199.0,4994.0,4308.0,264.0,61.0,3.0,3.0,4.0,351.0,33.0,60,050,951203,26352.0
+5,"Census Tract 9513, Western District, American Samoa",3010.0,443.0,419.0,2844.0,632.0,115.0,542.0,107.0,240.0,220.0,27330.0,2994.0,791.0,488.0,384.0,593.0,3010.0,2833.0,97.0,10.0,1.0,0.0,1.0,68.0,11.0,60,050,951300,26352.0
+6,"Census Tract 9515, Western District, American Samoa",1689.0,270.0,235.0,1588.0,355.0,77.0,307.0,96.0,139.0,110.0,28611.0,1674.0,484.0,184.0,217.0,246.0,1689.0,1593.0,14.0,2.0,1.0,0.0,0.0,79.0,2.0,60,050,951500,26352.0
+7,"Census Tract 9516, Western District, American Samoa",4293.0,629.0,565.0,4077.0,1075.0,306.0,937.0,351.0,311.0,322.0,28958.0,4280.0,990.0,726.0,621.0,889.0,4293.0,3900.0,122.0,20.0,5.0,0.0,2.0,244.0,16.0,60,050,951600,26352.0
+0,"Census Tract 9501, Guam, Guam",1347.0,7.0,6.0,77.0,25.0,1.0,14.0,0.0,2.0,29.0,40000.0,80.0,6.0,5.0,12.0,32.0,1347.0,13.0,128.0,764.0,159.0,11.0,63.0,111.0,221.0,66,010,950100,58289.0
+1,"Census Tract 9502, Guam, Guam",626.0,9.0,9.0,87.0,23.0,2.0,19.0,2.0,3.0,30.0,43750.0,102.0,6.0,0.0,10.0,44.0,626.0,29.0,91.0,296.0,56.0,3.0,32.0,86.0,81.0,66,010,950200,58289.0
+2,"Census Tract 9503, Guam, Guam",629.0,4.0,1.0,6.0,1.0,0.0,1.0,0.0,0.0,1.0,53750.0,6.0,0.0,0.0,0.0,0.0,629.0,3.0,620.0,5.0,0.0,0.0,0.0,1.0,0.0,66,010,950300,58289.0
+3,"Census Tract 9504.01, Guam, Guam",5809.0,669.0,581.0,5273.0,1447.0,150.0,1191.0,141.0,151.0,2096.0,53309.0,5688.0,455.0,322.0,376.0,2836.0,5809.0,1592.0,3775.0,54.0,14.0,9.0,3.0,359.0,70.0,66,010,950401,58289.0
+4,"Census Tract 9504.02, Guam, Guam",6606.0,735.0,668.0,5972.0,1581.0,168.0,1234.0,157.0,145.0,2198.0,50980.0,6579.0,834.0,472.0,531.0,2759.0,6606.0,2722.0,3135.0,122.0,32.0,0.0,13.0,574.0,101.0,66,010,950402,58289.0
+5,"Census Tract 9505.01, Guam, Guam",1834.0,211.0,188.0,1638.0,398.0,45.0,295.0,48.0,35.0,588.0,42054.0,1803.0,309.0,166.0,171.0,646.0,1834.0,1060.0,560.0,23.0,2.0,0.0,1.0,186.0,39.0,66,010,950501,58289.0
+6,"Census Tract 9505.02, Guam, Guam",5343.0,660.0,577.0,4952.0,1387.0,115.0,1052.0,86.0,160.0,1932.0,55833.0,5285.0,532.0,206.0,297.0,2911.0,5343.0,1833.0,2813.0,183.0,46.0,2.0,36.0,421.0,110.0,66,010,950502,58289.0
+7,"Census Tract 9507.01, Guam, Guam",5213.0,549.0,535.0,4849.0,1327.0,75.0,1088.0,86.0,162.0,1927.0,66023.0,5187.0,316.0,190.0,224.0,3283.0,5213.0,1279.0,2776.0,448.0,64.0,12.0,50.0,556.0,134.0,66,010,950701,58289.0
+8,"Census Tract 9507.02, Guam, Guam",4020.0,485.0,449.0,3640.0,930.0,89.0,742.0,61.0,102.0,1338.0,56406.0,3978.0,513.0,294.0,235.0,1894.0,4020.0,2093.0,1426.0,61.0,17.0,3.0,2.0,408.0,44.0,66,010,950702,58289.0
+9,"Census Tract 9508.01, Guam, Guam",3921.0,503.0,470.0,3607.0,932.0,127.0,836.0,124.0,66.0,1365.0,51528.0,3898.0,545.0,217.0,210.0,1804.0,3921.0,2058.0,1539.0,36.0,8.0,2.0,4.0,273.0,36.0,66,010,950801,58289.0
+10,"Census Tract 9508.02, Guam, Guam",4110.0,483.0,550.0,3846.0,894.0,102.0,746.0,111.0,97.0,1580.0,35372.0,4099.0,691.0,292.0,375.0,1531.0,4110.0,2055.0,1629.0,51.0,3.0,2.0,14.0,352.0,56.0,66,010,950802,58289.0
+11,"Census Tract 9509, Guam, Guam",4653.0,531.0,506.0,4404.0,1149.0,94.0,1014.0,94.0,98.0,1837.0,53221.0,4638.0,386.0,196.0,284.0,2672.0,4653.0,1048.0,3251.0,40.0,2.0,5.0,16.0,291.0,57.0,66,010,950900,58289.0
+12,"Census Tract 9510, Guam, Guam",3449.0,441.0,400.0,3240.0,821.0,74.0,739.0,57.0,105.0,1277.0,51806.0,3400.0,325.0,175.0,219.0,1743.0,3449.0,1137.0,1993.0,37.0,4.0,3.0,3.0,271.0,50.0,66,010,951000,58289.0
+13,"Census Tract 9511, Guam, Guam",6498.0,775.0,751.0,6108.0,1745.0,98.0,1498.0,134.0,216.0,2433.0,55673.0,6472.0,517.0,279.0,356.0,3630.0,6498.0,1974.0,3791.0,155.0,26.0,9.0,23.0,511.0,118.0,66,010,951100,58289.0
+14,"Census Tract 9516, Guam, Guam",142.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,142.0,111.0,16.0,3.0,4.0,0.0,1.0,7.0,3.0,66,010,951600,58289.0
+15,"Census Tract 9517, Guam, Guam",2239.0,193.0,198.0,1910.0,523.0,24.0,442.0,40.0,57.0,756.0,78333.0,2014.0,138.0,46.0,97.0,1387.0,2239.0,779.0,993.0,149.0,21.0,4.0,22.0,269.0,57.0,66,010,951700,58289.0
+16,"Census Tract 9518, Guam, Guam",99.0,18.0,13.0,83.0,27.0,1.0,18.0,3.0,1.0,33.0,50000.0,92.0,2.0,5.0,3.0,36.0,99.0,80.0,14.0,0.0,0.0,0.0,0.0,5.0,0.0,66,010,951800,58289.0
+17,"Census Tract 9519.01, Guam, Guam",4081.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,4081.0,797.0,2733.0,197.0,34.0,5.0,24.0,287.0,47.0,66,010,951901,58289.0
+18,"Census Tract 9519.02, Guam, Guam",3484.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,3484.0,575.0,2057.0,456.0,52.0,6.0,48.0,262.0,108.0,66,010,951902,58289.0
+19,"Census Tract 9522, Guam, Guam",3273.0,322.0,333.0,3102.0,900.0,84.0,749.0,105.0,58.0,1282.0,49355.0,3253.0,267.0,161.0,185.0,1805.0,3273.0,912.0,1845.0,125.0,26.0,1.0,13.0,349.0,47.0,66,010,952200,58289.0
+20,"Census Tract 9523, Guam, Guam",2829.0,250.0,289.0,2639.0,776.0,67.0,684.0,71.0,54.0,1080.0,59083.0,2811.0,233.0,141.0,128.0,1763.0,2829.0,848.0,1355.0,242.0,29.0,11.0,27.0,307.0,68.0,66,010,952300,58289.0
+21,"Census Tract 9524, Guam, Guam",1637.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,1637.0,614.0,737.0,72.0,16.0,0.0,18.0,176.0,46.0,66,010,952400,58289.0
+22,"Census Tract 9527, Guam, Guam",4468.0,537.0,542.0,3973.0,1016.0,78.0,874.0,68.0,117.0,1550.0,67578.0,4302.0,367.0,183.0,208.0,2623.0,4468.0,2929.0,823.0,253.0,19.0,0.0,11.0,423.0,136.0,66,010,952700,58289.0
+23,"Census Tract 9528, Guam, Guam",,,,,,,,,,,,,,,,,,,,,,,,,,66,010,952800,
+24,"Census Tract 9529, Guam, Guam",5145.0,599.0,604.0,4121.0,1096.0,96.0,971.0,99.0,132.0,1631.0,62031.0,4430.0,349.0,125.0,215.0,2754.0,5145.0,3508.0,779.0,234.0,27.0,9.0,41.0,540.0,133.0,66,010,952900,58289.0
+25,"Census Tract 9530, Guam, Guam",3388.0,359.0,419.0,3023.0,775.0,85.0,742.0,100.0,112.0,1222.0,49605.0,3251.0,360.0,178.0,189.0,1777.0,3388.0,1990.0,747.0,157.0,16.0,0.0,22.0,453.0,98.0,66,010,953000,58289.0
+26,"Census Tract 9531.01, Guam, Guam",3966.0,530.0,500.0,3720.0,1029.0,110.0,889.0,75.0,111.0,1435.0,76413.0,3947.0,250.0,133.0,202.0,2659.0,3966.0,2585.0,551.0,236.0,15.0,1.0,20.0,544.0,171.0,66,010,953101,58289.0
+27,"Census Tract 9531.02, Guam, Guam",3098.0,362.0,380.0,2885.0,776.0,50.0,637.0,80.0,107.0,1078.0,67841.0,3074.0,196.0,114.0,161.0,1948.0,3098.0,1891.0,609.0,122.0,16.0,1.0,13.0,445.0,116.0,66,010,953102,58289.0
+28,"Census Tract 9532, Guam, Guam",2611.0,351.0,356.0,2410.0,614.0,72.0,577.0,77.0,48.0,960.0,67813.0,2597.0,286.0,80.0,137.0,1612.0,2611.0,1725.0,343.0,118.0,28.0,3.0,5.0,385.0,105.0,66,010,953200,58289.0
+29,"Census Tract 9533, Guam, Guam",3808.0,461.0,449.0,3478.0,902.0,85.0,769.0,94.0,78.0,1352.0,47419.0,3764.0,499.0,256.0,243.0,1889.0,3808.0,2268.0,910.0,137.0,18.0,0.0,11.0,452.0,99.0,66,010,953300,58289.0
+30,"Census Tract 9534, Guam, Guam",943.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,943.0,609.0,120.0,61.0,10.0,0.0,9.0,129.0,38.0,66,010,953400,58289.0
+31,"Census Tract 9535, Guam, Guam",,,,,,,,,,,,,,,,,,,,,,,,,,66,010,953500,
+32,"Census Tract 9536, Guam, Guam",3616.0,418.0,408.0,3349.0,925.0,87.0,773.0,78.0,103.0,1295.0,71711.0,3574.0,278.0,171.0,104.0,2372.0,3616.0,2403.0,427.0,144.0,16.0,5.0,16.0,596.0,278.0,66,010,953600,58289.0
+33,"Census Tract 9539, Guam, Guam",4192.0,558.0,522.0,3831.0,946.0,97.0,789.0,74.0,85.0,1456.0,62386.0,4174.0,560.0,256.0,232.0,2215.0,4192.0,3101.0,328.0,176.0,3.0,5.0,39.0,532.0,137.0,66,010,953900,58289.0
+34,"Census Tract 9540, Guam, Guam",2106.0,259.0,234.0,1961.0,527.0,29.0,446.0,39.0,61.0,732.0,83889.0,2102.0,111.0,58.0,88.0,1447.0,2106.0,1283.0,277.0,207.0,11.0,1.0,16.0,308.0,82.0,66,010,954000,58289.0
+35,"Census Tract 9543, Guam, Guam",1532.0,153.0,172.0,1405.0,426.0,41.0,325.0,39.0,42.0,534.0,77125.0,1526.0,131.0,63.0,58.0,993.0,1532.0,932.0,160.0,160.0,10.0,3.0,14.0,237.0,61.0,66,010,954300,58289.0
+36,"Census Tract 9544, Guam, Guam",53.0,7.0,4.0,48.0,14.0,4.0,10.0,2.0,0.0,19.0,92500.0,50.0,8.0,0.0,1.0,32.0,53.0,39.0,4.0,1.0,0.0,0.0,0.0,9.0,0.0,66,010,954400,58289.0
+37,"Census Tract 9545, Guam, Guam",2483.0,9.0,7.0,69.0,20.0,1.0,14.0,3.0,3.0,21.0,40000.0,83.0,12.0,15.0,3.0,28.0,2483.0,71.0,203.0,1444.0,257.0,35.0,98.0,219.0,324.0,66,010,954500,58289.0
+38,"Census Tract 9547, Guam, Guam",2089.0,287.0,255.0,1910.0,497.0,71.0,407.0,48.0,52.0,729.0,54931.0,2061.0,238.0,108.0,95.0,1129.0,2089.0,1259.0,485.0,111.0,30.0,1.0,3.0,197.0,53.0,66,010,954700,58289.0
+39,"Census Tract 9548, Guam, Guam",2426.0,288.0,273.0,2183.0,472.0,99.0,444.0,83.0,31.0,833.0,47969.0,2416.0,504.0,186.0,173.0,979.0,2426.0,1638.0,489.0,69.0,5.0,0.0,2.0,217.0,68.0,66,010,954800,58289.0
+40,"Census Tract 9551, Guam, Guam",3550.0,514.0,473.0,3285.0,873.0,78.0,677.0,80.0,69.0,1200.0,80909.0,3518.0,265.0,165.0,162.0,2364.0,3550.0,2554.0,168.0,340.0,28.0,5.0,25.0,409.0,175.0,66,010,955100,58289.0
+41,"Census Tract 9552, Guam, Guam",2317.0,367.0,322.0,2116.0,529.0,56.0,416.0,39.0,31.0,799.0,67813.0,2295.0,252.0,105.0,149.0,1358.0,2317.0,2016.0,68.0,59.0,7.0,0.0,3.0,163.0,90.0,66,010,955200,58289.0
+42,"Census Tract 9553, Guam, Guam",1604.0,230.0,235.0,1481.0,305.0,50.0,264.0,28.0,25.0,582.0,51667.0,1600.0,267.0,82.0,84.0,726.0,1604.0,1362.0,43.0,65.0,4.0,0.0,1.0,129.0,50.0,66,010,955300,58289.0
+43,"Census Tract 9554, Guam, Guam",647.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,-999999999.0,647.0,565.0,10.0,16.0,0.0,2.0,3.0,51.0,24.0,66,010,955400,58289.0
+44,"Census Tract 9556, Guam, Guam",1008.0,125.0,118.0,940.0,267.0,11.0,209.0,11.0,22.0,355.0,81250.0,1001.0,63.0,36.0,48.0,640.0,1008.0,683.0,168.0,37.0,8.0,1.0,4.0,106.0,24.0,66,010,955600,58289.0
+45,"Census Tract 9557, Guam, Guam",4940.0,625.0,550.0,4642.0,1263.0,132.0,1059.0,105.0,139.0,1803.0,63352.0,4901.0,456.0,235.0,198.0,2988.0,4940.0,2251.0,2010.0,146.0,22.0,2.0,8.0,497.0,95.0,66,010,955700,58289.0
+46,"Census Tract 9558, Guam, Guam",7804.0,920.0,801.0,7224.0,1979.0,153.0,1695.0,167.0,267.0,2723.0,66349.0,7738.0,475.0,326.0,410.0,4635.0,7804.0,2631.0,3870.0,393.0,71.0,16.0,48.0,759.0,179.0,66,010,955800,58289.0
+47,"Census Tract 9559, Guam, Guam",3185.0,322.0,324.0,3023.0,1011.0,65.0,740.0,66.0,99.0,1230.0,64833.0,3150.0,237.0,90.0,119.0,2201.0,3185.0,650.0,1461.0,562.0,50.0,28.0,39.0,389.0,139.0,66,010,955900,58289.0
+48,"Census Tract 9560, Guam, Guam",2572.0,260.0,285.0,2256.0,536.0,46.0,481.0,77.0,42.0,855.0,59375.0,2524.0,337.0,208.0,179.0,1227.0,2572.0,1693.0,419.0,159.0,5.0,3.0,20.0,259.0,60.0,66,010,956000,58289.0
+49,"Census Tract 9561, Guam, Guam",2011.0,218.0,236.0,1846.0,528.0,50.0,425.0,36.0,53.0,721.0,77500.0,1981.0,150.0,98.0,84.0,1301.0,2011.0,1306.0,239.0,169.0,12.0,1.0,16.0,263.0,69.0,66,010,956100,58289.0
+50,"Census Tract 9562, Guam, Guam",3945.0,469.0,420.0,3270.0,797.0,69.0,747.0,68.0,66.0,1255.0,77455.0,3488.0,220.0,71.0,122.0,2406.0,3945.0,1911.0,833.0,643.0,20.0,2.0,35.0,471.0,158.0,66,010,956200,58289.0
+51,"Census Tract 9563, Guam, Guam",2385.0,278.0,250.0,2173.0,592.0,54.0,429.0,64.0,58.0,822.0,57031.0,2346.0,242.0,140.0,121.0,1133.0,2385.0,1316.0,748.0,112.0,7.0,2.0,14.0,181.0,51.0,66,010,956300,58289.0
+52,"Census Tract 9801, Guam, Guam",42.0,7.0,9.0,41.0,12.0,0.0,11.0,2.0,1.0,17.0,60000.0,42.0,6.0,0.0,3.0,28.0,42.0,26.0,7.0,0.0,0.0,0.0,0.0,9.0,0.0,66,010,980100,58289.0
+53,"Census Tract 9802, Guam, Guam",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,66,010,980200,
+54,"Census Tract 9803, Guam, Guam",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,66,010,980300,
+55,"Census Tract 9804, Guam, Guam",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,66,010,980400,
+56,"Census Tract 9900, Guam, Guam",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,66,010,990000,
+0,"Census Tract 9501, Northern Islands Municipality, Commonwealth of the Northern Mariana Islands",7.0,2.0,0.0,7.0,5.0,0.0,0.0,0.0,2.0,0.0,13125.0,7.0,2.0,0.0,1.0,0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,085,950100,31362.0
+0,"Census Tract 9501, Rota Municipality, Commonwealth of the Northern Mariana Islands",1893.0,347.0,252.0,1810.0,514.0,96.0,374.0,75.0,109.0,119.0,31289.0,1891.0,357.0,175.0,157.0,548.0,1893.0,1212.0,557.0,27.0,1.0,0.0,3.0,92.0,17.0,69,100,950100,31362.0
+1,"Census Tract 9900, Rota Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,100,990000,
+0,"Census Tract 1, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1206.0,161.0,143.0,1164.0,342.0,40.0,231.0,37.0,112.0,65.0,42000.0,1203.0,130.0,86.0,121.0,427.0,1206.0,613.0,413.0,41.0,1.0,1.0,1.0,136.0,10.0,69,110,000100,31362.0
+1,"Census Tract 2, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1399.0,193.0,148.0,1340.0,328.0,39.0,229.0,39.0,91.0,86.0,31250.0,1394.0,280.0,162.0,189.0,362.0,1399.0,817.0,454.0,36.0,0.0,1.0,3.0,88.0,6.0,69,110,000200,31362.0
+2,"Census Tract 3, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1616.0,189.0,158.0,1551.0,478.0,53.0,365.0,62.0,93.0,117.0,48281.0,1602.0,147.0,115.0,114.0,754.0,1616.0,497.0,856.0,103.0,3.0,1.0,8.0,148.0,33.0,69,110,000300,31362.0
+3,"Census Tract 4, Saipan Municipality, Commonwealth of the Northern Mariana Islands",3096.0,477.0,364.0,2993.0,980.0,123.0,651.0,92.0,140.0,122.0,24056.0,3087.0,423.0,325.0,508.0,622.0,3096.0,457.0,2462.0,47.0,5.0,2.0,8.0,114.0,15.0,69,110,000400,31362.0
+4,"Census Tract 5, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2448.0,311.0,269.0,2367.0,645.0,79.0,442.0,61.0,180.0,154.0,30431.0,2440.0,345.0,243.0,307.0,749.0,2448.0,799.0,1393.0,67.0,3.0,0.0,7.0,177.0,41.0,69,110,000500,31362.0
+5,"Census Tract 6, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2739.0,337.0,240.0,2633.0,830.0,77.0,562.0,67.0,209.0,170.0,31250.0,2728.0,448.0,181.0,381.0,826.0,2739.0,801.0,1736.0,44.0,2.0,2.0,1.0,153.0,28.0,69,110,000600,31362.0
+6,"Census Tract 7, Saipan Municipality, Commonwealth of the Northern Mariana Islands",3071.0,508.0,308.0,2937.0,949.0,67.0,599.0,61.0,171.0,176.0,26417.0,3066.0,504.0,377.0,406.0,701.0,3071.0,973.0,1919.0,33.0,2.0,0.0,3.0,140.0,17.0,69,110,000700,31362.0
+7,"Census Tract 8, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1840.0,224.0,183.0,1771.0,494.0,77.0,315.0,53.0,133.0,104.0,23693.0,1657.0,308.0,200.0,224.0,289.0,1840.0,576.0,1131.0,39.0,1.0,0.0,4.0,87.0,5.0,69,110,000800,31362.0
+8,"Census Tract 9, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2967.0,438.0,295.0,2870.0,838.0,138.0,520.0,108.0,183.0,172.0,23365.0,2957.0,651.0,395.0,384.0,506.0,2967.0,947.0,1871.0,15.0,2.0,0.0,0.0,132.0,18.0,69,110,000900,31362.0
+9,"Census Tract 10, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2566.0,335.0,232.0,2446.0,733.0,89.0,487.0,73.0,153.0,156.0,32500.0,2564.0,372.0,255.0,344.0,797.0,2566.0,906.0,1365.0,32.0,6.0,2.0,4.0,249.0,26.0,69,110,001000,31362.0
+10,"Census Tract 11, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1597.0,248.0,178.0,1536.0,471.0,48.0,294.0,50.0,111.0,84.0,23015.0,1594.0,375.0,169.0,187.0,341.0,1597.0,392.0,1099.0,19.0,3.0,0.0,1.0,83.0,14.0,69,110,001100,31362.0
+11,"Census Tract 12, Saipan Municipality, Commonwealth of the Northern Mariana Islands",1900.0,321.0,214.0,1835.0,535.0,100.0,327.0,76.0,130.0,101.0,21250.0,1895.0,455.0,207.0,268.0,373.0,1900.0,645.0,1158.0,15.0,0.0,0.0,0.0,82.0,2.0,69,110,001200,31362.0
+12,"Census Tract 13, Saipan Municipality, Commonwealth of the Northern Mariana Islands",3236.0,434.0,331.0,3119.0,738.0,117.0,521.0,105.0,234.0,229.0,27850.0,3225.0,777.0,351.0,379.0,746.0,3236.0,1702.0,1264.0,23.0,0.0,0.0,5.0,242.0,44.0,69,110,001300,31362.0
+13,"Census Tract 14, Saipan Municipality, Commonwealth of the Northern Mariana Islands",4266.0,565.0,478.0,4081.0,1007.0,132.0,753.0,129.0,311.0,261.0,36086.0,4263.0,674.0,426.0,487.0,1265.0,4266.0,2577.0,1282.0,46.0,8.0,0.0,2.0,350.0,86.0,69,110,001400,31362.0
+14,"Census Tract 15, Saipan Municipality, Commonwealth of the Northern Mariana Islands",3306.0,458.0,356.0,3172.0,909.0,88.0,689.0,89.0,194.0,196.0,37550.0,3296.0,456.0,280.0,289.0,1196.0,3306.0,1506.0,1358.0,136.0,12.0,1.0,4.0,282.0,82.0,69,110,001500,31362.0
+15,"Census Tract 16, Saipan Municipality, Commonwealth of the Northern Mariana Islands",4027.0,569.0,496.0,3816.0,952.0,139.0,748.0,141.0,326.0,315.0,43073.0,4022.0,653.0,309.0,310.0,1425.0,4027.0,3049.0,469.0,36.0,3.0,0.0,3.0,460.0,30.0,69,110,001600,31362.0
+16,"Census Tract 17, Saipan Municipality, Commonwealth of the Northern Mariana Islands",2105.0,264.0,232.0,2020.0,516.0,55.0,417.0,53.0,126.0,107.0,54554.0,2102.0,226.0,103.0,154.0,1115.0,2105.0,1156.0,497.0,204.0,11.0,2.0,5.0,225.0,55.0,69,110,001700,31362.0
+17,"Census Tract 9900, Saipan Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,110,990000,
+0,"Census Tract 9501.01, Tinian Municipality, Commonwealth of the Northern Mariana Islands",1250.0,230.0,149.0,1207.0,402.0,52.0,262.0,33.0,62.0,54.0,31771.0,1249.0,245.0,124.0,114.0,346.0,1250.0,535.0,589.0,9.0,0.0,0.0,3.0,113.0,21.0,69,120,950101,31362.0
+1,"Census Tract 9501.02, Tinian Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,120,950102,
+2,"Census Tract 9502.01, Tinian Municipality, Commonwealth of the Northern Mariana Islands",794.0,119.0,95.0,763.0,220.0,28.0,171.0,27.0,38.0,51.0,41875.0,793.0,98.0,73.0,70.0,329.0,794.0,498.0,181.0,13.0,2.0,0.0,0.0,100.0,4.0,69,120,950201,31362.0
