From 2aa79a334c72e90f39ce614519fe599909a796b7 Mon Sep 17 00:00:00 2001 From: Shaun Verch Date: Wed, 5 Jan 2022 11:17:43 -0500 Subject: [PATCH 1/4] Add worst case scenario rollback plan (#1069) Fixes: https://github.com/usds/justice40-tool/issues/947 This is ensuring we at least have some way to rollback in the worst case scenario of the data being corrupted somehow, and our upstream data sources being down in a way that prevents us from regenerating any datasets. See https://github.com/usds/justice40-tool/issues/946#issuecomment-989155252 for an analysis of the different ways the pipeline can fail and their impacts. This guide is very brittle to pipeline changes, and should be considered a temporary solution for the worst case. I think that someday the github actions should run more frequently and write to different paths, perhaps with a timestamp. That would make rolling back more straightforward, as a previous version of the data would already exist at some path in s3. --- docs/operations/README.md | 3 + docs/operations/rollback-plan.md | 194 +++++++++++++++++++++++++++++++ 2 files changed, 197 insertions(+) create mode 100644 docs/operations/README.md create mode 100644 docs/operations/rollback-plan.md diff --git a/docs/operations/README.md b/docs/operations/README.md new file mode 100644 index 00000000..b79d0fce --- /dev/null +++ b/docs/operations/README.md @@ -0,0 +1,3 @@ +# Operations Guides + +This directory should have guides for operating any services that are being supported by the Justice40 team. diff --git a/docs/operations/rollback-plan.md b/docs/operations/rollback-plan.md new file mode 100644 index 00000000..ad260b52 --- /dev/null +++ b/docs/operations/rollback-plan.md @@ -0,0 +1,194 @@ +# Rollback Plan + +> Note: This guide is up to date as of [this commit](https://github.com/usds/justice40-tool/commit/0d57dd572be027a2fc8b1625958ed68c4b900653), 12/16/2021. If you want to sanity check that this guide is still relevant, go to [Rollback Details](#rollback-details). + +## When To Rollback? + +If for some reason the final map data that has been generated by the pipeline +has become incorrect or is missing, this page documents the emergency steps to +get the data back to a known good state. + +## Rollback Theory + +The theory of rollback depends on two things: + +1. The s3 bucket containing our data uses [s3 bucket versioning](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Versioning.html), allowing us to revert specific files back to a previous version. +2. The [Combine and Tileify step](https://github.com/usds/justice40-tool/blob/main/.github/workflows/combine-tilefy.yml) consumes only two files in s3 as input, making the strategy of reverting those files to a previous version a feasible way to run this job against a previous version. + +If you feel confident in this and want to do the rollback now, proceed to [Rollback Steps](#rollback-steps). + +If you want to understand more deeply what's going on, and sanity check that the +code hasn't changed since this guide was written, go to [Rollback Details](#rollback-details). + +## Rollback Steps + +### 1. List Object Versions + +```shell +aws s3api list-object-versions \ + --bucket justice40-data \ + --prefix data-sources/census.zip \ + --query "Versions[*].{Key:Key,VersionId:VersionId,LastModified:LastModified}" +``` + +You should get something like: + +``` +[ +... +{ + "Key": "data-sources/census.zip", + "VersionId": "", + "LastModified": "2021-11-29T18:57:40+00:00" +} +... +] +``` + +Do the same thing with the score file: + +```shell +aws s3api list-object-versions \ + --bucket justice40-data \ + --prefix data-pipeline/data/score/csv/tiles/usa.csv \ + --query "Versions[*].{Key:Key,VersionId:VersionId,LastModified:LastModified}" +``` + +### 2. Download Previous Version + +Based on the output from the commands above, select the `` of the known good version of that file, and run the following command to download it: + +```shell +aws s3api get-object \ + --bucket justice40-data \ + --key data-sources/census.zip \ + --version-id \ + census.zip +``` + +Do the same for the score file: + +```shell +aws s3api get-object \ + --bucket justice40-data \ + --key data-pipeline/data/score/csv/tiles/usa.csv \ + --version-id \ + usa.csv +``` + +> Note: This command doesn't give you feedback like `curl` does on download progress, it just sits there. To verify the download is happening, open another terminal and check the output file size. + +### 3. Upload New Version + +After you've verified that the local files are correct, you can overwrite the +live versions by running a normal s3 copy: + +```shell +aws s3 cp census.zip s3://justice40-data/data-sources/census.zip +aws s3 cp usa.csv s3://justice40-data/data-pipeline/data/score/csv/tiles/usa.csv +``` + +### 4. Rerun Combine and Tileify + +Run the [Combine and Tileify Github Action](https://github.com/usds/justice40-tool/actions/workflows/combine-tilefy.yml) to regenerate the map tiles from the data you just rolled back. + +## Rollback Details + +> Note: The links to the relevant code are included alongside the relevant snippets as a defense against this page becoming outdated. Make sure the code matches what the links point to, and verify that things still work as this guide assumes. + +The relevant step that consumes the files from s3 should be in the [Combine and Tileify Job](https://github.com/usds/justice40-tool/blob/main/.github/workflows/combine-tilefy.yml#L56-L59): + +```yaml +- name: Run Scripts + run: | + poetry run python3 data_pipeline/application.py geo-score -s aws + poetry run python3 data_pipeline/application.py generate-map-tiles +``` + +This runs the [`score_geo` task](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/application.py#L166): + +```python +score_geo(data_source=data_source) +``` + +This uses the [`GeoScoreETL` object to run the etl](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/runner.py#L121-L124): + +```python +score_geo = GeoScoreETL(data_source=data_source) +score_geo.extract() +score_geo.transform() +score_geo.load() +``` + +In the extract step, `GeoScoreETL` calls [`check_census_data` and `check_score_data`](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/score/etl_score_geo.py#L57-L67): + +```python +# check census data +check_census_data_source( + census_data_path=self.DATA_PATH / "census", + census_data_source=self.DATA_SOURCE, +) + +# check score data +check_score_data_source( + score_csv_data_path=self.SCORE_CSV_PATH, + score_data_source=self.DATA_SOURCE, +) +``` + +The `check_score_data_source` function downloads [one file from s3](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/score/etl_utils.py#L32-L43): + +```python +TILE_SCORE_CSV_S3_URL = ( + settings.AWS_JUSTICE40_DATAPIPELINE_URL + + "/data/score/csv/tiles/usa.csv" +) +TILE_SCORE_CSV = score_csv_data_path / "tiles" / "usa.csv" + +# download from s3 if census_data_source is aws +if score_data_source == "aws": + logger.info("Fetching Score Tile data from AWS S3") + download_file_from_url( + file_url=TILE_SCORE_CSV_S3_URL, download_file_name=TILE_SCORE_CSV + ) +``` + +This