+3,"Census Tract 9502.02, Tinian Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,120,950202,
+4,"Census Tract 9900, Tinian Municipality, Commonwealth of the Northern Mariana Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,69,120,990000,
+0,"Census Tract 9704, St. Croix Island, United States Virgin Islands",3983.0,666.0,538.0,3362.0,923.0,47.0,881.0,33.0,33.0,82.0,56402.0,3971.0,318.0,71.0,145.0,2742.0,3983.0,0.0,39.0,595.0,2676.0,28.0,430.0,183.0,940.0,78,010,970400,40408.0
+1,"Census Tract 9701, St. Croix Island, United States Virgin Islands",1832.0,197.0,170.0,1583.0,475.0,23.0,410.0,23.0,1.0,78.0,75625.0,1767.0,69.0,31.0,48.0,1411.0,1832.0,0.0,28.0,986.0,534.0,20.0,149.0,94.0,267.0,78,010,970100,40408.0
+2,"Census Tract 9702, St. Croix Island, United States Virgin Islands",2446.0,369.0,361.0,1947.0,536.0,41.0,501.0,27.0,32.0,39.0,33400.0,2366.0,438.0,90.0,147.0,1154.0,2446.0,0.0,18.0,299.0,1816.0,14.0,181.0,102.0,695.0,78,010,970200,40408.0
+3,"Census Tract 9703, St. Croix Island, United States Virgin Islands",3025.0,410.0,459.0,2384.0,638.0,45.0,623.0,50.0,3.0,44.0,34444.0,3002.0,603.0,174.0,219.0,1440.0,3025.0,0.0,17.0,368.0,2081.0,8.0,318.0,215.0,907.0,78,010,970300,40408.0
+4,"Census Tract 9705, St. Croix Island, United States Virgin Islands",4814.0,567.0,541.0,3195.0,903.0,84.0,815.0,73.0,20.0,51.0,44196.0,3779.0,371.0,145.0,219.0,2251.0,4814.0,44.0,63.0,530.0,2999.0,31.0,581.0,448.0,1405.0,78,010,970500,40408.0
+5,"Census Tract 9706, St. Croix Island, United States Virgin Islands",2074.0,236.0,195.0,1773.0,570.0,31.0,500.0,27.0,5.0,70.0,62750.0,2074.0,111.0,45.0,86.0,1516.0,2074.0,0.0,59.0,531.0,1089.0,13.0,193.0,170.0,344.0,78,010,970600,40408.0
+6,"Census Tract 9707, St. Croix Island, United States Virgin Islands",1909.0,280.0,269.0,1651.0,435.0,31.0,400.0,27.0,8.0,34.0,40809.0,1896.0,147.0,80.0,109.0,1179.0,1909.0,0.0,5.0,208.0,1393.0,13.0,184.0,105.0,410.0,78,010,970700,40408.0
+7,"Census Tract 9708, St. Croix Island, United States Virgin Islands",3157.0,383.0,397.0,2405.0,585.0,62.0,595.0,91.0,19.0,15.0,29375.0,3086.0,648.0,214.0,244.0,1270.0,3157.0,0.0,5.0,59.0,2181.0,19.0,630.0,230.0,1331.0,78,010,970800,40408.0
+8,"Census Tract 9709, St. Croix Island, United States Virgin Islands",914.0,77.0,151.0,665.0,169.0,18.0,231.0,36.0,6.0,9.0,30385.0,911.0,227.0,48.0,50.0,363.0,914.0,0.0,1.0,40.0,779.0,1.0,67.0,22.0,135.0,78,010,970900,40408.0
+9,"Census Tract 9710, St. Croix Island, United States Virgin Islands",2041.0,270.0,259.0,1690.0,430.0,44.0,404.0,42.0,25.0,33.0,34550.0,2032.0,340.0,128.0,121.0,1018.0,2041.0,0.0,6.0,278.0,1499.0,11.0,118.0,115.0,270.0,78,010,971000,40408.0
+10,"Census Tract 9711, St. Croix Island, United States Virgin Islands",3079.0,399.0,433.0,2317.0,527.0,99.0,627.0,103.0,26.0,35.0,24036.0,3011.0,743.0,265.0,223.0,1087.0,3079.0,0.0,6.0,171.0,2569.0,5.0,203.0,117.0,683.0,78,010,971100,40408.0
+11,"Census Tract 9712, St. Croix Island, United States Virgin Islands",3372.0,546.0,506.0,2835.0,712.0,67.0,622.0,44.0,11.0,31.0,34250.0,3366.0,445.0,176.0,200.0,1800.0,3372.0,0.0,21.0,123.0,2708.0,6.0,266.0,215.0,644.0,78,010,971200,40408.0
+12,"Census Tract 9713, St. Croix Island, United States Virgin Islands",2678.0,361.0,344.0,2191.0,582.0,63.0,536.0,53.0,25.0,33.0,37132.0,2674.0,306.0,145.0,186.0,1397.0,2678.0,0.0,21.0,161.0,2113.0,15.0,231.0,134.0,585.0,78,010,971300,40408.0
+13,"Census Tract 9714, St. Croix Island, United States Virgin Islands",2550.0,297.0,359.0,1759.0,402.0,20.0,467.0,33.0,9.0,14.0,28641.0,2337.0,570.0,110.0,190.0,996.0,2550.0,0.0,6.0,80.0,2067.0,7.0,261.0,98.0,563.0,78,010,971400,40408.0
+14,"Census Tract 9715, St. Croix Island, United States Virgin Islands",3130.0,450.0,436.0,2573.0,707.0,26.0,754.0,36.0,14.0,35.0,45292.0,3124.0,255.0,106.0,205.0,1887.0,3130.0,0.0,20.0,129.0,2603.0,11.0,190.0,171.0,557.0,78,010,971500,40408.0
+15,"Census Tract 9900, St. Croix Island, United States Virgin Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,78,010,990000,
+0,"Census Tract 9501, St. John Island, United States Virgin Islands",1233.0,164.0,130.0,1058.0,322.0,34.0,329.0,32.0,8.0,35.0,52143.0,1228.0,97.0,36.0,63.0,799.0,1233.0,0.0,7.0,570.0,532.0,6.0,37.0,75.0,88.0,78,020,950100,40408.0
+1,"Census Tract 9502, St. John Island, United States Virgin Islands",2648.0,496.0,401.0,2305.0,853.0,72.0,700.0,99.0,25.0,47.0,49100.0,2623.0,301.0,98.0,134.0,1617.0,2648.0,1.0,13.0,777.0,1420.0,19.0,158.0,248.0,444.0,78,020,950200,40408.0
+2,"Census Tract 9900, St. John Island, United States Virgin Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,78,020,990000,
+0,"Census Tract 9601, St. Thomas Island, United States Virgin Islands",3329.0,485.0,449.0,2761.0,856.0,108.0,861.0,118.0,37.0,39.0,40234.0,3313.0,339.0,160.0,200.0,1840.0,3329.0,0.0,11.0,409.0,2403.0,7.0,90.0,402.0,542.0,78,030,960100,40408.0
+1,"Census Tract 9602, St. Thomas Island, United States Virgin Islands",3762.0,556.0,652.0,3101.0,894.0,116.0,871.0,90.0,84.0,22.0,37628.0,3722.0,401.0,175.0,209.0,1959.0,3762.0,0.0,13.0,41.0,3427.0,1.0,62.0,213.0,287.0,78,030,960200,40408.0
+2,"Census Tract 9603, St. Thomas Island, United States Virgin Islands",3117.0,516.0,560.0,2708.0,692.0,61.0,766.0,65.0,56.0,45.0,41746.0,3099.0,253.0,160.0,159.0,1926.0,3117.0,0.0,1.0,19.0,2838.0,1.0,59.0,199.0,248.0,78,030,960300,40408.0
+3,"Census Tract 9604, St. Thomas Island, United States Virgin Islands",3872.0,494.0,547.0,3303.0,992.0,52.0,1068.0,65.0,14.0,99.0,50602.0,3868.0,284.0,100.0,176.0,2548.0,3872.0,3.0,98.0,1000.0,2369.0,24.0,106.0,239.0,333.0,78,030,960400,40408.0
+4,"Census Tract 9605, St. Thomas Island, United States Virgin Islands",5001.0,676.0,580.0,4375.0,1340.0,90.0,1435.0,102.0,14.0,138.0,61962.0,4990.0,253.0,103.0,180.0,3793.0,5001.0,2.0,51.0,1715.0,2608.0,27.0,135.0,430.0,414.0,78,030,960500,40408.0
+5,"Census Tract 9606, St. Thomas Island, United States Virgin Islands",3114.0,402.0,423.0,2538.0,743.0,59.0,741.0,79.0,30.0,53.0,39393.0,3081.0,286.0,145.0,212.0,1658.0,3114.0,0.0,71.0,332.0,2220.0,13.0,158.0,302.0,561.0,78,030,960600,40408.0
+6,"Census Tract 9607, St. Thomas Island, United States Virgin Islands",2900.0,486.0,379.0,2489.0,783.0,77.0,688.0,64.0,23.0,51.0,44375.0,2897.0,273.0,135.0,147.0,1797.0,2900.0,0.0,15.0,488.0,2088.0,10.0,63.0,224.0,263.0,78,030,960700,40408.0
+7,"Census Tract 9608, St. Thomas Island, United States Virgin Islands",3161.0,430.0,428.0,2399.0,687.0,66.0,702.0,88.0,41.0,37.0,41375.0,2837.0,306.0,101.0,207.0,1578.0,3161.0,1.0,33.0,302.0,2351.0,11.0,173.0,278.0,602.0,78,030,960800,40408.0
+8,"Census Tract 9609, St. Thomas Island, United States Virgin Islands",3721.0,449.0,501.0,3049.0,930.0,107.0,826.0,119.0,57.0,66.0,38444.0,3700.0,478.0,239.0,241.0,1959.0,3721.0,0.0,82.0,437.0,2662.0,24.0,90.0,419.0,490.0,78,030,960900,40408.0
+9,"Census Tract 9610, St. Thomas Island, United States Virgin Islands",4386.0,681.0,620.0,3582.0,1095.0,135.0,976.0,156.0,60.0,29.0,31429.0,4276.0,559.0,301.0,317.0,1861.0,4386.0,0.0,85.0,217.0,3437.0,11.0,123.0,491.0,1050.0,78,030,961000,40408.0
+10,"Census Tract 9611, St. Thomas Island, United States Virgin Islands",3780.0,521.0,647.0,3144.0,833.0,114.0,866.0,117.0,24.0,24.0,29135.0,3754.0,511.0,272.0,278.0,1592.0,3780.0,0.0,92.0,84.0,3033.0,5.0,137.0,420.0,623.0,78,030,961100,40408.0
+11,"Census Tract 9612, St. Thomas Island, United States Virgin Islands",2118.0,261.0,280.0,1608.0,436.0,53.0,432.0,69.0,59.0,13.0,28523.0,1982.0,354.0,153.0,131.0,751.0,2118.0,0.0,23.0,87.0,1688.0,10.0,85.0,210.0,394.0,78,030,961200,40408.0
+12,"Census Tract 9900, St. Thomas Island, United States Virgin Islands",0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-666666666.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,78,030,990000,
diff --git a/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py
new file mode 100644
index 00000000..0471733b
--- /dev/null
+++ b/data/data-pipeline/data_pipeline/tests/sources/census_decennial/test_etl.py
@@ -0,0 +1,260 @@
+import pytest
+import pandas as pd
+from pathlib import Path
+from data_pipeline.etl.sources.census_decennial.constants import (
+    DEC_TERRITORY_PARAMS,
+    DEC_FIELD_NAMES,
+    OUTPUT_RACE_FIELDS,
+)
+from data_pipeline.etl.sources.census_decennial.etl import CensusDecennialETL
+from data_pipeline.score import field_names
+
+
+def _check_fields_exist(df: pd.DataFrame, field_names: list):
+    for field in field_names:
+        assert field in df.columns
+
+
+@pytest.fixture
+def territory_params_fixture():
+    return [
+        {
+            "state_abbreviation": "as",
+            "fips": "60",
+            "county_fips": ["010"],
+            "xwalk": DEC_TERRITORY_PARAMS[0]["xwalk"],
+            "median_income": 26352,
+        },
+        {
+            "state_abbreviation": "gu",
+            "fips": "66",
+            "county_fips": ["010"],
+            "xwalk": DEC_TERRITORY_PARAMS[1]["xwalk"],
+            "median_income": 58289,
+        },
+    ]
+
+
+@pytest.fixture
+def extract_path_fixture() -> Path:
+    return Path(__file__).parents[0] / "data/extract"
+
+
+@pytest.fixture
+def transform_path_fixture() -> Path:
+    return Path(__file__).parents[0] / "data/transform"
+
+
+@pytest.fixture
+def imputed_path_fixture() -> Path:
+    return Path(__file__).parents[0] / "data/imputation"
+
+
+@pytest.fixture
+def extracted_data_fixture(
+    transform_path_fixture: pd.DataFrame,
+) -> pd.DataFrame:
+    return pd.read_csv(
+        transform_path_fixture / "usa.csv",
+        # Make sure these columns are string as expected of the original
+        dtype={"state": "object", "county": "object", "tract": "object"},
+    )
+
+
+@pytest.fixture
+def transformed_data_fixture(
+    extracted_data_fixture: pd.DataFrame, imputed_path_fixture: Path
+) -> pd.DataFrame:
+    """Load the test data and call the ETL transform"""
+    dec = CensusDecennialETL()
+    dec.df_all = extracted_data_fixture
+    dec.transform(imputed_path_fixture / "census-us-territory-geojson.json")
+    return dec.df_all
+
+
+###############
+# Extract tests
+###############
+def test_no_files_found(territory_params_fixture):
+    """Test the ETL raises an exception if the file is not found"""
+    dec = CensusDecennialETL()
+    with pytest.raises(FileNotFoundError):
+        dec.extract(
+            use_cached_data_sources=True,
+            test_territory_params=territory_params_fixture,
+            test_path=Path("/path_does_not_exist"),
+        )
+
+
+def test_load_data(extract_path_fixture: Path, territory_params_fixture):
+    """Test the ETL loads and translates the data"""
+    dec = CensusDecennialETL()
+    dec.extract(
+        use_cached_data_sources=True,
+        test_territory_params=territory_params_fixture,
+        test_path=extract_path_fixture,
+    )
+    df = dec.df_all
+    assert len(df) == 64
+    assert len(df.columns) == 30
+    assert df.index.is_unique
+
+    # Columns should not have any census variable names
+    census_vars = list(DEC_TERRITORY_PARAMS[0]["xwalk"].keys()) + list(
+        DEC_TERRITORY_PARAMS[1]["xwalk"].keys()
+    )
+    for var in census_vars:
+        assert var not in df.columns
+
+    # Median income is added for entries with population
+    assert (
+        df.loc[
+            df[field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019] > 0,
+            DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME,
+        ]
+        > 0
+    ).all()
+    assert not (
+        df.loc[
+            df[field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019] == 0,
+            DEC_FIELD_NAMES.TERRITORY_MEDIAN_INCOME,
+        ]
+        > 0
+    ).any()
+
+
+#################
+# Transform tests
+#################
+def test_geo_tract_generation(transformed_data_fixture: pd.DataFrame):
+    result = transformed_data_fixture
+    assert field_names.GEOID_TRACT_FIELD in result.columns
+    assert result[field_names.GEOID_TRACT_FIELD].notnull().all()
+
+    # Grab one GEO ID and test it
+    assert (
+        result[field_names.GEOID_TRACT_FIELD][0]
+        == result["state"][0] + result["county"][0] + result["tract"][0]
+    )
+
+
+def test_merge_tracts(transformed_data_fixture: pd.DataFrame):
+    result = transformed_data_fixture
+    # 69120950200 exists, but the tract split does now
+    assert (
+        result.loc[result[field_names.GEOID_TRACT_FIELD] == "69120950200"]
+        .any()
+        .any()
+    )
+    assert (
+        not result.loc[result[field_names.GEOID_TRACT_FIELD] == "69120950201"]
+        .any()
+        .any()
+    )
+    assert (
+        not result.loc[result[field_names.GEOID_TRACT_FIELD] == "69120950202"]
+        .any()
+        .any()
+    )
+
+
+def test_remove_invalid_values(transformed_data_fixture: pd.DataFrame):
+    numeric_df = transformed_data_fixture.select_dtypes(include="number")
+    assert not (numeric_df < -999).any().any()
+
+
+def test_race_fields(transformed_data_fixture: pd.DataFrame):
+    for race_field_name in OUTPUT_RACE_FIELDS:
+        assert race_field_name in transformed_data_fixture.columns
+        assert any(
+            col.startswith(field_names.PERCENT_PREFIX + race_field_name)
+            for col in transformed_data_fixture.columns
+        )
+
+
+def test_transformation_fields(transformed_data_fixture: pd.DataFrame):
+    _check_fields_exist(
+        transformed_data_fixture,
+        [
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_HIGH_SCHOOL_ED_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2019,
+            field_names.CENSUS_DECENNIAL_AREA_MEDIAN_INCOME_PERCENT_FIELD_2019,
+            DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_POPULATION,
+            DEC_FIELD_NAMES.COLLEGE_ATTENDANCE_PERCENT,
+        ],
+    )
+
+
+##################
+# Imputation tests
+##################
+def test_merge_geojson(transformed_data_fixture: pd.DataFrame):
+    _check_fields_exist(transformed_data_fixture, ["STATEFP10", "COUNTYFP10"])
+
+
+def test_imputation_added(transformed_data_fixture: pd.DataFrame):
+    assert (
+        DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL
+        in transformed_data_fixture.columns
+    )
+
+    # All rows with population > 0 need to have an value (real or imputed)
+    df_has_pop = transformed_data_fixture[
+        transformed_data_fixture[
+            field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019
+        ]
+        > 0
+    ]
+    assert (
+        df_has_pop[
+            DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL
+        ]
+        .notnull()
+        .all()
+    )
+
+    # The imputed value equals the real value when available
+    df_has_real_data = transformed_data_fixture[
+        transformed_data_fixture[
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019
+        ].notnull()
+    ]
+    assert (
+        df_has_real_data[
+            DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL
+        ]
+        == df_has_real_data[
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019
+        ]
+    ).all()
+
+    # The imputed value exists when no real value exists
+    df_missing_data = transformed_data_fixture[
+        transformed_data_fixture[
+            field_names.CENSUS_DECENNIAL_POVERTY_LESS_THAN_200_FPL_FIELD_2019
+        ].isnull()
+    ]
+    assert (
+        df_missing_data[
+            df_missing_data[
+                DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL
+            ].notnull()
+        ][
+            DEC_FIELD_NAMES.IMPUTED_PERCENTAGE_HOUSEHOLDS_BELOW_200_PERC_POVERTY_LEVEL
+        ]
+        .notnull()
+        .all()
+    )
+
+    # Test the imputation flag is set
+    df_missing_no_pop = df_missing_data[
+        df_missing_data[
+            field_names.CENSUS_DECENNIAL_TOTAL_POPULATION_FIELD_2019
+        ]
+        > 0
+    ]
+    assert df_missing_no_pop[
+        field_names.ISLAND_AREAS_IMPUTED_INCOME_FLAG_FIELD
+    ].all()
diff --git a/data/data-pipeline/data_pipeline/utils.py b/data/data-pipeline/data_pipeline/utils.py
index 31b52880..fd5ff5d7 100644
--- a/data/data-pipeline/data_pipeline/utils.py
+++ b/data/data-pipeline/data_pipeline/utils.py
@@ -13,6 +13,7 @@ import requests
 import urllib3
 import yaml
 from data_pipeline.config import settings
+from data_pipeline.constants import LOG_LEVEL
 from data_pipeline.content.schemas.download_schemas import CodebookConfig
 from data_pipeline.content.schemas.download_schemas import CSVConfig
 from data_pipeline.content.schemas.download_schemas import ExcelConfig
@@ -48,7 +49,7 @@ def get_module_logger(module_name: str) -> logging.Logger:
     )
     handler.setFormatter(formatter)
     logger.addHandler(handler)
-    logger.setLevel(logging.INFO)
+    logger.setLevel(LOG_LEVEL)
     logger.propagate = False  # don't send log messages to the parent logger (to avoid duplicate log messages)
     return logger
 
diff --git a/data/data-pipeline/poetry.lock b/data/data-pipeline/poetry.lock
index 79c7f6a5..51b54284 100644
--- a/data/data-pipeline/poetry.lock
+++ b/data/data-pipeline/poetry.lock
@@ -1,78 +1,224 @@
-# This file is automatically @generated by Poetry and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
 
 [[package]]
-name = "ansiwrap"
-version = "0.8.4"
-description = "textwrap, but savvy to ANSI colors and styles"
-category = "dev"
+name = "aiofiles"
+version = "22.1.0"
+description = "File support for asyncio."
 optional = false
-python-versions = "*"
+python-versions = ">=3.7,<4.0"
 files = [
-    {file = "ansiwrap-0.8.4-py2.py3-none-any.whl", hash = "sha256:7b053567c88e1ad9eed030d3ac41b722125e4c1271c8a99ade797faff1f49fb1"},
-    {file = "ansiwrap-0.8.4.zip", hash = "sha256:ca0c740734cde59bf919f8ff2c386f74f9a369818cdc60efe94893d01ea8d9b7"},
+    {file = "aiofiles-22.1.0-py3-none-any.whl", hash = "sha256:1142fa8e80dbae46bb6339573ad4c8c0841358f79c6eb50a493dceca14621bad"},
+    {file = "aiofiles-22.1.0.tar.gz", hash = "sha256:9107f1ca0b2a5553987a94a3c9959fe5b491fdf731389aa5b7b1bd0733e32de6"},
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.4.3"
+description = "Happy Eyeballs for asyncio"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"},
+    {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"},
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.11.7"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "aiohttp-3.11.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8bedb1f6cb919af3b6353921c71281b1491f948ca64408871465d889b4ee1b66"},
+    {file = "aiohttp-3.11.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f5022504adab881e2d801a88b748ea63f2a9d130e0b2c430824682a96f6534be"},
+    {file = "aiohttp-3.11.7-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e22d1721c978a6494adc824e0916f9d187fa57baeda34b55140315fa2f740184"},
+    {file = "aiohttp-3.11.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e993676c71288618eb07e20622572b1250d8713e7e00ab3aabae28cb70f3640d"},
+    {file = "aiohttp-3.11.7-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e13a05db87d3b241c186d0936808d0e4e12decc267c617d54e9c643807e968b6"},
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+    {file = "aiohttp-3.11.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dda3ed0a7869d2fa16aa41f9961ade73aa2c2e3b2fcb0a352524e7b744881889"},
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+    {file = "aiohttp-3.11.7-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:3dd3e7e7c9ef3e7214f014f1ae260892286647b3cf7c7f1b644a568fd410f8ca"},
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-docs = ["furo", "sphinx", "sphinx-notfound-page"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
+dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
+tests = ["hypothesis", "pytest"]
+typing = ["mypy"]
 