can be found here: + +``` +% aws s3 ls s3://justice40-data/data-pipeline/data/score/csv/tiles/usa.csv +2021-12-13 15:23:49 27845542 usa.csv +% curl --head https://justice40-data.s3.amazonaws.com/data-pipeline/data/score/csv/tiles/usa.csv +HTTP/1.1 200 OK +... +``` + +The `check_census_data_source` function downloads [one file from s3](https://github.com/usds/justice40-tool/blob/main/data/data-pipeline/data_pipeline/etl/sources/census/etl_utils.py#L99-L109): + +```python +CENSUS_DATA_S3_URL = settings.AWS_JUSTICE40_DATASOURCES_URL + "/census.zip" +DATA_PATH = settings.APP_ROOT / "data" + +# download from s3 if census_data_source is aws +if census_data_source == "aws": + logger.info("Fetching Census data from AWS S3") + unzip_file_from_url( + CENSUS_DATA_S3_URL, + DATA_PATH / "tmp", + DATA_PATH, + ) +``` + +This can be found here: + +``` +% aws s3 ls s3://justice40-data/data-sources/census.zip +2021-11-29 13:57:40 845390373 census.zip +% curl --head https://justice40-data.s3.amazonaws.com/data-sources/census.zip +HTTP/1.1 200 OK +... +``` + +So this is how to see that the pipeline needs those two files. You can also confirm this in the [Combine and Tileify Github Action logs](https://github.com/usds/justice40-tool/actions/workflows/combine-tilefy.yml). + +If you feel confident in this and want to do the rollback now, proceed to [Rollback Steps](#rollback-steps). From 93595b7bb45e6b173889b9a02ff1ec82d864e0d3 Mon Sep 17 00:00:00 2001 From: Shaun Verch Date: Wed, 5 Jan 2022 15:58:24 -0500 Subject: [PATCH 2/4] Re-export requirements.txt to fix version errors (#1099) * Re-export requirements.txt to fix version errors The version of lxml in this file had a known vulnerability that got caught by the "safety" checker, but it is updated in the poetry files. Regenerated using: https://github.com/usds/justice40-tool/tree/main/data/data-pipeline#miscellaneous * Fix lint error * Run lint on all envs and add comments * Ignore testst that fail lint because of dev deps * Ignore medium.com in link checker It's returning 403s to github actions... --- .../etl.py | 10 +- data/data-pipeline/requirements.txt | 104 +++++++----------- data/data-pipeline/tox.ini | 19 +++- mlc_config.json | 3 + 4 files changed, 66 insertions(+), 70 deletions(-) diff --git a/data/data-pipeline/data_pipeline/etl/sources/energy_definition_alternative_draft/etl.py b/data/data-pipeline/data_pipeline/etl/sources/energy_definition_alternative_draft/etl.py index c8a95fe1..6023f71d 100644 --- a/data/data-pipeline/data_pipeline/etl/sources/energy_definition_alternative_draft/etl.py +++ b/data/data-pipeline/data_pipeline/etl/sources/energy_definition_alternative_draft/etl.py @@ -95,9 +95,13 @@ class EnergyDefinitionAlternativeDraft(ExtractTransformLoad): ) # Convert to boolean: - self.df[field_names.ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE] = \ - self.df[field_names.ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE - ].astype('bool') + self.df[ + field_names.ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE + ] = self.df[ + field_names.ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE + ].astype( + "bool" + ) def validate(self) -> None: logger.info("Validating data") diff --git a/data/data-pipeline/requirements.txt b/data/data-pipeline/requirements.txt index a12d0449..3eacb5cf 100644 --- a/data/data-pipeline/requirements.txt +++ b/data/data-pipeline/requirements.txt @@ -1,48 +1,39 @@ appnope==0.1.2; sys_platform == "darwin" and python_version >= "3.7" and platform_system == "Darwin" argcomplete==1.12.3; python_version < "3.8.0" and python_version >= "3.7" argon2-cffi==21.1.0; python_version >= "3.6" -astroid==2.8.0; python_version >= "3.6" and python_version < "4.0" -atomicwrites==1.4.0; python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.4.0" -attrs==21.2.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6" +astroid==2.8.3; python_version >= "3.6" and python_version < "4.0" +attrs==21.2.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" backcall==0.2.0; python_version >= "3.7" -backports.entry-points-selectable==1.1.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "2.7" -black==21.9b0; python_full_version >= "3.6.2" bleach==4.1.0; python_version >= "3.7" censusdata==1.15; python_version >= "2.7" -certifi==2021.5.30; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" -cffi==1.14.6; implementation_name == "pypy" and python_version >= "3.6" -charset-normalizer==2.0.6; python_full_version >= "3.6.0" and python_version >= "3" +certifi==2021.10.8; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.7" +cffi==1.15.0; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.6.1" +charset-normalizer==2.0.7; python_full_version >= "3.6.0" and python_version >= "3" click-plugins==1.1.1; python_version >= "3.6" -click==8.0.1; python_version >= "3.6" +click==8.0.3; python_version >= "3.6" cligj==0.7.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version < "4" and python_version >= "3.6" -colorama==0.4.4; platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.6.2" and sys_platform == "win32" and python_version < "4.0" and (python_version >= "3.6" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.6" and python_full_version >= "3.5.0") and (python_version >= "3.7" and python_full_version < "3.0.0" and sys_platform == "win32" or sys_platform == "win32" and python_version >= "3.7" and python_full_version >= "3.5.0") -configparser==5.0.2; python_version >= "3.6" +colorama==0.4.4; python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" and sys_platform == "win32" and python_version < "4.0" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0" and sys_platform == "win32" and python_version < "4.0" cycler==0.10.0; python_version >= "3.7" -debugpy==1.4.3; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" +debugpy==1.5.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" decorator==5.1.0; python_version >= "3.7" defusedxml==0.7.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" -distlib==0.3.2; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" -dparse==0.5.1; python_version >= "3.5" dynaconf==3.1.7; python_version >= "3.7" entrypoints==0.3; python_full_version >= "3.6.1" and python_version >= "3.7" -et-xmlfile==1.1.0; python_version >= "3.6" -filelock==3.0.12; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" fiona==1.8.20; python_version >= "3.6" -flake8==3.9.2; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0") geopandas==0.9.0; python_version >= "3.6" -idna==3.2; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5" -importlib-metadata==4.8.1; python_version == "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "3.8" or python_full_version >= "3.5.0" and python_version < "3.8" and python_version >= "3.6") and python_full_version >= "3.6.2" -iniconfig==1.1.1; python_version >= "3.6" -ipykernel==6.4.1; python_version >= "3.7" +idna==3.3; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5" +importlib-metadata==4.8.1; python_version == "3.7" +ipdb==0.13.9; python_version >= "2.7" +ipykernel==6.4.2; python_version >= "3.7" ipython-genutils==0.2.0; python_version >= "3.7" -ipython==7.27.0; python_version >= "3.7" +ipython==7.28.0; python_version >= "3.7" ipywidgets==7.6.5 isort==5.9.3; python_full_version >= "3.6.1" and python_version < "4.0" and python_version >= "3.6" jedi==0.18.0; python_version >= "3.7" jellyfish==0.6.1 -jinja2==3.0.1; python_version >= "3.7" -jsonschema==3.2.0; python_version >= "3.5" -jupyter-client==7.0.3; python_full_version >= "3.6.1" and python_version >= "3.7" +jinja2==3.0.2; python_version >= "3.7" +jsonschema==4.1.2; python_version >= "3.7" +jupyter-client==7.0.6; python_full_version >= "3.6.1" and python_version >= "3.7" jupyter-console==6.4.0; python_version >= "3.6" jupyter-contrib-core==0.3.3 jupyter-contrib-nbextensions==0.5.1 @@ -55,79 +46,64 @@ jupyterlab-pygments==0.1.2; python_version >= "3.7" jupyterlab-widgets==1.0.2; python_version >= "3.6" kiwisolver==1.3.2; python_version >= "3.7" lazy-object-proxy==1.6.0; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.6.0" -liccheck==0.6.2; python_version >= "2.7" -lxml==4.6.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" +lxml==4.6.5; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" markupsafe==2.0.1; python_version >= "3.7" matplotlib-inline==0.1.3; python_version >= "3.7" matplotlib==3.4.3; python_version >= "3.7" -mccabe==0.6.1; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.5.0" +mccabe==0.6.1; python_version >= "3.6" and python_version < "4.0" mistune==0.8.4; python_version >= "3.7" munch==2.5.0; python_version >= "3.6" -mypy-extensions==0.4.3; python_full_version >= "3.6.2" and python_version >= "3.5" -mypy==0.910; python_version >= "3.5" nbclient==0.5.4; python_full_version >= "3.6.1" and python_version >= "3.7" -nbconvert==6.1.0; python_version >= "3.7" +nbconvert==6.2.0; python_version >= "3.7" nbformat==5.1.3; python_full_version >= "3.6.1" and python_version >= "3.7" nest-asyncio==1.5.1; python_full_version >= "3.6.1" and python_version >= "3.7" -notebook==6.4.4; python_version >= "3.6" +notebook==6.4.5; python_version >= "3.6" numpy==1.21.1; python_version >= "3.7" -openpyxl==3.0.7; python_version >= "3.6" -packaging==21.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" -pandas==1.3.3; python_full_version >= "3.7.1" +packaging==21.0; python_version >= "3.7" +pandas==1.3.4; python_full_version >= "3.7.1" pandocfilters==1.5.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.7" parso==0.8.2; python_version >= "3.7" -pathspec==0.9.0; python_full_version >= "3.6.2" pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.7" pickleshare==0.7.5; python_version >= "3.7" -pillow==8.3.2; python_version >= "3.7" -platformdirs==2.3.0; python_version >= "3.6" and python_full_version >= "3.6.2" and python_version < "4.0" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6") -pluggy==1.0.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6" +pillow==8.4.0; python_version >= "3.7" +platformdirs==2.4.0; python_version >= "3.6" and python_version < "4.0" prometheus-client==0.11.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" -prompt-toolkit==3.0.20; python_full_version >= "3.6.2" and python_version >= "3.7" +prompt-toolkit==3.0.21; python_full_version >= "3.6.2" and python_version >= "3.7" ptyprocess==0.7.0; sys_platform != "win32" and python_version >= "3.7" and os_name != "nt" -py==1.10.0; python_version >= "3.6" and python_full_version < "3.0.0" and implementation_name == "pypy" or python_full_version >= "3.5.0" and python_version >= "3.6" and implementation_name == "pypy" -pycodestyle==2.7.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" -pycparser==2.20; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6" -pyflakes==2.3.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" +py==1.10.0; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.6.1" +pycparser==2.20; implementation_name == "pypy" and python_version >= "3.7" and python_full_version >= "3.6.1" pygments==2.10.0; python_version >= "3.7" pylint==2.11.1; python_version >= "3.6" and python_version < "4.0" pypandoc==1.6.4 pyparsing==2.4.7; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7" pyproj==3.2.1; python_version >= "3.7" -pyrsistent==0.18.0; python_version >= "3.6" -pytest-mock==3.6.1; python_version >= "3.6" -pytest==6.2.5; python_version >= "3.6" +pyrsistent==0.18.0; python_version >= "3.7" python-dateutil==2.8.2; python_full_version >= "3.7.1" and python_version >= "3.7" -pytz==2021.1; python_full_version >= "3.7.1" and python_version >= "2.7" -pywin32==301; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.6" +pytz==2021.3; python_full_version >= "3.7.1" and python_version >= "3.6" +pywin32==302; sys_platform == "win32" and platform_python_implementation != "PyPy" and python_version >= "3.7" pywinpty==1.1.4; os_name == "nt" and python_version >= "3.6" -pyyaml==5.4.1; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" +pyyaml==6.0; python_version >= "3.6" pyzmq==22.3.0; python_full_version >= "3.6.1" and python_version >= "3.7" qtconsole==5.1.1; python_version >= "3.6" -qtpy==1.11.1; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" -regex==2021.8.28; python_full_version >= "3.6.2" +qtpy==1.11.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.6" requests==2.26.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0") -safety==1.10.3; python_version >= "3.5" -semantic-version==2.8.5; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "2.7" send2trash==1.8.0; python_version >= "3.6" shapely==1.7.1; python_version >= "3.6" -six==1.16.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7" +six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.7" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") terminado==0.12.1; python_version >= "3.6" testpath==0.5.0; python_version >= "3.7" -toml==0.10.2; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.5.0" and python_version >= "3.6" and python_version < "4.0" -tomli==1.2.1; python_version >= "3.6" and python_full_version >= "3.6.2" +toml==0.10.2; python_version > "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_full_version >= "3.3.0" and python_version > "3.6" and python_version < "4.0" tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.7" -tox==3.24.4; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.5.0") tqdm==4.62.