 [[package]]
 name = "argon2-cffi-bindings"
 version = "21.2.0"
 description = "Low-level CFFI bindings for Argon2"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -107,113 +253,112 @@ dev = ["cogapp", "pre-commit", "pytest", "wheel"]
 tests = ["pytest"]
 
 [[package]]
-name = "astroid"
-version = "2.11.7"
-description = "An abstract syntax tree for Python with inference support."
-category = "main"
+name = "arrow"
+version = "1.3.0"
+description = "Better dates & times for Python"
 optional = false
-python-versions = ">=3.6.2"
+python-versions = ">=3.8"
 files = [
-    {file = "astroid-2.11.7-py3-none-any.whl", hash = "sha256:86b0a340a512c65abf4368b80252754cda17c02cdbbd3f587dddf98112233e7b"},
-    {file = "astroid-2.11.7.tar.gz", hash = "sha256:bb24615c77f4837c707669d16907331374ae8a964650a66999da3f5ca68dc946"},
+    {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"},
+    {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.7.0"
+types-python-dateutil = ">=2.8.10"
+
+[package.extras]
+doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"]
+test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"]
+
+[[package]]
+name = "astroid"
+version = "2.15.8"
+description = "An abstract syntax tree for Python with inference support."
+optional = false
+python-versions = ">=3.7.2"
+files = [
+    {file = "astroid-2.15.8-py3-none-any.whl", hash = "sha256:1aa149fc5c6589e3d0ece885b4491acd80af4f087baafa3fb5203b113e68cd3c"},
+    {file = "astroid-2.15.8.tar.gz", hash = "sha256:6c107453dffee9055899705de3c9ead36e74119cee151e5a9aaf7f0b0e020a6a"},
 ]
 
 [package.dependencies]
 lazy-object-proxy = ">=1.4.0"
-setuptools = ">=20.0"
-typing-extensions = {version = ">=3.10", markers = "python_version < \"3.10\""}
-wrapt = ">=1.11,<2"
+typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
+wrapt = [
+    {version = ">=1.11,<2", markers = "python_version < \"3.11\""},
+    {version = ">=1.14,<2", markers = "python_version >= \"3.11\""},
+]
 
 [[package]]
 name = "asttokens"
-version = "2.2.1"
+version = "2.4.1"
 description = "Annotate AST trees with source code positions"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "asttokens-2.2.1-py2.py3-none-any.whl", hash = "sha256:6b0ac9e93fb0335014d382b8fa9b3afa7df546984258005da0b9e7095b3deb1c"},
-    {file = "asttokens-2.2.1.tar.gz", hash = "sha256:4622110b2a6f30b77e1473affaa97e711bc2f07d3f10848420ff1898edbe94f3"},
+    {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
+    {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
 ]
 
 [package.dependencies]
-six = "*"
+six = ">=1.12.0"
 
 [package.extras]
-test = ["astroid", "pytest"]
-
-[[package]]
-name = "atomicwrites"
-version = "1.4.1"
-description = "Atomic file writes."
-category = "dev"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-files = [
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-]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
 
 [[package]]
 name = "attrs"
-version = "22.1.0"
+version = "24.2.0"
 description = "Classes Without Boilerplate"
-category = "main"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.7"
 files = [
-    {file = "attrs-22.1.0-py2.py3-none-any.whl", hash = "sha256:86efa402f67bf2df34f51a335487cf46b1ec130d02b8d39fd248abfd30da551c"},
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+    {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
+    {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
 ]
 
 [package.extras]
-dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy (>=0.900,!=0.940)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "sphinx", "sphinx-notfound-page", "zope.interface"]
-docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"]
-tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "zope.interface"]
-tests-no-zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins"]
+benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "babel"
-version = "2.10.3"
+version = "2.16.0"
 description = "Internationalization utilities"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "Babel-2.10.3-py3-none-any.whl", hash = "sha256:ff56f4892c1c4bf0d814575ea23471c230d544203c7748e8c68f0089478d48eb"},
-    {file = "Babel-2.10.3.tar.gz", hash = "sha256:7614553711ee97490f732126dc077f8d0ae084ebc6a96e23db1482afabdb2c51"},
+    {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"},
+    {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"},
 ]
 
-[package.dependencies]
-pytz = ">=2015.7"
-
-[[package]]
-name = "backcall"
-version = "0.2.0"
-description = "Specifications for callback functions passed in to an API"
-category = "main"
-optional = false
-python-versions = "*"
-files = [
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-    {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
-]
+[package.extras]
+dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.11.1"
+version = "4.12.3"
 description = "Screen-scraping library"
-category = "main"
 optional = false
 python-versions = ">=3.6.0"
 files = [
-    {file = "beautifulsoup4-4.11.1-py3-none-any.whl", hash = "sha256:58d5c3d29f5a36ffeb94f02f0d786cd53014cf9b3b3951d42e0080d8a9498d30"},
-    {file = "beautifulsoup4-4.11.1.tar.gz", hash = "sha256:ad9aa55b65ef2808eb405f46cf74df7fcb7044d5cbc26487f96eb2ef2e436693"},
+    {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"},
+    {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"},
 ]
 
 [package.dependencies]
 soupsieve = ">1.2"
 
 [package.extras]
+cchardet = ["cchardet"]
+chardet = ["chardet"]
+charset-normalizer = ["charset-normalizer"]
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
@@ -221,7 +366,6 @@ lxml = ["lxml"]
 name = "black"
 version = "21.12b0"
 description = "The uncompromising code formatter."
-category = "dev"
 optional = false
 python-versions = ">=3.6.2"
 files = [
@@ -235,10 +379,7 @@ mypy-extensions = ">=0.4.3"
 pathspec = ">=0.9.0,<1"
 platformdirs = ">=2"
 tomli = ">=0.2.6,<2.0.0"
-typing-extensions = [
-    {version = ">=3.10.0.0", markers = "python_version < \"3.10\""},
-    {version = ">=3.10.0.0,<3.10.0.1 || >3.10.0.1", markers = "python_version >= \"3.10\""},
-]
+typing-extensions = {version = ">=3.10.0.0,<3.10.0.1 || >3.10.0.1", markers = "python_version >= \"3.10\""}
 
 [package.extras]
 colorama = ["colorama (>=0.4.3)"]
@@ -249,29 +390,25 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "bleach"
-version = "5.0.1"
+version = "6.2.0"
 description = "An easy safelist-based HTML-sanitizing tool."
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 files = [
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+    {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
+    {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
 ]
 
 [package.dependencies]
-six = ">=1.9.0"
 webencodings = "*"
 
 [package.extras]
-css = ["tinycss2 (>=1.1.0,<1.2)"]
-dev = ["Sphinx (==4.3.2)", "black (==22.3.0)", "build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "mypy (==0.961)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)"]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
 
 [[package]]
 name = "censusdata"
 version = "1.15.post1"
 description = "Download data from U.S. Census API"
-category = "main"
 optional = false
 python-versions = ">=2.7"
 files = [
@@ -284,88 +421,89 @@ requests = "*"
 
 [[package]]
 name = "certifi"
-version = "2022.12.7"
+version = "2024.8.30"
 description = "Python package for providing Mozilla's CA Bundle."
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
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+    {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
+    {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
 ]
 
 [[package]]
 name = "cffi"
-version = "1.15.1"
+version = "1.17.1"
 description = "Foreign Function Interface for Python calling C code."
-category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -373,24 +511,122 @@ pycparser = "*"
 
 [[package]]
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 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-category = "main"
 optional = false
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-
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 version = "8.0.4"
 description = "Composable command line interface toolkit"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -405,7 +641,6 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 name = "click-plugins"
 version = "1.1.1"
 description = "An extension module for click to enable registering CLI commands via setuptools entry-points."
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 python-versions = "*"
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@@ -423,7 +658,6 @@ dev = ["coveralls", "pytest (>=3.6)", "pytest-cov", "wheel"]
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 description = "Click params for commmand line interfaces to GeoJSON"
-category = "main"
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 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, <4"
 files = [
@@ -439,77 +673,159 @@ test = ["pytest-cov"]
 
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+
+[package.extras]
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+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.11.1)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
 
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 description = "Composable style cycles"
-category = "main"
 optional = false
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 ]
 
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+docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
+tests = ["pytest", "pytest-cov", "pytest-xdist"]
+
 [[package]]
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 description = "An implementation of the Debug Adapter Protocol for Python"
-category = "main"
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+    {file = "debugpy-1.8.8.zip", hash = "sha256:e6355385db85cbd666be703a96ab7351bc9e6c61d694893206f8001e22aee091"},
 ]
 
 [[package]]
 name = "decorator"
 version = "5.1.1"
 description = "Decorators for Humans"
-category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -521,7 +837,6 @@ files = [
 name = "defusedxml"
 version = "0.7.1"
 description = "XML bomb protection for Python stdlib modules"
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 files = [
@@ -531,61 +846,60 @@ files = [
 
 [[package]]
 name = "dill"
-version = "0.3.5.1"
-description = "serialize all of python"
-category = "main"
+version = "0.3.9"
+description = "serialize all of Python"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+python-versions = ">=3.8"
 files = [
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-    {file = "dill-0.3.5.1.tar.gz", hash = "sha256:d75e41f3eff1eee599d738e76ba8f4ad98ea229db8b085318aa2b3333a208c86"},
+    {file = "dill-0.3.9-py3-none-any.whl", hash = "sha256:468dff3b89520b474c0397703366b7b95eebe6303f108adf9b19da1f702be87a"},
+    {file = "dill-0.3.9.tar.gz", hash = "sha256:81aa267dddf68cbfe8029c42ca9ec6a4ab3b22371d1c450abc54422577b4512c"},
 ]
 
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
+profile = ["gprof2dot (>=2022.7.29)"]
 
 [[package]]
 name = "distlib"
-version = "0.3.5"
+version = "0.3.9"
 description = "Distribution utilities"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
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+    {file = "distlib-0.3.9-py2.py3-none-any.whl", hash = "sha256:47f8c22fd27c27e25a65601af709b38e4f0a45ea4fc2e710f65755fa8caaaf87"},
+    {file = "distlib-0.3.9.tar.gz", hash = "sha256:a60f20dea646b8a33f3e7772f74dc0b2d0772d2837ee1342a00645c81edf9403"},
 ]
 
 [[package]]
 name = "dparse"
-version = "0.6.2"
+version = "0.6.4"
 description = "A parser for Python dependency files"
-category = "dev"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.7"
 files = [
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+    {file = "dparse-0.6.4-py3-none-any.whl", hash = "sha256:fbab4d50d54d0e739fbb4dedfc3d92771003a5b9aa8545ca7a7045e3b174af57"},
+    {file = "dparse-0.6.4.tar.gz", hash = "sha256:90b29c39e3edc36c6284c82c4132648eaf28a01863eb3c231c2512196132201a"},
 ]
 
 [package.dependencies]
 packaging = "*"
-toml = "*"
+tomli = {version = "*", markers = "python_version < \"3.11\""}
 
 [package.extras]
+all = ["pipenv", "poetry", "pyyaml"]
 conda = ["pyyaml"]
 pipenv = ["pipenv"]
+poetry = ["poetry"]
 
 [[package]]
 name = "dynaconf"
-version = "3.1.11"
+version = "3.2.6"
 description = "The dynamic configurator for your Python Project"
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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-    {file = "dynaconf-3.1.11.tar.gz", hash = "sha256:d9cfb50fd4a71a543fd23845d4f585b620b6ff6d9d3cc1825c614f7b2097cb39"},
+    {file = "dynaconf-3.2.6-py2.py3-none-any.whl", hash = "sha256:3911c740d717df4576ed55f616c7cbad6e06bc8ef23ffca444b6e2a12fb1c34c"},
+    {file = "dynaconf-3.2.6.tar.gz", hash = "sha256:74cc1897396380bb957730eb341cc0976ee9c38bbcb53d3307c50caed0aedfb8"},
 ]
 
 [package.extras]
@@ -593,7 +907,7 @@ all = ["configobj", "hvac", "redis", "ruamel.yaml"]
 configobj = ["configobj"]
 ini = ["configobj"]
 redis = ["redis"]
-test = ["codecov", "configobj", "django", "flake8", "flake8-debugger", "flake8-print", "flake8-todo", "flask (>=0.12)", "hvac", "pep8-naming", "pytest", "pytest-cov", "pytest-mock", "pytest-xdist", "python-dotenv", "radon", "redis", "toml"]
+test = ["configobj", "django", "flask (>=0.12)", "hvac (>=1.1.0)", "pytest", "pytest-cov", "pytest-mock", "pytest-xdist", "python-dotenv", "radon", "redis", "toml"]
 toml = ["toml"]
 vault = ["hvac"]
 yaml = ["ruamel.yaml"]
@@ -602,7 +916,6 @@ yaml = ["ruamel.yaml"]
 name = "entrypoints"
 version = "0.4"
 description = "Discover and load entry points from installed packages."
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -612,41 +925,52 @@ files = [
 
 [[package]]
 name = "et-xmlfile"
-version = "1.1.0"
+version = "2.0.0"
 description = "An implementation of lxml.xmlfile for the standard library"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "et_xmlfile-1.1.0-py3-none-any.whl", hash = "sha256:a2ba85d1d6a74ef63837eed693bcb89c3f752169b0e3e7ae5b16ca5e1b3deada"},
-    {file = "et_xmlfile-1.1.0.tar.gz", hash = "sha256:8eb9e2bc2f8c97e37a2dc85a09ecdcdec9d8a396530a6d5a33b30b9a92da0c5c"},
+    {file = "et_xmlfile-2.0.0-py3-none-any.whl", hash = "sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa"},
+    {file = "et_xmlfile-2.0.0.tar.gz", hash = "sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54"},
 ]
 
 [[package]]
-name = "executing"
-version = "1.2.0"
-description = "Get the currently executing AST node of a frame, and other information"
-category = "main"
+name = "exceptiongroup"
+version = "1.2.2"
+description = "Backport of PEP 654 (exception groups)"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 files = [
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 [package.extras]
-tests = ["asttokens", "littleutils", "pytest", "rich"]
+test = ["pytest (>=6)"]
+
+[[package]]
+name = "executing"
+version = "2.1.0"
+description = "Get the currently executing AST node of a frame, and other information"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
+[package.extras]
+tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
 
 [[package]]
 name = "fastjsonschema"
-version = "2.16.1"
+version = "2.20.0"
 description = "Fastest Python implementation of JSON schema"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.extras]
@@ -654,39 +978,46 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 
 [[package]]
 name = "filelock"
-version = "3.8.0"
+version = "3.16.1"
 description = "A platform independent file lock."
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 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+typing = ["typing-extensions (>=4.12.2)"]
 
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 name = "fiona"
-version = "1.8.21"
+version = "1.8.22"
 description = "Fiona reads and writes spatial data files"
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 optional = false
 python-versions = "*"
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 ]
 
 [package.dependencies]
@@ -707,52 +1038,209 @@ test = ["boto3 (>=1.2.4)", "mock", "pytest (>=3)", "pytest-cov"]
 
 [[package]]
 name = "flake8"
-version = "3.9.2"
+version = "7.1.1"
 description = "the modular source code checker: pep8 pyflakes and co"
-category = "dev"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
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+pycodestyle = ">=2.12.0,<2.13.0"
+pyflakes = ">=3.2.0,<3.3.0"
 
 [[package]]
 name = "fonttools"
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+version = "4.55.0"
 description = "Tools to manipulate font files"
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 optional = false
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+]
+
 [[package]]
 name = "geopandas"
 version = "0.11.1"
 description = "Geographic pandas extensions"
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
@@ -769,156 +1257,120 @@ shapely = ">=1.7,<2"
 
 [[package]]
 name = "idna"
-version = "3.3"
+version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
-category = "main"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
 files = [
-    {file = "idna-3.3-py3-none-any.whl", hash = "sha256:84d9dd047ffa80596e0f246e2eab0b391788b0503584e8945f2368256d2735ff"},
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+    {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
+    {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
 ]
 
-[[package]]
-name = "importlib-metadata"
-version = "4.12.0"
-description = "Read metadata from Python packages"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
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-
-[package.dependencies]
-zipp = ">=0.5"
-
 [package.extras]
-docs = ["jaraco.packaging (>=9)", "rst.linker (>=1.9)", "sphinx"]
-perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"]
-
-[[package]]
-name = "importlib-resources"
-version = "5.9.0"
-description = "Read resources from Python packages"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "importlib_resources-5.9.0-py3-none-any.whl", hash = "sha256:f78a8df21a79bcc30cfd400bdc38f314333de7c0fb619763f6b9dabab8268bb7"},
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-
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-docs = ["jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx"]
-testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
 
 [[package]]
 name = "iniconfig"
-version = "1.1.1"
-description = "iniconfig: brain-dead simple config-ini parsing"
-category = "dev"
+version = "2.0.0"
+description = "brain-dead simple config-ini parsing"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
 files = [
-    {file = "iniconfig-1.1.1-py2.py3-none-any.whl", hash = "sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3"},
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+    {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
+    {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
 [[package]]
 name = "ipdb"
-version = "0.13.9"
+version = "0.13.13"
 description = "IPython-enabled pdb"
-category = "main"
 optional = false
-python-versions = ">=2.7"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
-    {file = "ipdb-0.13.9.tar.gz", hash = "sha256:951bd9a64731c444fd907a5ce268543020086a697f6be08f7cc2c9a752a278c5"},
+    {file = "ipdb-0.13.13-py3-none-any.whl", hash = "sha256:45529994741c4ab6d2388bfa5d7b725c2cf7fe9deffabdb8a6113aa5ed449ed4"},
+    {file = "ipdb-0.13.13.tar.gz", hash = "sha256:e3ac6018ef05126d442af680aad863006ec19d02290561ac88b8b1c0b0cfc726"},
 ]
 
 [package.dependencies]
 decorator = {version = "*", markers = "python_version > \"3.6\""}
-ipython = {version = ">=7.17.0", markers = "python_version > \"3.6\""}
-setuptools = "*"
-toml = {version = ">=0.10.2", markers = "python_version > \"3.6\""}
+ipython = {version = ">=7.31.1", markers = "python_version > \"3.6\""}
+tomli = {version = "*", markers = "python_version > \"3.6\" and python_version < \"3.11\""}
 
 [[package]]
 name = "ipykernel"
-version = "6.15.1"
+version = "6.29.5"
 description = "IPython Kernel for Jupyter"
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.15.1-py3-none-any.whl", hash = "sha256:d8969c5b23b0e453a23166da5a669c954db399789293fcb03fec5cb25367e43c"},
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+    {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
+    {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
 ]
 
 [package.dependencies]
 appnope = {version = "*", markers = "platform_system == \"Darwin\""}
-debugpy = ">=1.0"
+comm = ">=0.1.1"
+debugpy = ">=1.6.5"
 ipython = ">=7.23.1"
 jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 matplotlib-inline = ">=0.1"
 nest-asyncio = "*"
 packaging = "*"
 psutil = "*"
-pyzmq = ">=17"
+pyzmq = ">=24"
 tornado = ">=6.1"
-traitlets = ">=5.1.0"
+traitlets = ">=5.4.0"
 
 [package.extras]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=6.0)", "pytest-cov", "pytest-timeout"]
+cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+pyqt5 = ["pyqt5"]
+pyside6 = ["pyside6"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "8.11.0"
+version = "8.29.0"
 description = "IPython: Productive Interactive Computing"
-category = "main"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
 files = [
-    {file = "ipython-8.11.0-py3-none-any.whl", hash = "sha256:5b54478e459155a326bf5f42ee4f29df76258c0279c36f21d71ddb560f88b156"},
-    {file = "ipython-8.11.0.tar.gz", hash = "sha256:735cede4099dbc903ee540307b9171fbfef4aa75cfcacc5a273b2cda2f02be04"},
+    {file = "ipython-8.29.0-py3-none-any.whl", hash = "sha256:0188a1bd83267192123ccea7f4a8ed0a78910535dbaa3f37671dca76ebd429c8"},
+    {file = "ipython-8.29.0.tar.gz", hash = "sha256:40b60e15b22591450eef73e40a027cf77bd652e757523eebc5bd7c7c498290eb"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
+exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
-pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
-traitlets = ">=5"
+traitlets = ">=5.13.0"
+typing-extensions = {version = ">=4.6", markers = "python_version < \"3.12\""}
 
 [package.extras]
-all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
 black = ["black"]
-doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"]
+doc = ["docrepr", "exceptiongroup", "intersphinx-registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing-extensions"]
 kernel = ["ipykernel"]
+matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
 notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
-test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"]
+test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
 name = "ipython-genutils"
 version = "0.2.0"
 description = "Vestigial utilities from IPython"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -928,68 +1380,76 @@ files = [
 
 [[package]]
 name = "ipywidgets"
-version = "8.0.2"
+version = "8.1.5"
 description = "Jupyter interactive widgets"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ipywidgets-8.0.2-py3-none-any.whl", hash = "sha256:1dc3dd4ee19ded045ea7c86eb273033d238d8e43f9e7872c52d092683f263891"},
-    {file = "ipywidgets-8.0.2.tar.gz", hash = "sha256:08cb75c6e0a96836147cbfdc55580ae04d13e05d26ffbc377b4e1c68baa28b1f"},
+    {file = "ipywidgets-8.1.5-py3-none-any.whl", hash = "sha256:3290f526f87ae6e77655555baba4f36681c555b8bdbbff430b70e52c34c86245"},
+    {file = "ipywidgets-8.1.5.tar.gz", hash = "sha256:870e43b1a35656a80c18c9503bbf2d16802db1cb487eec6fab27d683381dde17"},
 ]
 
 [package.dependencies]
-ipykernel = ">=4.5.1"
+comm = ">=0.1.3"
 ipython = ">=6.1.0"
-jupyterlab-widgets = ">=3.0,<4.0"
+jupyterlab-widgets = ">=3.0.12,<3.1.0"
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0,<5.0"
+widgetsnbextension = ">=4.0.12,<4.1.0"
 
 [package.extras]
-test = ["jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
+[[package]]
+name = "isoduration"
+version = "20.11.0"
+description = "Operations with ISO 8601 durations"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
+    {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
+]
+
+[package.dependencies]
+arrow = ">=0.15.0"
 
 [[package]]
 name = "isort"
-version = "5.10.1"
+version = "5.13.2"
 description = "A Python utility / library to sort Python imports."
-category = "main"
 optional = false
-python-versions = ">=3.6.1,<4.0"
+python-versions = ">=3.8.0"
 files = [
-    {file = "isort-5.10.1-py3-none-any.whl", hash = "sha256:6f62d78e2f89b4500b080fe3a81690850cd254227f27f75c3a0c491a1f351ba7"},
-    {file = "isort-5.10.1.tar.gz", hash = "sha256:e8443a5e7a020e9d7f97f1d7d9cd17c88bcb3bc7e218bf9cf5095fe550be2951"},
+    {file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"},
+    {file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"},
 ]
 
 [package.extras]
-colors = ["colorama (>=0.4.3,<0.5.0)"]
-pipfile-deprecated-finder = ["pipreqs", "requirementslib"]
-plugins = ["setuptools"]
-requirements-deprecated-finder = ["pip-api", "pipreqs"]
+colors = ["colorama (>=0.4.6)"]
 
 [[package]]
 name = "jedi"
-version = "0.18.1"
+version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "jedi-0.18.1-py2.py3-none-any.whl", hash = "sha256:637c9635fcf47945ceb91cd7f320234a7be540ded6f3e99a50cb6febdfd1ba8d"},
-    {file = "jedi-0.18.1.tar.gz", hash = "sha256:74137626a64a99c8eb6ae5832d99b3bdd7d29a3850fe2aa80a4126b2a7d949ab"},
+    {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"},
+    {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"},
 ]
 
 [package.dependencies]
-parso = ">=0.8.0,<0.9.0"
+parso = ">=0.8.4,<0.9.0"
 
 [package.extras]
-qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
-testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<7.0.0)"]
+docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jellyfish"
 version = "0.6.1"
 description = "a library for doing approximate and phonetic matching of strings."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -998,14 +1458,13 @@ files = [
 
 [[package]]
 name = "jinja2"
-version = "3.1.2"
+version = "3.1.4"
 description = "A very fast and expressive template engine."
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
-    {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
+    {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
+    {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
 ]
 
 [package.dependencies]
@@ -1016,72 +1475,100 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "json5"
-version = "0.9.9"
+version = "0.9.28"
 description = "A Python implementation of the JSON5 data format."
-category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8.0"
 files = [
-    {file = "json5-0.9.9-py2.py3-none-any.whl", hash = "sha256:1ff8351ee2ae80fd89d64210d9522db7e157516a7b12c72089ded6964527283f"},
-    {file = "json5-0.9.9.tar.gz", hash = "sha256:2ace77117c068c5f1f23f97e530a0d49bc09a46039521b6daa74aa39524e02a2"},
+    {file = "json5-0.9.28-py3-none-any.whl", hash = "sha256:29c56f1accdd8bc2e037321237662034a7e07921e2b7223281a5ce2c46f0c4df"},
+    {file = "json5-0.9.28.tar.gz", hash = "sha256:1f82f36e615bc5b42f1bbd49dbc94b12563c56408c6ffa06414ea310890e9a6e"},
 ]
 
 [package.extras]
-dev = ["hypothesis"]
+dev = ["build (==1.2.2.post1)", "coverage (==7.5.3)", "mypy (==1.13.0)", "pip (==24.3.1)", "pylint (==3.2.3)", "ruff (==0.7.3)", "twine (==5.1.1)", "uv (==0.5.1)"]
 
 [[package]]
-name = "jsonschema"
-version = "4.10.0"
-description = "An implementation of JSON Schema validation for Python"
-category = "main"
+name = "jsonpointer"
+version = "3.0.0"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jsonschema-4.10.0-py3-none-any.whl", hash = "sha256:92128509e5b700bf0f1fd08a7d018252b16a1454465dfa6b899558eeae584241"},
-    {file = "jsonschema-4.10.0.tar.gz", hash = "sha256:8ff7b44c6a99c6bfd55ca9ac45261c649cefd40aaba1124c29aaef1bcb378d84"},
+    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
+    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
+]
+
+[[package]]
+name = "jsonschema"
+version = "4.23.0"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
+    {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
 ]
 
 [package.dependencies]
-attrs = ">=17.4.0"
-importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
-pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
-pyrsistent = ">=0.14.0,<0.17.0 || >0.17.0,<0.17.1 || >0.17.1,<0.17.2 || >0.17.2"
+attrs = ">=22.2.0"
+fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rpds-py = ">=0.7.1"
+uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
 
 [package.extras]
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
-format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2024.10.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
+    {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
+]
+
+[package.dependencies]
+referencing = ">=0.31.0"
 
 [[package]]
 name = "jupyter"
-version = "1.0.0"
+version = "1.1.1"
 description = "Jupyter metapackage. Install all the Jupyter components in one go."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "jupyter-1.0.0-py2.py3-none-any.whl", hash = "sha256:5b290f93b98ffbc21c0c7e749f054b3267782166d72fa5e3ed1ed4eaf34a2b78"},
-    {file = "jupyter-1.0.0.tar.gz", hash = "sha256:d9dc4b3318f310e34c82951ea5d6683f67bed7def4b259fafbfe4f1beb1d8e5f"},
-    {file = "jupyter-1.0.0.zip", hash = "sha256:3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7"},
+    {file = "jupyter-1.1.1-py2.py3-none-any.whl", hash = "sha256:7a59533c22af65439b24bbe60373a4e95af8f16ac65a6c00820ad378e3f7cc83"},
+    {file = "jupyter-1.1.1.tar.gz", hash = "sha256:d55467bceabdea49d7e3624af7e33d59c37fff53ed3a350e1ac957bed731de7a"},
 ]
 
 [package.dependencies]
 ipykernel = "*"
 ipywidgets = "*"
 jupyter-console = "*"
+jupyterlab = "*"
 nbconvert = "*"
 notebook = "*"
-qtconsole = "*"
 
 [[package]]
 name = "jupyter-client"
-version = "7.3.4"
+version = "7.4.9"
 description = "Jupyter protocol implementation and client libraries"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyter_client-7.3.4-py3-none-any.whl", hash = "sha256:17d74b0d0a7b24f1c8c527b24fcf4607c56bee542ffe8e3418e50b21e514b621"},
-    {file = "jupyter_client-7.3.4.tar.gz", hash = "sha256:aa9a6c32054b290374f95f73bb0cae91455c58dfb84f65c8591912b8f65e6d56"},
+    {file = "jupyter_client-7.4.9-py3-none-any.whl", hash = "sha256:214668aaea208195f4c13d28eb272ba79f945fc0cf3f11c7092c20b2ca1980e7"},
+    {file = "jupyter_client-7.4.9.tar.gz", hash = "sha256:52be28e04171f07aed8f20e1616a5a552ab9fee9cbbe6c1896ae170c3880d392"},
 ]
 
 [package.dependencies]
@@ -1090,49 +1577,49 @@ jupyter-core = ">=4.9.2"
 nest-asyncio = ">=1.5.4"
 python-dateutil = ">=2.8.2"
 pyzmq = ">=23.0"
-tornado = ">=6.0"
+tornado = ">=6.2"
 traitlets = "*"
 