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0") traitlets==5.1.0; python_full_version >= "3.6.1" and python_version >= "3.7" -types-requests==2.25.8 -typing-extensions==3.10.0.2; python_version < "3.8" and python_version >= "3.6" -urllib3==1.26.6; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "2.7" +typed-ast==1.4.3; implementation_name == "cpython" and python_version < "3.8" and python_version >= "3.6" +types-requests==2.25.11 +typing-extensions==3.10.0.2; python_version >= "3.6" and python_version < "3.8" +urllib3==1.26.7; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version < "4" and python_version >= "2.7" us==2.0.2 -virtualenv==20.8.0; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" wcwidth==0.2.5; python_full_version >= "3.6.2" and python_version >= "3.7" webencodings==0.5.1; python_version >= "3.7" widgetsnbextension==3.5.1 -wrapt==1.12.1; python_version >= "3.6" and python_version < "4.0" +wrapt==1.13.2; python_version >= "3.6" and python_full_version < "3.0.0" and python_version < "4.0" or python_version >= "3.6" and python_version < "4.0" and python_full_version >= "3.5.0" xlsxwriter==2.0.0 -zipp==3.5.0; python_version < "3.8" and python_version >= "3.6" +zipp==3.6.0; python_version < "3.8" and python_version >= "3.6" diff --git a/data/data-pipeline/tox.ini b/data/data-pipeline/tox.ini index 381139bb..059520a4 100644 --- a/data/data-pipeline/tox.ini +++ b/data/data-pipeline/tox.ini @@ -7,19 +7,32 @@ skip_missing_interpreters = true [testenv:lint] # lints python code in src and tests -basepython = python3.9 deps = -rrequirements.txt +# These are "external" because they are dev dependencies that not in +# "requirements.txt" and therefore not installed in this env, but they will be +# available where this is being run. See: +# https://stackoverflow.com/questions/47642747/tox-warningtest-command-found-but-not-installed-in-testenv +allowlist_externals = black + flake8 + pylint commands = black data_pipeline flake8 data_pipeline - pylint data_pipeline +# Ignore tests this lint check because test dependencies are not installed here. + pylint data_pipeline --ignore tests [testenv:checkdeps] # checks the dependencies for security vulnerabilities and open source licenses deps = -rrequirements.txt +# These are "external" because they are dev dependencies that not in +# "requirements.txt" and therefore not installed in this env, but they will be +# available where this is being run. See: +# https://stackoverflow.com/questions/47642747/tox-warningtest-command-found-but-not-installed-in-testenv +allowlist_externals = safety + liccheck commands = safety check liccheck [testenv:pytest] # Run tests deps = pytest -commands = pytest \ No newline at end of file +commands = pytest diff --git a/mlc_config.json b/mlc_config.json index ebc7c01e..53f78d89 100644 --- a/mlc_config.json +++ b/mlc_config.json @@ -14,6 +14,9 @@ }, { "pattern": "^https://github.com/usds/environmental-justice/" + }, + { + "pattern": "^https://.*medium.com" } ], "replacementPatterns": [ From 56644698ff40b0a155ef1f872cd798bb3462f25f Mon Sep 17 00:00:00 2001 From: Saran Ahluwalia <94847739+saran-ahluwalia@users.noreply.github.com> Date: Wed, 5 Jan 2022 17:03:37 -0500 Subject: [PATCH 3/4] Address rounding issue in Pandas series to floor numerically unstable values (#1085) * wip - added tests - 1 failing * added check for empty series + added test * passing tests * parallelism in variable assingnment choice * resolve merge conflicts * variable name changes * cleanup logic and move comments out of main code execution + add one more test for an extreme example eith -np.inf * cleanup logic and move comments out of main code execution + add one more test for an extreme example eith -np.inf * revisions to handle type ambiguity * fixing tests * fix pytest * fix linting * fix pytest * reword comments * cleanup comments * cleanup comments - fix typo * added type check and corresponding test * added type check and corresponding test * language cleanup * revert * update picke fixture Co-authored-by: Jorge Escobar --- .../data_pipeline/etl/score/etl_score_post.py | 15 ++-- .../data_pipeline/etl/score/etl_utils.py | 60 +++++++++++++++ .../tests/snapshots/tile_data_expected.pkl | Bin 3034 -> 3085 bytes .../etl/score/tests/test_etl_utils.py | 72 ++++++++++++++++++ 4 files changed, 141 insertions(+), 6 deletions(-) create mode 100644 data/data-pipeline/data_pipeline/etl/score/tests/test_etl_utils.py 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 8f527c7a..244cccbe 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 @@ -3,6 +3,7 @@ import json import pandas as pd from data_pipeline.etl.base import ExtractTransformLoad +from data_pipeline.etl.score.etl_utils import floor_series from data_pipeline.utils import get_module_logger, zip_files from data_pipeline.score import field_names @@ -207,13 +208,15 @@ class PostScoreETL(ExtractTransformLoad): # filter the columns on full score score_tiles = score_county_state_merged_df[tiles_score_column_titles] - # round decimals - decimals = pd.Series( - [constants.TILES_ROUND_NUM_DECIMALS] - * len(constants.TILES_SCORE_FLOAT_COLUMNS), - index=constants.TILES_SCORE_FLOAT_COLUMNS, + score_tiles[constants.TILES_SCORE_FLOAT_COLUMNS] = score_tiles[ + constants.TILES_SCORE_FLOAT_COLUMNS + ].apply( + func=lambda series: floor_series( + series=series, + number_of_decimals=constants.TILES_ROUND_NUM_DECIMALS, + ), + axis=0, ) - score_tiles = score_tiles.round(decimals) # create indexes score_tiles = score_tiles.rename( diff --git a/data/data-pipeline/data_pipeline/etl/score/etl_utils.py b/data/data-pipeline/data_pipeline/etl/score/etl_utils.py index c3fec035..2219ab18 100644 --- a/data/data-pipeline/data_pipeline/etl/score/etl_utils.py +++ b/data/data-pipeline/data_pipeline/etl/score/etl_utils.py @@ -1,6 +1,9 @@ import os import sys from pathlib import Path +import numpy as np +import pandas as pd + from data_pipeline.config import settings from data_pipeline.utils import ( @@ -48,3 +51,60 @@ def check_score_data_source( "No local score tiles data found. Please use '-d aws` to fetch from AWS" ) sys.exit() + + +def floor_series(series: pd.Series, number_of_decimals: int) -> pd.Series: + """Floors all non-null numerical values to a specific number of decimal points + + Args: + series (pd.Series): Input pandas series + number_of_decimals (int): Number of decimal points to floor all numerical values to + Returns: + floored_series (pd.Series): A Pandas Series of numerical values with appropriate number of decimal points + """ + + # we perform many operations using the division operator + # as well as elementwise multiplication. The result of such + # operations can introduce such values, below, due to numerical + # instability. This results in unsafe type inference for numpy + # float types - exacerbated by panda's type inference engine. + # Hence, to handle such offending values we default to None + # Please see the reference, below, on nullable integer types for more details + unacceptable_values = [-np.inf, np.inf, "None", np.nan] + mapping = { + unacceptable_value: None for unacceptable_value in unacceptable_values + } + + # ensure we are working with a numpy array (which is really what a pandas series is) + if not isinstance(series, pd.Series): + raise TypeError( + f"Argument series must be of type pandas series, not of type {type(series).__name__}." + ) + + # raise exception for handling empty series + if series.empty: + raise ValueError("Empty series provided.") + + # if we have any values, just replace them with None + if series.isin(unacceptable_values).any(): + series.replace(mapping, regex=False, inplace=True) + + multiplication_factor = 10 ** number_of_decimals + + # In order to safely cast NaNs + # First coerce series to float type: series.astype(float) + # Please see here: + # https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html#nullable-integer-data-type + product_for_numerator = np.floor( + series.astype(float) * multiplication_factor + ) + + floored_series = np.where( + series.isnull(), + # For all null values default to null + None, + # The other default condition - floor non-null values + product_for_numerator / multiplication_factor, + ) + + return floored_series diff --git a/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl b/data/data-pipeline/data_pipeline/etl/score/tests/snapshots/tile_data_expected.pkl index 8c47d6538afd0dfd2b146bce3b936c243ade83eb..d066a170c285542f993355c2d18925d057ef3394 100644 GIT binary patch delta 755 zcmca5-Ydb{z%rGIXCrF}6Qk1PC?