 [package.extras]
 doc = ["ipykernel", "myst-parser", "sphinx (>=1.3.6)", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
-test = ["codecov", "coverage", "ipykernel (>=6.5)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
+test = ["codecov", "coverage", "ipykernel (>=6.12)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-console"
-version = "6.4.4"
+version = "6.6.3"
 description = "Jupyter terminal console"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyter_console-6.4.4-py3-none-any.whl", hash = "sha256:756df7f4f60c986e7bc0172e4493d3830a7e6e75c08750bbe59c0a5403ad6dee"},
-    {file = "jupyter_console-6.4.4.tar.gz", hash = "sha256:172f5335e31d600df61613a97b7f0352f2c8250bbd1092ef2d658f77249f89fb"},
+    {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"},
+    {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"},
 ]
 
 [package.dependencies]
-ipykernel = "*"
+ipykernel = ">=6.14"
 ipython = "*"
 jupyter-client = ">=7.0.0"
-prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+prompt-toolkit = ">=3.0.30"
 pygments = "*"
+pyzmq = ">=17"
+traitlets = ">=5.4"
 
 [package.extras]
-test = ["pexpect"]
+test = ["flaky", "pexpect", "pytest"]
 
 [[package]]
 name = "jupyter-contrib-core"
-version = "0.4.0"
+version = "0.4.2"
 description = "Common utilities for jupyter-contrib projects."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "jupyter_contrib_core-0.4.0-py2.py3-none-any.whl", hash = "sha256:5e77cdff5c73f5ff76b3713c459f928c0dff45eee53ee8c2fe3f26490f4d4270"},
-    {file = "jupyter_contrib_core-0.4.0.tar.gz", hash = "sha256:ad995f5754188ab41ea5f94d69c9a1f96ba50543274d798d9001f937f730d326"},
+    {file = "jupyter_contrib_core-0.4.2.tar.gz", hash = "sha256:1887212f3ca9d4487d624c0705c20dfdf03d5a0b9ea2557d3aaeeb4c38bdcabb"},
 ]
 
 [package.dependencies]
-jupyter-core = "*"
+jupyter_core = "*"
 notebook = ">=4.0"
 setuptools = "*"
 tornado = "*"
@@ -1145,7 +1632,6 @@ testing-utils = ["mock", "nose"]
 name = "jupyter-contrib-nbextensions"
 version = "0.5.1"
 description = "A collection of Jupyter nbextensions."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -1172,30 +1658,53 @@ test = ["mock", "nbformat", "nose", "pip", "requests"]
 
 [[package]]
 name = "jupyter-core"
-version = "5.2.0"
+version = "5.7.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
-category = "main"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.2.0-py3-none-any.whl", hash = "sha256:4bdc2928c37f6917130c667d8b8708f20aee539d8283c6be72aabd2a4b4c83b0"},
-    {file = "jupyter_core-5.2.0.tar.gz", hash = "sha256:1407cdb4c79ee467696c04b76633fc1884015fa109323365a6372c8e890cc83f"},
+    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
+    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
 ]
 
 [package.dependencies]
 platformdirs = ">=2.5"
-pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
+pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
 traitlets = ">=5.3"
 
 [package.extras]
-docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
-test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
+test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-events"
+version = "0.10.0"
+description = "Jupyter Event System library"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
+    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
+]
+
+[package.dependencies]
+jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]}
+python-json-logger = ">=2.0.4"
+pyyaml = ">=5.3"
+referencing = "*"
+rfc3339-validator = "*"
+rfc3986-validator = ">=0.1.1"
+traitlets = ">=5.3"
+
+[package.extras]
+cli = ["click", "rich"]
+docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
+test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
 
 [[package]]
 name = "jupyter-highlight-selected-word"
 version = "0.2.0"
 description = "Jupyter notebook extension that enables highlighting every instance of the current word in the notebook."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -1207,7 +1716,6 @@ files = [
 name = "jupyter-latex-envs"
 version = "1.4.6"
 description = "Jupyter notebook extension which supports (some) LaTeX environments within markdown cells. Also provides support for labels and crossreferences, document wide numbering, bibliography, and more..."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -1223,19 +1731,18 @@ traitlets = ">=4.1"
 
 [[package]]
 name = "jupyter-nbextensions-configurator"
-version = "0.5.0"
+version = "0.6.4"
 description = "jupyter serverextension providing configuration interfaces for nbextensions."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "jupyter_nbextensions_configurator-0.5.0-py2.py3-none-any.whl", hash = "sha256:0be87b9d828673f691120a026327e0ff4fa7f1602dc94b00b3b9e48d52391e84"},
-    {file = "jupyter_nbextensions_configurator-0.5.0.tar.gz", hash = "sha256:bdc312c6baed70f6f35b464fa0bca850a266062c486af3e0ff601079e3238ceb"},
+    {file = "jupyter_nbextensions_configurator-0.6.4-py2.py3-none-any.whl", hash = "sha256:fe7a7b0805b5926449692fb077e0e659bab8b27563bc68cba26854532fdf99c7"},
 ]
 
 [package.dependencies]
 jupyter-contrib-core = ">=0.3.3"
 jupyter-core = "*"
+jupyter-server = "*"
 notebook = ">=6.0"
 pyyaml = "*"
 tornado = "*"
@@ -1246,442 +1753,645 @@ test = ["jupyter-contrib-core[testing-utils]", "mock", "nose", "requests", "sele
 
 [[package]]
 name = "jupyter-server"
-version = "1.18.1"
+version = "2.14.2"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
-category = "dev"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-1.18.1-py3-none-any.whl", hash = "sha256:022759b09c96a4e2feb95de59ce4283e04e17782efe197b91d23a47521609b77"},
-    {file = "jupyter_server-1.18.1.tar.gz", hash = "sha256:2b72fc595bccae292260aad8157a0ead8da2c703ec6ae1bb7b36dbad0e267ea7"},
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+typeguard = ">=4.0,<5"
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-enum = ["marshmallow-enum"]
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-union = ["typeguard"]
+tests = ["pytest (>=5.4)", "pytest-mypy-plugins (>=1.2.0)"]
 
 [[package]]
 name = "marshmallow-enum"
 version = "1.5.1"
 description = "Enum field for Marshmallow"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -1694,69 +2404,76 @@ marshmallow = ">=2.0.0"
 
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@@ -1764,52 +2481,146 @@ traitlets = "*"
 
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 ]
 
 [[package]]
 name = "munch"
-version = "2.5.0"
+version = "4.0.0"
 description = "A dot-accessible dictionary (a la JavaScript objects)"
-category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
 files = [
-    {file = "munch-2.5.0-py2.py3-none-any.whl", hash = "sha256:6f44af89a2ce4ed04ff8de41f70b226b984db10a91dcc7b9ac2efc1c77022fdd"},
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+    {file = "munch-4.0.0-py2.py3-none-any.whl", hash = "sha256:71033c45db9fb677a0b7eb517a4ce70ae09258490e419b0e7f00d1e386ecb1b4"},
+    {file = "munch-4.0.0.tar.gz", hash = "sha256:542cb151461263216a4e37c3fd9afc425feeaf38aaa3025cd2a981fadb422235"},
 ]
 
-[package.dependencies]
-six = "*"
-
 [package.extras]
-testing = ["astroid (>=1.5.3,<1.6.0)", "astroid (>=2.0)", "coverage", "pylint (>=1.7.2,<1.8.0)", "pylint (>=2.3.1,<2.4.0)", "pytest"]
+testing = ["astroid (>=2.0)", "coverage", "pylint (>=2.3.1,<2.4.0)", "pytest"]
 yaml = ["PyYAML (>=5.1.0)"]
 
 [[package]]
 name = "mypy"
 version = "0.910"
 description = "Optional static typing for Python"
-category = "dev"
 optional = false
 python-versions = ">=3.5"
 files = [
@@ -1849,157 +2660,136 @@ python2 = ["typed-ast (>=1.4.0,<1.5.0)"]
 
 [[package]]
 name = "mypy-extensions"
-version = "0.4.3"
+version = "0.4.4"
 description = "Experimental type system extensions for programs checked with the mypy typechecker."
-category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=2.7"
 files = [
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 ]
 
 [[package]]
 name = "nbclassic"
-version = "0.4.3"
-description = "A web-based notebook environment for interactive computing"
-category = "dev"
+version = "1.1.0"
+description = "Jupyter Notebook as a Jupyter Server extension."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "nbclassic-0.4.3-py3-none-any.whl", hash = "sha256:4b01076effdac53e775cd1b6a4e891663568b32621468e205b502a23b2921899"},
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+    {file = "nbclassic-1.1.0-py3-none-any.whl", hash = "sha256:8c0fd6e36e320a18657ff44ed96c3a400f17a903a3744fc322303a515778f2ba"},
+    {file = "nbclassic-1.1.0.tar.gz", hash = "sha256:77b77ba85f9e988f9bad85df345b514e9e64c7f0e822992ab1df4a78ac64fc1e"},
 ]
 
 [package.dependencies]
-argon2-cffi = "*"
 ipykernel = "*"
 ipython-genutils = "*"
-jinja2 = "*"
-jupyter-client = ">=6.1.1"
-jupyter-core = ">=4.6.1"
-jupyter-server = ">=1.8"
-nbconvert = ">=5"
-nbformat = "*"
 nest-asyncio = ">=1.5"
-notebook-shim = ">=0.1.0"
-prometheus-client = "*"
-pyzmq = ">=17"
-Send2Trash = ">=1.8.0"
-terminado = ">=0.8.3"
-tornado = ">=6.1"
-traitlets = ">=4.2.1"
+notebook-shim = ">=0.2.3"
 
 [package.extras]
 docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
 json-logging = ["json-logging"]
-test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-tornasync", "requests", "requests-unixsocket", "selenium (==4.1.5)", "testpath"]
+test = ["coverage", "nbval", "pytest", "pytest-cov", "pytest-jupyter", "pytest-playwright", "pytest-tornasync", "requests", "requests-unixsocket", "testpath"]
 
 [[package]]
 name = "nbclient"
-version = "0.6.6"
+version = "0.10.0"
 description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
-category = "main"
 optional = false
-python-versions = ">=3.7.0"
+python-versions = ">=3.8.0"
 files = [
-    {file = "nbclient-0.6.6-py3-none-any.whl", hash = "sha256:09bae4ea2df79fa6bc50aeb8278d8b79d2036792824337fa6eee834afae17312"},
-    {file = "nbclient-0.6.6.tar.gz", hash = "sha256:0df76a7961d99a681b4796c74a1f2553b9f998851acc01896dce064ad19a9027"},
+    {file = "nbclient-0.10.0-py3-none-any.whl", hash = "sha256:f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f"},
+    {file = "nbclient-0.10.0.tar.gz", hash = "sha256:4b3f1b7dba531e498449c4db4f53da339c91d449dc11e9af3a43b4eb5c5abb09"},
 ]
 
 [package.dependencies]
-jupyter-client = ">=6.1.5"
-nbformat = ">=5.0"
-nest-asyncio = "*"
-traitlets = ">=5.2.2"
+jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+nbformat = ">=5.1"
+traitlets = ">=5.4"
 
 [package.extras]
-sphinx = ["Sphinx (>=1.7)", "autodoc-traits", "mock", "moto", "myst-parser", "sphinx-book-theme"]
-test = ["black", "check-manifest", "flake8", "ipykernel", "ipython (<8.0.0)", "ipywidgets (<8.0.0)", "mypy", "pip (>=18.1)", "pre-commit", "pytest (>=4.1)", "pytest-asyncio", "pytest-cov (>=2.6.1)", "setuptools (>=60.0)", "testpath", "twine (>=1.11.0)", "xmltodict"]
+dev = ["pre-commit"]
+docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"]
+test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"]
 
 [[package]]
 name = "nbconvert"
-version = "7.0.0"
-description = "Converting Jupyter Notebooks"
-category = "main"
+version = "7.16.4"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "nbconvert-7.0.0-py3-none-any.whl", hash = "sha256:26843ae233167e8aae31c20e3e1d91f431f04c9f34363bbe2dd0d247f772641c"},
-    {file = "nbconvert-7.0.0.tar.gz", hash = "sha256:fd1e361da30e30e4c5a5ae89f7cae95ca2a4d4407389672473312249a7ba0060"},
+    {file = "nbconvert-7.16.4-py3-none-any.whl", hash = "sha256:05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3"},
+    {file = "nbconvert-7.16.4.tar.gz", hash = "sha256:86ca91ba266b0a448dc96fa6c5b9d98affabde2867b363258703536807f9f7f4"},
 ]
 
 [package.dependencies]
 beautifulsoup4 = "*"
-bleach = "*"
+bleach = "!=5.0.0"
 defusedxml = "*"
-importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
 jinja2 = ">=3.0"
 jupyter-core = ">=4.7"
 jupyterlab-pygments = "*"
-lxml = "*"
 markupsafe = ">=2.0"
-mistune = ">=2.0.3,<3"
+mistune = ">=2.0.3,<4"
 nbclient = ">=0.5.0"
-nbformat = ">=5.1"
+nbformat = ">=5.7"
 packaging = "*"
 pandocfilters = ">=1.4.1"
 pygments = ">=2.4.1"
 tinycss2 = "*"
-traitlets = ">=5.0"
-
-[package.extras]
-all = ["ipykernel", "ipython", "ipywidgets (>=7)", "nbsphinx (>=0.2.12)", "pre-commit", "pyppeteer (>=1,<1.1)", "pyqtwebengine (>=5.15)", "pytest", "pytest-cov", "pytest-dependency", "sphinx (==5.0.2)", "sphinx-rtd-theme", "tornado (>=6.1)"]
-docs = ["ipython", "nbsphinx (>=0.2.12)", "sphinx (==5.0.2)", "sphinx-rtd-theme"]
-qtpdf = ["pyqtwebengine (>=5.15)"]
-qtpng = ["pyqtwebengine (>=5.15)"]
-serve = ["tornado (>=6.1)"]
-test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pyppeteer (>=1,<1.1)", "pytest", "pytest-cov", "pytest-dependency"]
-webpdf = ["pyppeteer (>=1,<1.1)"]
-
-[[package]]
-name = "nbformat"
-version = "5.4.0"
-description = "The Jupyter Notebook format"
-category = "main"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "nbformat-5.4.0-py3-none-any.whl", hash = "sha256:0d6072aaec95dddc39735c144ee8bbc6589c383fb462e4058abc855348152dad"},
-    {file = "nbformat-5.4.0.tar.gz", hash = "sha256:44ba5ca6acb80c5d5a500f1e5b83ede8cbe364d5a495c4c8cf60aaf1ba656501"},
-]
-
-[package.dependencies]
-fastjsonschema = "*"
-jsonschema = ">=2.6"
-jupyter-core = "*"
 traitlets = ">=5.1"
 
 [package.extras]
-test = ["check-manifest", "pre-commit", "pytest", "testpath"]
+all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"]
+docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
+qtpng = ["pyqtwebengine (>=5.15)"]
+serve = ["tornado (>=6.1)"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"]
+webpdf = ["playwright"]
+
+[[package]]
+name = "nbformat"
+version = "5.10.4"
+description = "The Jupyter Notebook format"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"},
+    {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"},
+]
+
+[package.dependencies]
+fastjsonschema = ">=2.15"
+jsonschema = ">=2.6"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+traitlets = ">=5.1"
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["pep440", "pre-commit", "pytest", "testpath"]
 
 [[package]]
 name = "nest-asyncio"
-version = "1.5.5"
+version = "1.6.0"
 description = "Patch asyncio to allow nested event loops"
-category = "main"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "nest_asyncio-1.5.5-py3-none-any.whl", hash = "sha256:b98e3ec1b246135e4642eceffa5a6c23a3ab12c82ff816a92c612d68205813b2"},
-    {file = "nest_asyncio-1.5.5.tar.gz", hash = "sha256:e442291cd942698be619823a17a86a5759eabe1f8613084790de189fe9e16d65"},
+    {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
+    {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
 ]
 