+jNm&vV6_G}CwFlF+2c2S`46QHobWPaw)let(F zCU0gpWMrJ2!lEhTEU+SAp;If@R{I^&(>1>P9W@GwWv5hPr*6AG_Ry` zN=Bc%{kt83uUqAIxZA&v(fE3(KgQkuwJ{jD+rR1u3%T1r=dvwWe4WeP{w;{IEp)ek z0oD}cZvW;C2%H7#0vZAo1eya=`~arr5m0wMP#;hm$OesXK!d=F<=yQc!?Zn|2?Dd+ z?VnUVbYCY@?QVY+r1tA!Bop7Ifq}dIKA59HMuSa$1!uq#LsQm-fRy-mwsZ>xJP{~mDW@w#~q1iqqXv*XV>?{&8KwfC4 z^8-exIwmMR*?_HJ@(MN&8C9r&HI#OQ(x5ao*@?r1an|H=4lTy6&5JmKnHc9x{>Ej< QxL~pdcOc`-$yMA50Le=j-~a#s delta 698 zcmeB`xFycoz%unb_eRzbCPtOXQA}El9+O*{>=hUppkPYLl%x!m40UgY)+rg9?NfrL zXm~Svt4w|XRH`tUoB1=N_2lo&>Wq$)#aS#EB_;>3Xx2M50JU>nUtD0z_1=D+`@<@c zb?@zs!Qiz$i0A(Bm3>VAp|2V-&+Q9sK`P(c2knq+eI5A1{_GhLcw-ObfJE$J=0M~h z*n_q8KeGR(0W#qQTt7tpWBWIO^&ou@p$0v%KMb^{|Ik&qL124!yt7{dbl&yF2jB*R z-C_F*?m$hj3-{R1nh63cq2|1W(<=fNI<<0bwcjBfW7<@>YzJN3t!noLihz#<7>FjA^P4x9dZk<4iZv#?LpyJCGyT5;*ojw@?fvO zg@=S%HCV+Pd+c#10gTAdPUi!Y7qS;H3Qp$Y@SiNlV#vbm&D1tIpIcqpKv5XtvLbd^ zB7o4043qz{NNZsv24;wX5)x45s!-Y%oFteg2eF!~P3;lzN=-{kEh^5>Q*h2N%`2&# zk})k6l(=kw>518!5tOthmvWde&YQf7LyK|J=BpgROw0> Date: Wed, 5 Jan 2022 17:37:47 -0500 Subject: [PATCH 4/4] Add category header and updates from Living Copy (#1084) * Add category header (Indicator and Percentile) - add local styling - override accordion-content styles to push into parent container - intl copy - update snapshots * Add latest Living Copy - refactor dataset cards to allow for any styling in description, used in and responsible party - add percent for HS edu indicator - updates snapshots * Update copy from Living Copy doc -update snapshots * Add methodology to copy * Remove link from readme file based on md checker * Comment out certain markdown links from checker * Comment out markdown links that render in browser --- INSTALLATION-es.md | 2 +- client/README.md | 2 + .../AreaDetail/areaDetail.module.scss | 18 + .../AreaDetail/areaDetail.module.scss.d.ts | 1 + client/src/components/AreaDetail/index.tsx | 125 +-- .../__snapshots__/areaDetail.test.tsx.snap | 73 +- .../__snapshots__/BetaBanner.test.tsx.snap | 4 +- .../__snapshots__/Categories.test.tsx.snap | 2 +- client/src/components/DatasetCard/index.tsx | 4 +- .../__snapshots__/datasetCard.test.tsx.snap | 8 +- .../datasetContainer.test.tsx.snap | 253 +++--- client/src/components/Indicator/Indicator.tsx | 9 +- .../__snapshots__/J40Header.test.tsx.snap | 4 +- client/src/data/copy/common.tsx | 4 +- client/src/data/copy/explore.tsx | 15 +- client/src/data/copy/methodology.tsx | 741 +++++++++++++----- .../pages/__snapshots__/contact.test.tsx.snap | 4 +- .../pages/__snapshots__/index.test.tsx.snap | 4 +- .../__snapshots__/methodology.test.tsx.snap | 259 +++--- client/src/styles/global.scss | 6 + .../0002-mapping-visualization-library.md | 1 + docs/decisions/0004-client-side-framework.md | 6 +- 22 files changed, 1059 insertions(+), 486 deletions(-) diff --git a/INSTALLATION-es.md b/INSTALLATION-es.md index b4365aa3..9403ea2b 100644 --- a/INSTALLATION-es.md +++ b/INSTALLATION-es.md @@ -25,7 +25,7 @@ Descargar desde [sitio web](https://git-scm.com/download/win) 2. Valide la instalación escribiendo `brew -v` en la terminal y asegúrese de que se muestre un número de versión. ### Instalar Node usando NVM - + Esto funcionará tanto para MacOS como para Win10. Siga las instrucciones de este [enlace](https://medium.com/@nodesource/installing-node-js-tutorial-using-nvm-5c6ff5925dd8) ### Configuración de IDE diff --git a/client/README.md b/client/README.md index 84eee920..2f7497bd 100644 --- a/client/README.md +++ b/client/README.md @@ -19,6 +19,7 @@ This README contains the following content: #### Install Node using NVM + This will work for both MacOS and Win10. Follow instructions on this [link](https://medium.com/@nodesource/installing-node-js-tutorial-using-nvm-5c6ff5925dd8). Be sure to read through the whole doc to find the sections within each step relevant to you (e.g. if you're using Homebrew, when you get to Step 2 look for the section, "Install NVM with Homebrew"). If you install NVM using Homebrew, make sure to read the output in terminal after you run `brew install nvm`. You will need to add a few lines to your ~/.bash_profile and perhaps complete a couple other tasks. @@ -70,6 +71,7 @@ DATA_SOURCE env variable in the docker-compose.yml. See [environment variables]( #### Troubleshooting docker + - If an error is thrown about [running out of space](https://medium.com/@wlarch/no-space-left-on-device-when-using-docker-compose-why-c4a2c783c6f6) on device see this for ways to reclaim space. diff --git a/client/src/components/AreaDetail/areaDetail.module.scss b/client/src/components/AreaDetail/areaDetail.module.scss index 9e319643..03f644da 100644 --- a/client/src/components/AreaDetail/areaDetail.module.scss +++ b/client/src/components/AreaDetail/areaDetail.module.scss @@ -82,3 +82,21 @@ $sidePanelLabelFontColor: #171716; font-size: medium; } } + +// The following class is used in the AccordionItems in the AreaDetail component. +// The Accordion component (parent of AccordionItems) requires some CSS overrides. +// Local styling is not allowing the override. +// The override is needed to push into the bounds of the Accordion component's styles. +// To override this, in globals.scss, we set the this .categoryHeader's vertical alignment by +// setting styles in: +// .usa-accordion__content > *:first-child +// This first child of the accordion content is the category header: +.categoryHeader { + display: flex; + justify-content: space-between; + text-transform: uppercase; + font-size: small; + @include u-bg('gray-cool-5'); + @include u-padding-left(2.5); + @include u-padding-right(2); +} \ No newline at end of file diff --git a/client/src/components/AreaDetail/areaDetail.module.scss.d.ts b/client/src/components/AreaDetail/areaDetail.module.scss.d.ts index ad2fc967..03e161a5 100644 --- a/client/src/components/AreaDetail/areaDetail.module.scss.d.ts +++ b/client/src/components/AreaDetail/areaDetail.module.scss.d.ts @@ -11,6 +11,7 @@ declare namespace MapModuleScssNamespace { versionInfo: string; showThresholdExceed:string; hideThresholdExceed:string; + categoryHeader:string; } } diff --git a/client/src/components/AreaDetail/index.tsx b/client/src/components/AreaDetail/index.tsx index b017b6bd..9a91c02d 100644 --- a/client/src/components/AreaDetail/index.tsx +++ b/client/src/components/AreaDetail/index.tsx @@ -18,11 +18,20 @@ interface IAreaDetailProps { properties: constants.J40Properties, } +/** + * This interface is used as define the various fields for each indicator in the side panel + * label: the indicator label or title + * description: the description of the indicator used in the side panel + * value: the