 [[package]]
 name = "notebook"
-version = "6.4.12"
+version = "6.5.7"
 description = "A web-based notebook environment for interactive computing"
-category = "main"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "notebook-6.4.12-py3-none-any.whl", hash = "sha256:8c07a3bb7640e371f8a609bdbb2366a1976c6a2589da8ef917f761a61e3ad8b1"},
-    {file = "notebook-6.4.12.tar.gz", hash = "sha256:6268c9ec9048cff7a45405c990c29ac9ca40b0bc3ec29263d218c5e01f2b4e86"},
+    {file = "notebook-6.5.7-py3-none-any.whl", hash = "sha256:a6afa9a4ff4d149a0771ff8b8c881a7a73b3835f9add0606696d6e9d98ac1cd0"},
+    {file = "notebook-6.5.7.tar.gz", hash = "sha256:04eb9011dfac634fbd4442adaf0a8c27cd26beef831fe1d19faf930c327768e4"},
 ]
 
 [package.dependencies]
@@ -2007,8 +2797,9 @@ argon2-cffi = "*"
 ipykernel = "*"
 ipython-genutils = "*"
 jinja2 = "*"
-jupyter-client = ">=5.3.4"
+jupyter-client = ">=5.3.4,<8"
 jupyter-core = ">=4.6.1"
+nbclassic = ">=0.4.7"
 nbconvert = ">=5"
 nbformat = "*"
 nest-asyncio = ">=1.5"
@@ -2022,132 +2813,138 @@ traitlets = ">=4.2.1"
 [package.extras]
 docs = ["myst-parser", "nbsphinx", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-github-alt"]
 json-logging = ["json-logging"]
-test = ["coverage", "nbval", "pytest", "pytest-cov", "requests", "requests-unixsocket", "selenium", "testpath"]
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 [[package]]
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 [package.dependencies]
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+python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
-numpy = [
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-    {version = ">=1.19.2", markers = "platform_machine == \"aarch64\" and python_version < \"3.10\""},
-    {version = ">=1.20.0", markers = "platform_machine == \"arm64\" and python_version < \"3.10\""},
-    {version = ">=1.21.0", markers = "python_version >= \"3.10\""},
-]
+numpy = {version = ">=1.21.0", markers = "python_version >= \"3.10\""}
 python-dateutil = ">=2.8.1"
 pytz = ">=2020.1"
 
@@ -2158,7 +2955,6 @@ test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
 name = "pandas-vet"
 version = "0.2.3"
 description = "A flake8 plugin to lint pandas in an opinionated way"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -2172,218 +2968,207 @@ flake8 = ">3.0.0"
 
 [[package]]
 name = "pandocfilters"
-version = "1.5.0"
+version = "1.5.1"
 description = "Utilities for writing pandoc filters in python"
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 files = [
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 ]
 
 [[package]]
 name = "papermill"
-version = "2.4.0"
-description = "Parametrize and run Jupyter and nteract Notebooks"
-category = "dev"
+version = "2.6.0"
+description = "Parameterize and run Jupyter and nteract Notebooks"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
-ansiwrap = "*"
+aiohttp = {version = ">=3.9.0", markers = "python_version == \"3.12\""}
+ansicolors = "*"
 click = "*"
 entrypoints = "*"
 nbclient = ">=0.2.0"
-nbformat = ">=5.1.2"
+nbformat = ">=5.2.0"
 pyyaml = "*"
 requests = "*"
-tenacity = "*"
+tenacity = ">=5.0.2"
 tqdm = ">=4.32.2"
 
 [package.extras]
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-azure = ["azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "requests (>=2.21.0)"]
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 black = ["black (>=19.3b0)"]
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 github = ["PyGithub (>=1.55)"]
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 [[package]]
 name = "parso"
-version = "0.8.3"
+version = "0.8.4"
 description = "A Python Parser"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.extras]
-qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
-testing = ["docopt", "pytest (<6.0.0)"]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["docopt", "pytest"]
 
 [[package]]
 name = "pathspec"
-version = "0.9.0"
+version = "0.12.1"
 description = "Utility library for gitignore style pattern matching of file paths."
-category = "dev"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
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 files = [
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 ]
 
 [[package]]
 name = "pexpect"
-version = "4.8.0"
+version = "4.9.0"
 description = "Pexpect allows easy control of interactive console applications."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
 ptyprocess = ">=0.5"
 
-[[package]]
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-category = "main"
-optional = false
-python-versions = "*"
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-]
-
 [[package]]
 name = "pillow"
-version = "9.3.0"
+version = "11.0.0"
 description = "Python Imaging Library (Fork)"
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 files = [
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 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
-
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+type = ["mypy (>=1.11.2)"]
 
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 name = "pluggy"
-version = "1.0.0"
+version = "1.5.0"
 description = "plugin and hook calling mechanisms for python"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
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 [package.extras]
@@ -2392,14 +3177,13 @@ testing = ["pytest", "pytest-benchmark"]
 
 [[package]]
 name = "prometheus-client"
-version = "0.14.1"
+version = "0.21.0"
 description = "Python client for the Prometheus monitoring system."
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 [package.extras]
@@ -2407,69 +3191,159 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.30"
+version = "3.0.48"
 description = "Library for building powerful interactive command lines in Python"
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-python-versions = ">=3.6.2"
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 ]
 
 [package.dependencies]
 wcwidth = "*"
 
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-category = "main"
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+description = "Accelerated property cache"
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-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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@@ -2479,14 +3353,13 @@ files = [
 
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 [package.dependencies]
-typing-extensions = ">=4.1.0"
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 [package.extras]
 dotenv = ["python-dotenv (>=0.10.4)"]
@@ -2583,53 +3459,52 @@ email = ["email-validator (>=1.0.3)"]
 
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 description = "passive checker of Python programs"
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 ]
 
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 [package.extras]
 spelling = ["pyenchant (>=3.2,<4.0)"]
@@ -2637,26 +3512,24 @@ testutils = ["gitpython (>3)"]
 
 [[package]]
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 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-category = "main"
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 [package.extras]
@@ -2664,126 +3537,89 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
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+version = "3.7.0"
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 jinja2 = ["ruamel.yaml.jinja2 (>=0.2)"]
 
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-version = "0.2.7"
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 description = "C version of reader, parser and emitter for ruamel.yaml derived from libyaml"
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 optional = false
-python-versions = ">=3.5"
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 ]
 
 [[package]]
 name = "safety"
-version = "2.3.1"
+version = "2.3.4"
 description = "Checks installed dependencies for known vulnerabilities and licenses."
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -3188,41 +4158,58 @@ gitlab = ["python-gitlab (>=1.3.0)"]
 
 [[package]]
 name = "scipy"
-version = "1.6.1"
-description = "SciPy: Scientific Library for Python"
-category = "dev"
+version = "1.14.1"
+description = "Fundamental algorithms for scientific computing in Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.10"
 files = [
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 [package.dependencies]
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+numpy = ">=1.23.5,<2.3"
+
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 description = "seaborn: statistical data visualization"
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 optional = false
 python-versions = ">=3.6"
 files = [
@@ -3240,7 +4227,6 @@ scipy = ">=1.0"
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 version = "2.10.0"
 description = "A library implementing the 'SemVer' scheme."
-category = "dev"
 optional = false
 python-versions = ">=2.7"
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@@ -3254,14 +4240,13 @@ doc = ["Sphinx", "sphinx-rtd-theme"]
 
 [[package]]
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-category = "main"
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 [package.extras]
@@ -3271,63 +4256,73 @@ win32 = ["pywin32"]
 
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 ]
 
 [package.extras]
@@ -3339,7 +4334,6 @@ vectorized = ["numpy"]
 name = "six"
 version = "1.16.0"
 description = "Python 2 and 3 compatibility utilities"
-category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
@@ -3349,38 +4343,35 @@ files = [
 
 [[package]]
 name = "sniffio"
-version = "1.2.0"
+version = "1.3.1"
 description = "Sniff out which async library your code is running under"
-category = "dev"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
 name = "soupsieve"
-version = "2.3.2.post1"
+version = "2.6"
 description = "A modern CSS selector implementation for Beautiful Soup."
-category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
 name = "stack-data"
-version = "0.6.2"
+version = "0.6.3"
 description = "Extract data from python stack frames and tracebacks for informative displays"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [package.dependencies]
@@ -3393,29 +4384,28 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "tenacity"
-version = "8.0.1"
+version = "9.0.0"
 description = "Retry code until it succeeds"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
-doc = ["reno", "sphinx", "tornado (>=4.5)"]
+doc = ["reno", "sphinx"]
+test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
 [[package]]
 name = "terminado"
-version = "0.15.0"
+version = "0.18.1"
 description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -3424,30 +4414,19 @@ pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""}
 tornado = ">=6.1.0"
 
 [package.extras]
-test = ["pre-commit", "pytest (>=6.0)", "pytest-timeout"]
-
-[[package]]
-name = "textwrap3"
-version = "0.9.2"
-description = "textwrap from Python 3.6 backport (plus a few tweaks)"
-category = "dev"
-optional = false
-python-versions = "*"
-files = [
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-]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"]
+typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"]
 
 [[package]]
 name = "tinycss2"
-version = "1.1.1"
+version = "1.4.0"
 description = "A tiny CSS parser"
-category = "main"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
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+    {file = "tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289"},
+    {file = "tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7"},
 ]
 
 [package.dependencies]
@@ -3455,13 +4434,12 @@ webencodings = ">=0.4"
 
 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["coverage[toml]", "pytest", "pytest-cov", "pytest-flake8", "pytest-isort"]
+test = ["pytest", "ruff"]
 
 [[package]]
 name = "toml"
 version = "0.10.2"
 description = "Python Library for Tom's Obvious, Minimal Language"
-category = "main"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
 files = [
@@ -3473,7 +4451,6 @@ files = [
 name = "tomli"
 version = "1.2.3"
 description = "A lil' TOML parser"
-category = "main"
 optional = false
 python-versions = ">=3.6"
 files = [
@@ -3483,42 +4460,39 @@ files = [
 
 [[package]]
 name = "tomlkit"
-version = "0.11.4"
+version = "0.13.2"
 description = "Style preserving TOML library"
-category = "main"
 optional = false
-python-versions = ">=3.6,<4.0"
+python-versions = ">=3.8"
 files = [
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-    {file = "tomlkit-0.11.4.tar.gz", hash = "sha256:3235a9010fae54323e727c3ac06fb720752fe6635b3426e379daec60fbd44a83"},
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 ]
 
 [[package]]
 name = "tornado"
-version = "6.2"
+version = "6.4.1"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
-category = "main"
 optional = false
-python-versions = ">= 3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:163b0aafc8e23d8cdc3c9dfb24c5368af84a81e3364745ccb4427669bf84aec8"},
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+    {file = "tornado-6.4.1-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2e20b9113cd7293f164dc46fffb13535266e713cdb87bd2d15ddb336e96cfc4"},
+    {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8ae50a504a740365267b2a8d1a90c9fbc86b780a39170feca9bcc1787ff80842"},
+    {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:613bf4ddf5c7a95509218b149b555621497a6cc0d46ac341b30bd9ec19eac7f3"},
+    {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:25486eb223babe3eed4b8aecbac33b37e3dd6d776bc730ca14e1bf93888b979f"},
+    {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:454db8a7ecfcf2ff6042dde58404164d969b6f5d58b926da15e6b23817950fc4"},
+    {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a02a08cc7a9314b006f653ce40483b9b3c12cda222d6a46d4ac63bb6c9057698"},
+    {file = "tornado-6.4.1-cp38-abi3-win32.whl", hash = "sha256:d9a566c40b89757c9aa8e6f032bcdb8ca8795d7c1a9762910c722b1635c9de4d"},
+    {file = "tornado-6.4.1-cp38-abi3-win_amd64.whl", hash = "sha256:b24b8982ed444378d7f21d563f4180a2de31ced9d8d84443907a0a64da2072e7"},
+    {file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"},
 ]
 
 [[package]]
 name = "tox"
 version = "3.25.1"
 description = "tox is a generic virtualenv management and test command line tool"
-category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
 files = [
@@ -3544,7 +4518,6 @@ testing = ["flaky (>=3.4.0)", "freezegun (>=0.3.11)", "pathlib2 (>=2.3.3)", "psu
 name = "tox-poetry"
 version = "0.4.1"
 description = "Tox poetry plugin"
-category = "dev"
 optional = false
 python-versions = "*"
 files = [
@@ -3562,89 +4535,103 @@ test = ["coverage", "pycodestyle", "pylint", "pytest"]
 
 [[package]]
 name = "tqdm"
-version = "4.62.0"
+version = "4.67.0"
 description = "Fast, Extensible Progress Meter"
-category = "main"
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.62.0-py2.py3-none-any.whl", hash = "sha256:706dea48ee05ba16e936ee91cb3791cd2ea6da348a0e50b46863ff4363ff4340"},
-    {file = "tqdm-4.62.0.tar.gz", hash = "sha256:3642d483b558eec80d3c831e23953582c34d7e4540db86d9e5ed9dad238dabc6"},
+    {file = "tqdm-4.67.0-py3-none-any.whl", hash = "sha256:0cd8af9d56911acab92182e88d763100d4788bdf421d251616040cc4d44863be"},
+    {file = "tqdm-4.67.0.tar.gz", hash = "sha256:fe5a6f95e6fe0b9755e9469b77b9c3cf850048224ecaa8293d7d2d31f97d869a"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
-dev = ["py-make (>=0.1.0)", "twine", "wheel"]
+dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
 telegram = ["requests"]
 
 [[package]]
 name = "traitlets"
-version = "5.3.0"
-description = ""
-category = "main"
+version = "5.14.3"
+description = "Traitlets Python configuration system"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "traitlets-5.3.0-py3-none-any.whl", hash = "sha256:65fa18961659635933100db8ca120ef6220555286949774b9cfc106f941d1c7a"},
-    {file = "traitlets-5.3.0.tar.gz", hash = "sha256:0bb9f1f9f017aa8ec187d8b1b2a7a6626a2a1d877116baba52a129bfa124f8e2"},
+    {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
+    {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
 ]
 
 [package.extras]
-test = ["pre-commit", "pytest"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"]
 
 [[package]]
-name = "types-requests"
-version = "2.28.8"
-description = "Typing stubs for requests"
-category = "main"
+name = "typeguard"
+version = "4.4.1"
+description = "Run-time type checker for Python"
 optional = false
-python-versions = "*"
+python-versions = ">=3.9"
 files = [
-    {file = "types-requests-2.28.8.tar.gz", hash = "sha256:7a9f7b152d594a1c18dd4932cdd2596b8efbeedfd73caa4e4abb3755805b4685"},
-    {file = "types_requests-2.28.8-py3-none-any.whl", hash = "sha256:b0421f9f2d0dd0f8df2c75f974686517ca67473f05b466232d4c6384d765ad7a"},
+    {file = "typeguard-4.4.1-py3-none-any.whl", hash = "sha256:9324ec07a27ec67fc54a9c063020ca4c0ae6abad5e9f0f9804ca59aee68c6e21"},
+    {file = "typeguard-4.4.1.tar.gz", hash = "sha256:0d22a89d00b453b47c49875f42b6601b961757541a2e1e0ef517b6e24213c21b"},
 ]
 
 [package.dependencies]
-types-urllib3 = "<1.27"
+typing-extensions = ">=4.10.0"
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme (>=1.3.0)"]
+test = ["coverage[toml] (>=7)", "mypy (>=1.2.0)", "pytest (>=7)"]
 
 [[package]]
-name = "types-urllib3"
-version = "1.26.22"
-description = "Typing stubs for urllib3"
-category = "main"
+name = "types-python-dateutil"
+version = "2.9.0.20241003"
+description = "Typing stubs for python-dateutil"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "types-urllib3-1.26.22.tar.gz", hash = "sha256:b05af90e73889e688094008a97ca95788db8bf3736e2776fd43fb6b171485d94"},
-    {file = "types_urllib3-1.26.22-py3-none-any.whl", hash = "sha256:09a8783e1002472e8d1e1f3792d4c5cca1fffebb9b48ee1512aae6d16fe186bc"},
+    {file = "types-python-dateutil-2.9.0.20241003.tar.gz", hash = "sha256:58cb85449b2a56d6684e41aeefb4c4280631246a0da1a719bdbe6f3fb0317446"},
+    {file = "types_python_dateutil-2.9.0.20241003-py3-none-any.whl", hash = "sha256:250e1d8e80e7bbc3a6c99b907762711d1a1cdd00e978ad39cb5940f6f0a87f3d"},
 ]
 
 [[package]]
-name = "typing-extensions"
-version = "4.3.0"
-description = "Backported and Experimental Type Hints for Python 3.7+"
-category = "main"
+name = "types-requests"
+version = "2.32.0.20241016"
+description = "Typing stubs for requests"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.3.0-py3-none-any.whl", hash = "sha256:25642c956049920a5aa49edcdd6ab1e06d7e5d467fc00e0506c44ac86fbfca02"},
-    {file = "typing_extensions-4.3.0.tar.gz", hash = "sha256:e6d2677a32f47fc7eb2795db1dd15c1f34eff616bcaf2cfb5e997f854fa1c4a6"},
+    {file = "types-requests-2.32.0.20241016.tar.gz", hash = "sha256:0d9cad2f27515d0e3e3da7134a1b6f28fb97129d86b867f24d9c726452634d95"},
+    {file = "types_requests-2.32.0.20241016-py3-none-any.whl", hash = "sha256:4195d62d6d3e043a4eaaf08ff8a62184584d2e8684e9d2aa178c7915a7da3747"},
+]
+
+[package.dependencies]
+urllib3 = ">=2"
+
+[[package]]
+name = "typing-extensions"
+version = "4.12.2"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
 [[package]]
 name = "typing-inspect"
-version = "0.7.1"
+version = "0.9.0"
 description = "Runtime inspection utilities for typing module."
-category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "typing_inspect-0.7.1-py2-none-any.whl", hash = "sha256:b1f56c0783ef0f25fb064a01be6e5407e54cf4a4bf4f3ba3fe51e0bd6dcea9e5"},
-    {file = "typing_inspect-0.7.1-py3-none-any.whl", hash = "sha256:3cd7d4563e997719a710a3bfe7ffb544c6b72069b6812a02e9b414a8fa3aaa6b"},
-    {file = "typing_inspect-0.7.1.tar.gz", hash = "sha256:047d4097d9b17f46531bf6f014356111a1b6fb821a24fe7ac909853ca2a782aa"},
+    {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
+    {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
 ]
 
 [package.dependencies]
@@ -3652,27 +4639,40 @@ mypy-extensions = ">=0.3.0"
 typing-extensions = ">=3.7.4"
 
 [[package]]
-name = "urllib3"
-version = "1.26.11"
-description = "HTTP library with thread-safe connection pooling, file post, and more."
-category = "main"
+name = "uri-template"
+version = "1.3.0"
+description = "RFC 6570 URI Template Processor"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, <4"
+python-versions = ">=3.7"
 files = [
-    {file = "urllib3-1.26.11-py2.py3-none-any.whl", hash = "sha256:c33ccba33c819596124764c23a97d25f32b28433ba0dedeb77d873a38722c9bc"},
-    {file = "urllib3-1.26.11.tar.gz", hash = "sha256:ea6e8fb210b19d950fab93b60c9009226c63a28808bc8386e05301e25883ac0a"},
+    {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"},
+    {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"},
 ]
 
 [package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
-secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)"]
-socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
+
+[[package]]
+name = "urllib3"
+version = "2.2.3"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
+    {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "us"
 version = "2.0.2"
 description = "US state meta information and other fun stuff"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
@@ -3684,42 +4684,50 @@ jellyfish = "0.6.1"
 
 [[package]]
 name = "virtualenv"
-version = "20.16.3"
+version = "20.27.1"
 description = "Virtual Python Environment builder"
-category = "dev"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "virtualenv-20.16.3-py2.py3-none-any.whl", hash = "sha256:4193b7bc8a6cd23e4eb251ac64f29b4398ab2c233531e66e40b19a6b7b0d30c1"},
-    {file = "virtualenv-20.16.3.tar.gz", hash = "sha256:d86ea0bb50e06252d79e6c241507cb904fcd66090c3271381372d6221a3970f9"},
+    {file = "virtualenv-20.27.1-py3-none-any.whl", hash = "sha256:f11f1b8a29525562925f745563bfd48b189450f61fb34c4f9cc79dd5aa32a1f4"},
+    {file = "virtualenv-20.27.1.tar.gz", hash = "sha256:142c6be10212543b32c6c45d3d3893dff89112cc588b7d0879ae5a1ec03a47ba"},
 ]
 
 [package.dependencies]
-distlib = ">=0.3.5,<1"
-filelock = ">=3.4.1,<4"
-platformdirs = ">=2.4,<3"
+distlib = ">=0.3.7,<1"
+filelock = ">=3.12.2,<4"
+platformdirs = ">=3.9.1,<5"
 
 [package.extras]
-docs = ["proselint (>=0.13)", "sphinx (>=5.1.1)", "sphinx-argparse (>=0.3.1)", "sphinx-rtd-theme (>=1)", "towncrier (>=21.9)"]
-testing = ["coverage (>=6.2)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=21.3)", "pytest (>=7.0.1)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.2)", "pytest-mock (>=3.6.1)", "pytest-randomly (>=3.10.3)", "pytest-timeout (>=2.1)"]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
 
 [[package]]
 name = "wcwidth"
-version = "0.2.5"
+version = "0.2.13"
 description = "Measures the displayed width of unicode strings in a terminal"
-category = "main"
 optional = false
 python-versions = "*"
 files = [
-    {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
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+optional = false
+python-versions = ">=3.9"
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 optional = false
 python-versions = "*"
 files = [
@@ -3729,112 +4737,114 @@ files = [
 
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+version = "1.8.0"
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 test = ["websockets"]
 
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+    {file = "yarl-1.18.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c60e547c0a375c4bfcdd60eef82e7e0e8698bf84c239d715f5c1278a73050393"},
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+    {file = "yarl-1.18.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e2580c1d7e66e6d29d6e11855e3b1c6381971e0edd9a5066e6c14d79bc8967af"},
+    {file = "yarl-1.18.0-cp313-cp313-win32.whl", hash = "sha256:14408cc4d34e202caba7b5ac9cc84700e3421a9e2d1b157d744d101b061a4a88"},
+    {file = "yarl-1.18.0-cp313-cp313-win_amd64.whl", hash = "sha256:1db1537e9cb846eb0ff206eac667f627794be8b71368c1ab3207ec7b6f8c5afc"},
+    {file = "yarl-1.18.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:fa2c9cb607e0f660d48c54a63de7a9b36fef62f6b8bd50ff592ce1137e73ac7d"},
+    {file = "yarl-1.18.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c0f4808644baf0a434a3442df5e0bedf8d05208f0719cedcd499e168b23bfdc4"},
+    {file = "yarl-1.18.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:7db9584235895a1dffca17e1c634b13870852094f6389b68dcc6338086aa7b08"},
+    {file = "yarl-1.18.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:309f8d27d6f93ceeeb80aa6980e883aa57895270f7f41842b92247e65d7aeddf"},
+    {file = "yarl-1.18.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:609ffd44fed2ed88d9b4ef62ee860cf86446cf066333ad4ce4123505b819e581"},
+    {file = "yarl-1.18.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f172b8b2c72a13a06ea49225a9c47079549036ad1b34afa12d5491b881f5b993"},
+    {file = "yarl-1.18.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d89ae7de94631b60d468412c18290d358a9d805182373d804ec839978b120422"},
+    {file = "yarl-1.18.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:466d31fd043ef9af822ee3f1df8fdff4e8c199a7f4012c2642006af240eade17"},
+    {file = "yarl-1.18.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:7609b8462351c4836b3edce4201acb6dd46187b207c589b30a87ffd1813b48dc"},
+    {file = "yarl-1.18.0-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:d9d4f5e471e8dc49b593a80766c2328257e405f943c56a3dc985c125732bc4cf"},
+    {file = "yarl-1.18.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:67b336c15e564d76869c9a21316f90edf546809a5796a083b8f57c845056bc01"},
+    {file = "yarl-1.18.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:b212452b80cae26cb767aa045b051740e464c5129b7bd739c58fbb7deb339e7b"},
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+    {file = "yarl-1.18.0-cp39-cp39-win32.whl", hash = "sha256:b4095c5019bb889aa866bf12ed4c85c0daea5aafcb7c20d1519f02a1e738f07f"},
+    {file = "yarl-1.18.0-cp39-cp39-win_amd64.whl", hash = "sha256:2d90f2e4d16a5b0915ee065218b435d2ef619dd228973b1b47d262a6f7cd8fa5"},
+    {file = "yarl-1.18.0-py3-none-any.whl", hash = "sha256:dbf53db46f7cf176ee01d8d98c39381440776fcda13779d269a8ba664f69bec0"},
+    {file = "yarl-1.18.0.tar.gz", hash = "sha256:20d95535e7d833889982bfe7cc321b7f63bf8879788fee982c76ae2b24cfb715"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.0"
+
+[[package]]
+name = "ypy-websocket"
+version = "0.8.4"
+description = "WebSocket connector for Ypy"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "zipp-3.8.1-py3-none-any.whl", hash = "sha256:47c40d7fe183a6f21403a199b3e4192cca5774656965b0a4988ad2f8feb5f009"},
-    {file = "zipp-3.8.1.tar.gz", hash = "sha256:05b45f1ee8f807d0cc928485ca40a07cb491cf092ff587c0df9cb1fd154848d2"},
+    {file = "ypy_websocket-0.8.4-py3-none-any.whl", hash = "sha256:b1ba0dfcc9762f0ca168d2378062d3ca1299d39076b0f145d961359121042be5"},
+    {file = "ypy_websocket-0.8.4.tar.gz", hash = "sha256:43a001473f5c8abcf182f603049cf305cbc855ad8deaa9dfa0f3b5a7cea9d0ff"},
 ]
 
+[package.dependencies]
+aiofiles = ">=22.1.0,<23"
+aiosqlite = ">=0.17.0,<1"
+y-py = ">=0.6.0,<0.7.0"
+
 [package.extras]
-docs = ["jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx"]
-testing = ["func-timeout", "jaraco.itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+test = ["mypy", "pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)"]
 
 [metadata]
 lock-version = "2.0"
-python-versions = "^3.8"
-content-hash = "00d03bea53e8909beacb8235f35a9c464cc1413e359ace11d05c2a8cffd3976b"
+python-versions = "^3.10"
+content-hash = "bdce0f2249243262fbfd1e73df3f2525c8ca624df6da458480636a19db26c4fe"
diff --git a/data/data-pipeline/pyproject.toml b/data/data-pipeline/pyproject.toml
index 8ea77ef3..e6fff8e8 100644
--- a/data/data-pipeline/pyproject.toml
+++ b/data/data-pipeline/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "justice40-data-pipeline"
-version = "0.1.0"
+version = "1.0.1"
 description = "ETL, Score and Map Generation of Justice 40 Tool"
 authors = ["Justice40 Engineering <j40-engineering@lists.usds.gov>"]
 keywords = ["justice40", "environmental_justice", "python", "etl"]
@@ -17,7 +17,7 @@ packages = [
 
 [tool.poetry.dependencies]
 CensusData = "^1.13"
-certifi = "^2022.12.07" # explicit callout due to https://pyup.io/v/52365/f17/
+certifi = ">= 2024.07.04" # Due to https://data.safetycli.com/v/72083/f17
 click = "8.0.4"  # pinning for now per https://github.com/psf/black/issues/2964
 dynaconf = "^3.1.4"
 geopandas = "^0.11.0"
@@ -27,39 +27,38 @@ jupyter = "^1.0.0"
 jupyter-contrib-nbextensions = "^0.5.1"
 marshmallow-dataclass = "^8.5.3"
 marshmallow-enum = "^1.5.1"
-matplotlib = "^3.4.2"
 numpy = "^1.22.1"
-pandas = "^1.2.5"
+pandas = "~1.4.3"
 pylint = "^2.11.1"
-pillow = "9.3.0"
-python = "^3.8"
+python = "^3.10"
 pypandoc = "^1.6.3"
 PyYAML = "^6.0"
 requests = "^2.25.1"
-tqdm = "4.62.0"
+tqdm = "^4.66.3"
 types-requests = "^2.25.0"
 us = "^2.0.2"
 xlsxwriter = "^2.0.0"
 pydantic = "^1.9.0"
 Rtree = "^1.0.0"
+fiona = "~1.8.21"
 
-[tool.poetry.dev-dependencies]
-black = {version = "^21.6b0", allow-prereleases = true}
-flake8 = "^3.9.2"
-liccheck = "^0.6.2"
+[tool.poetry.group.dev.dependencies]
+black = "^21"
+flake8 = "^7.1.1"
+liccheck = "^0.9.2"
 mypy = "^0.910"
 openpyxl = "^3.0.7"
 pylint = "^2.9.6"
-pytest = "^6.2.4"
+pytest = "^8.3.3"
 safety = "^2.3.1"
 tox = "^3.24.0"
-pytest-mock = "^3.6.1"
+pytest-mock = "^3.14.0"
 tox-poetry = "^0.4.1"
 pandas-vet = "^0.2.2"
-pytest-snapshot = "^0.8.1"
+pytest-snapshot = "^0.9.0"
 seaborn = "^0.11.2"
 papermill = "^2.3.4"
-jupyterlab = "3.4.4"
+jupyterlab = "^3.6.7"
 
 [build-system]
 build-backend = "poetry.core.masonry.api"
@@ -125,6 +124,7 @@ authorized_licenses = [
   "apache",
   "apache 2.0",
   "apache license 2.0",
+  "apache license, version 2.0",
   "apache software license",
   "apache software",
   "gnu lgpl",
@@ -144,3 +144,5 @@ authorized_licenses = [
   "gpl v3",
   "historical permission notice and disclaimer (hpnd)",
 ]
+[tool.liccheck.authorized_packages]
+ypy-websocket="0.8.4"
diff --git a/data/data-pipeline/tox.ini b/data/data-pipeline/tox.ini
index 558b5769..ebf462eb 100644
--- a/data/data-pipeline/tox.ini
+++ b/data/data-pipeline/tox.ini
@@ -1,7 +1,7 @@
 [tox]
 # required because we use pyproject.toml
 isolated_build = true
-envlist = py38, py39, lint, checkdeps, pytest
+envlist = py310, lint, checkdeps, pytest
 # only checks python versions installed locally
 skip_missing_interpreters = true
 
@@ -16,7 +16,9 @@ commands = black data_pipeline
 # checks the dependencies for security vulnerabilities and open source licenses
 allowlist_externals = bash
 commands = pip install -U wheel
-           safety check --ignore 51457 --ignore 44715  # known issue: https://github.com/pyupio/safety/issues/364
+           # known issue: https://github.com/pyupio/safety/issues/364
+           # jinja2 false positive for our use: https://data.safetycli.com/v/70612/f17
+           safety check --ignore 51457 --ignore 44715 --ignore 70612
            bash scripts/run-liccheck.sh
 
 [testenv:pytest]