number from the geoJSON tile + * isDisadvagtaged: the flag from the geoJSON tile + * isPercent: is the value a percent or percentile + * */ export interface indicatorInfo { label: string, description: string, value: number, isDisadvagtaged: boolean, + isPercent?: boolean, } const AreaDetail = ({properties}:IAreaDetailProps) => { @@ -45,84 +54,84 @@ const AreaDetail = ({properties}:IAreaDetailProps) => { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.EXP_AG_LOSS), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.EXP_AG_LOSS), value: properties[constants.EXP_AGRICULTURE_LOSS_PERCENTILE] ? - properties[constants.EXP_AGRICULTURE_LOSS_PERCENTILE] : null, + properties[constants.EXP_AGRICULTURE_LOSS_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_EXP_AGR_LOSS_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_EXP_AGR_LOSS_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_EXP_AGR_LOSS_AND_IS_LOW_INCOME] : null, }; const expBldLoss:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.EXP_BLD_LOSS), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.EXP_BLD_LOSS), value: properties[constants.EXP_BUILDING_LOSS_PERCENTILE] ? - properties[constants.EXP_BUILDING_LOSS_PERCENTILE] : null, + properties[constants.EXP_BUILDING_LOSS_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_EXP_BLD_LOSS_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_EXP_BLD_LOSS_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_EXP_BLD_LOSS_AND_IS_LOW_INCOME] : null, }; const expPopLoss:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.EXP_POP_LOSS), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.EXP_POP_LOSS), value: properties[constants.EXP_POPULATION_LOSS_PERCENTILE] ? - properties[constants.EXP_POPULATION_LOSS_PERCENTILE] : null, + properties[constants.EXP_POPULATION_LOSS_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_EXP_POP_LOSS_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_EXP_POP_LOSS_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_EXP_POP_LOSS_AND_IS_LOW_INCOME] : null, }; const lowInc:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LOW_INCOME), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LOW_INCOME), value: properties[constants.POVERTY_BELOW_200_PERCENTILE] ? - properties[constants.POVERTY_BELOW_200_PERCENTILE] : null, + properties[constants.POVERTY_BELOW_200_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_FEDERAL_POVERTY_LEVEL_200] ? - properties[constants.IS_FEDERAL_POVERTY_LEVEL_200] : null, + properties[constants.IS_FEDERAL_POVERTY_LEVEL_200] : null, }; const energyBurden:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.ENERGY_BURDEN), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.ENERGY_BURDEN), value: properties[constants.ENERGY_PERCENTILE] ? - properties[constants.ENERGY_PERCENTILE] : null, + properties[constants.ENERGY_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_ENERGY_BURDEN_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_ENERGY_BURDEN_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_ENERGY_BURDEN_AND_IS_LOW_INCOME] : null, }; const pm25:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.PM_2_5), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.PM_2_5), value: properties[constants.PM25_PERCENTILE] ? - properties[constants.PM25_PERCENTILE] : null, + properties[constants.PM25_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_PM25_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_PM25_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_PM25_AND_IS_LOW_INCOME] : null, }; const dieselPartMatter:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.DIESEL_PARTICULATE_MATTER), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.DIESEL_PARTICULATE_MATTER), value: properties[constants.DIESEL_MATTER_PERCENTILE] ? - properties[constants.DIESEL_MATTER_PERCENTILE] : null, + properties[constants.DIESEL_MATTER_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_DIESEL_PM_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_DIESEL_PM_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_DIESEL_PM_AND_IS_LOW_INCOME] : null, }; const trafficVolume:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.TRAFFIC_VOLUME), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.TRAFFIC_VOLUME), value: properties[constants.TRAFFIC_PERCENTILE] ? - properties[constants.TRAFFIC_PERCENTILE] : null, + properties[constants.TRAFFIC_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_TRAFFIC_PROX_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_TRAFFIC_PROX_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_TRAFFIC_PROX_AND_IS_LOW_INCOME] : null, }; const houseBurden:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.HOUSE_BURDEN), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.HOUSE_BURDEN), value: properties[constants.HOUSING_BURDEN_PROPERTY_PERCENTILE] ? - properties[constants.HOUSING_BURDEN_PROPERTY_PERCENTILE] : null, + properties[constants.HOUSING_BURDEN_PROPERTY_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_HOUSE_BURDEN_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_HOUSE_BURDEN_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_HOUSE_BURDEN_AND_IS_LOW_INCOME] : null, }; const leadPaint:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LEAD_PAINT), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LEAD_PAINT), value: properties[constants.LEAD_PAINT_PERCENTILE] ? - properties[constants.LEAD_PAINT_PERCENTILE] : null, + properties[constants.LEAD_PAINT_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_LEAD_PAINT_AND_MEDIAN_HOME_VAL_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_LEAD_PAINT_AND_MEDIAN_HOME_VAL_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_LEAD_PAINT_AND_MEDIAN_HOME_VAL_AND_IS_LOW_INCOME] : null, }; // const medHomeVal:indicatorInfo = { // label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.MED_HOME_VAL), @@ -136,108 +145,109 @@ const AreaDetail = ({properties}:IAreaDetailProps) => { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.PROX_HAZ), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.PROX_HAZ), value: properties[constants.PROXIMITY_TSDF_SITES_PERCENTILE] ? - properties[constants.PROXIMITY_TSDF_SITES_PERCENTILE] : null, + properties[constants.PROXIMITY_TSDF_SITES_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_HAZARD_WASTE_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_HAZARD_WASTE_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_HAZARD_WASTE_AND_IS_LOW_INCOME] : null, }; const proxNPL:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.PROX_NPL), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.PROX_NPL), value: properties[constants.PROXIMITY_NPL_SITES_PERCENTILE] ? - properties[constants.PROXIMITY_NPL_SITES_PERCENTILE] : null, + properties[constants.PROXIMITY_NPL_SITES_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_SUPERFUND_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_SUPERFUND_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_SUPERFUND_AND_IS_LOW_INCOME] : null, }; const proxRMP:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.PROX_RMP), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.PROX_RMP), value: properties[constants.PROXIMITY_RMP_SITES_PERCENTILE] ? - properties[constants.PROXIMITY_RMP_SITES_PERCENTILE] : null, + properties[constants.PROXIMITY_RMP_SITES_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_RMP_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_RMP_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_RMP_AND_IS_LOW_INCOME] : null, }; const wasteWater:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.WASTE_WATER), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.WASTE_WATER), value: properties[constants.WASTEWATER_PERCENTILE] ? - properties[constants.WASTEWATER_PERCENTILE] : null, + properties[constants.WASTEWATER_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_WASTEWATER_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_WASTEWATER_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_WASTEWATER_AND_IS_LOW_INCOME] : null, }; const asthma:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.ASTHMA), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.ASTHMA), value: properties[constants.ASTHMA_PERCENTILE] ? - properties[constants.ASTHMA_PERCENTILE] : null, + properties[constants.ASTHMA_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_ASTHMA_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_ASTHMA_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_ASTHMA_AND_IS_LOW_INCOME] : null, }; const diabetes:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.DIABETES), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.DIABETES), value: properties[constants.DIABETES_PERCENTILE] ? - properties[constants.DIABETES_PERCENTILE] : null, + properties[constants.DIABETES_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_DIABETES_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_DIABETES_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_DIABETES_AND_IS_LOW_INCOME] : null, }; const heartDisease:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.HEART_DISEASE), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.HEART_DISEASE), value: properties[constants.HEART_PERCENTILE] ? - properties[constants.HEART_PERCENTILE] : null, + properties[constants.HEART_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_HEART_DISEASE_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_HEART_DISEASE_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_HEART_DISEASE_AND_IS_LOW_INCOME] : null, }; const lifeExpect:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LIFE_EXPECT), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LOW_LIFE_EXPECT), value: properties[constants.LIFE_PERCENTILE] ? - properties[constants.LIFE_PERCENTILE] : null, + properties[constants.LIFE_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_LOW_LIFE_EXP_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_LOW_LIFE_EXP_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_LOW_LIFE_EXP_AND_IS_LOW_INCOME] : null, }; const lowMedInc:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LOW_MED_INC), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LOW_MED_INCOME), value: properties[constants.LOW_MEDIAN_INCOME_PERCENTILE] ? - properties[constants.LOW_MEDIAN_INCOME_PERCENTILE] : null, + properties[constants.LOW_MEDIAN_INCOME_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_LOW_MEDIAN_INCOME_AND_LOW_HIGH_SCHOOL_EDU] ? - properties[constants.IS_GTE_90_LOW_MEDIAN_INCOME_AND_LOW_HIGH_SCHOOL_EDU] : null, + properties[constants.IS_GTE_90_LOW_MEDIAN_INCOME_AND_LOW_HIGH_SCHOOL_EDU] : null, }; const lingIso:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.LING_ISO), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.LING_ISO), value: properties[constants.LINGUISTIC_ISOLATION_PROPERTY_PERCENTILE] ? - properties[constants.LINGUISTIC_ISOLATION_PROPERTY_PERCENTILE] : null, + properties[constants.LINGUISTIC_ISOLATION_PROPERTY_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_LINGUISITIC_ISO_AND_IS_LOW_INCOME] ? - properties[constants.IS_GTE_90_LINGUISITIC_ISO_AND_IS_LOW_INCOME] : null, + properties[constants.IS_GTE_90_LINGUISITIC_ISO_AND_IS_LOW_INCOME] : null, }; const unemploy:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.UNEMPLOY), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.UNEMPLOY), value: properties[constants.UNEMPLOYMENT_PROPERTY_PERCENTILE] ? - properties[constants.UNEMPLOYMENT_PROPERTY_PERCENTILE] : null, + properties[constants.UNEMPLOYMENT_PROPERTY_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] ? - properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] : null, + properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] : null, }; const poverty:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.POVERTY), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.POVERTY), value: properties[constants.POVERTY_PROPERTY_PERCENTILE] ? - properties[constants.POVERTY_PROPERTY_PERCENTILE] : null, + properties[constants.POVERTY_PROPERTY_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_BELOW_100_POVERTY_AND_LOW_HIGH_SCHOOL_EDU] ? - properties[constants.IS_GTE_90_BELOW_100_POVERTY_AND_LOW_HIGH_SCHOOL_EDU] : null, + properties[constants.IS_GTE_90_BELOW_100_POVERTY_AND_LOW_HIGH_SCHOOL_EDU] : null, }; const highSchool:indicatorInfo = { label: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATORS.HIGH_SCL), description: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_INDICATOR_DESCRIPTION.HIGH_SKL), value: properties[constants.HIGH_SCHOOL_PROPERTY_PERCENTILE] ? - properties[constants.HIGH_SCHOOL_PROPERTY_PERCENTILE] : null, + properties[constants.HIGH_SCHOOL_PROPERTY_PERCENTILE] : null, isDisadvagtaged: properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] ? - properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] : null, + properties[constants.IS_GTE_90_UNEMPLOYMENT_AND_LOW_HIGH_SCHOOL_EDU] : null, + isPercent: true, }; // Aggregate indicators based on categories @@ -247,56 +257,56 @@ const AreaDetail = ({properties}:IAreaDetailProps) => { titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.CLIMATE), indicators: [expAgLoss, expBldLoss, expPopLoss, lowInc], isDisadvagtaged: properties[constants.IS_CLIMATE_FACTOR_DISADVANTAGED_L] ? - properties[constants.IS_CLIMATE_FACTOR_DISADVANTAGED_L] : null, + properties[constants.IS_CLIMATE_FACTOR_DISADVANTAGED_L] : null, }, { id: 'clean-energy', titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.CLEAN_ENERGY), indicators: [energyBurden, pm25, lowInc], isDisadvagtaged: properties[constants.IS_ENERGY_FACTOR_DISADVANTAGED_L] ? - properties[constants.IS_ENERGY_FACTOR_DISADVANTAGED_L] : null, + properties[constants.IS_ENERGY_FACTOR_DISADVANTAGED_L] : null, }, { id: 'clean-transport', titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.CLEAN_TRANSPORT), indicators: [dieselPartMatter, trafficVolume, lowInc], isDisadvagtaged: properties[constants.IS_TRANSPORT_FACTOR_DISADVANTAGED_L] ? - properties[constants.IS_TRANSPORT_FACTOR_DISADVANTAGED_L] : null, + properties[constants.IS_TRANSPORT_FACTOR_DISADVANTAGED_L] : null, }, { id: 'sustain-house', titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.SUSTAIN_HOUSE), indicators: [houseBurden, leadPaint, lowInc], isDisadvagtaged: properties[constants.IS_HOUSING_FACTOR_DISADVANTAGED_L] ? - properties[constants.IS_HOUSING_FACTOR_DISADVANTAGED_L] : null, + properties[constants.IS_HOUSING_FACTOR_DISADVANTAGED_L] : null, }, { id: 'leg-pollute', titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.LEG_POLLUTE), indicators: [proxHaz, proxNPL, proxRMP, lowInc], isDisadvagtaged: properties[constants.IS_POLLUTION_FACTOR_DISADVANTAGED_L] ? - properties[constants.IS_POLLUTION_FACTOR_DISADVANTAGED_L] : null, + properties[constants.IS_POLLUTION_FACTOR_DISADVANTAGED_L] : null, }, { id: 'clean-water', titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.CLEAN_WATER), indicators: [wasteWater, lowInc], isDisadvagtaged: properties[constants.IS_WATER_FACTOR_DISADVANTAGED_L] ? - properties[constants.IS_WATER_FACTOR_DISADVANTAGED_L] : null, + properties[constants.IS_WATER_FACTOR_DISADVANTAGED_L] : null, }, { id: 'health-burdens', titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.HEALTH_BURDEN), indicators: [asthma, diabetes, heartDisease, lifeExpect, lowInc], isDisadvagtaged: properties[constants.IS_HEALTH_FACTOR_DISADVANTAGED_L] ? - properties[constants.IS_HEALTH_FACTOR_DISADVANTAGED_L] : null, + properties[constants.IS_HEALTH_FACTOR_DISADVANTAGED_L] : null, }, { id: 'work-dev', titleText: intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.WORK_DEV), indicators: [lowMedInc, lingIso, unemploy, poverty, highSchool], isDisadvagtaged: properties[constants.IS_WORKFORCE_FACTOR_DISADVANTAGED_L] ? - properties[constants.IS_WORKFORCE_FACTOR_DISADVANTAGED_L] : null, + properties[constants.IS_WORKFORCE_FACTOR_DISADVANTAGED_L] : null, }, ]; @@ -308,6 +318,13 @@ const AreaDetail = ({properties}:IAreaDetailProps) => { title: , content: ( <> + {/* Category Header */} +
+
{intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.INDICATOR)}
+
{intl.formatMessage(EXPLORE_COPY.SIDE_PANEL_CATEGORY.PERCENTILE)}
+
+ + {/* Category Indicators */} {category.indicators.map((indicator:any, index:number) => { return ; })} diff --git a/client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap b/client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap index 40d5ac81..55b7b218 100644 --- a/client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap +++ b/client/src/components/AreaDetail/tests/__snapshots__/areaDetail.test.tsx.snap @@ -87,6 +87,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="climate-change" > +
+
+ Indicator +
+
+ Percentile (0-100) +
+
  • @@ -190,6 +198,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="clean-energy" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -273,6 +289,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="clean-transport" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -356,6 +380,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="sustain-house" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -441,6 +473,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="leg-pollute" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -542,6 +582,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="clean-water" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -607,6 +655,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="health-burdens" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -731,6 +787,14 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 hidden="" id="work-dev" > +
    +
    + Indicator +
    +
    + Percentile (0-100) +
    +
  • @@ -822,15 +886,16 @@ exports[`rendering of the AreaDetail checks if various text fields are visible 1 High school degree achievement rate
    - Percent of people ages 25 years or older whose education level is less than a high school diploma + Percent (not a percentile) of people ages 25 years or older whose education level is less than a + high school diploma
    N/A - - - + + % +
    • diff --git a/client/src/components/BetaBanner/__snapshots__/BetaBanner.test.tsx.snap b/client/src/components/BetaBanner/__snapshots__/BetaBanner.test.tsx.snap index a688be38..cbc3f79f 100644 --- a/client/src/components/BetaBanner/__snapshots__/BetaBanner.test.tsx.snap +++ b/client/src/components/BetaBanner/__snapshots__/BetaBanner.test.tsx.snap @@ -10,8 +10,8 @@ exports[`rendering of the BetaBanner checks if component renders 1`] = ` This is a beta site. - It is an early, in-progress version of the tool with limited datasets - that will be continuously updated. + It is an early, in-progress version of the tool with limited datasets that will + be regularly updated. diff --git a/client/src/components/Categories/__snapshots__/Categories.test.tsx.snap b/client/src/components/Categories/__snapshots__/Categories.test.tsx.snap index 50a9511f..167f5aa7 100644 --- a/client/src/components/Categories/__snapshots__/Categories.test.tsx.snap +++ b/client/src/components/Categories/__snapshots__/Categories.test.tsx.snap @@ -176,7 +176,7 @@ exports[`rendering of the Categories checks if component renders 1`] = ` low median home value is at or less than - 90th percentile OR at or above the 10th percentile for the + 90th percentile OR at or above the 90th percentile for the diff --git a/client/src/components/DatasetCard/index.tsx b/client/src/components/DatasetCard/index.tsx index f20223a5..52c14dfa 100644 --- a/client/src/components/DatasetCard/index.tsx +++ b/client/src/components/DatasetCard/index.tsx @@ -25,9 +25,7 @@ const DatasetCard = ({datasetCardProps}:IDatasetCardProps) => { {intl.formatMessage(METHODOLOGY_COPY.DATASET_CARD_LABELS.RESP_PARTY)} - - {datasetCardProps.respPartyLabel} - + {datasetCardProps.responsibleParty}
  • diff --git a/client/src/components/DatasetCard/tests/__snapshots__/datasetCard.test.tsx.snap b/client/src/components/DatasetCard/tests/__snapshots__/datasetCard.test.tsx.snap index 2006c358..cc95578d 100644 --- a/client/src/components/DatasetCard/tests/__snapshots__/datasetCard.test.tsx.snap +++ b/client/src/components/DatasetCard/tests/__snapshots__/datasetCard.test.tsx.snap @@ -10,9 +10,9 @@ exports[`rendering of indicator dataset card checks if component renders 1`] = `
    - Percent of a block group's population in households where household income is at or below - 200% of the federal poverty level. - + Percent of a census tract's population in households where household income is at or below + 200% of the federal poverty level. +
    • @@ -24,7 +24,7 @@ exports[`rendering of indicator dataset card checks if component renders 1`] = ` rel="noreferrer" target="_blank" > - Census's American Community Survey. + Census's American Community Survey
    • diff --git a/client/src/components/DatasetContainer/tests/__snapshots__/datasetContainer.test.tsx.snap b/client/src/components/DatasetContainer/tests/__snapshots__/datasetContainer.test.tsx.snap index 0ce9f9f5..d3e85ea9 100644 --- a/client/src/components/DatasetContainer/tests/__snapshots__/datasetContainer.test.tsx.snap +++ b/client/src/components/DatasetContainer/tests/__snapshots__/datasetContainer.test.tsx.snap @@ -40,9 +40,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
      - Percent of a block group's population in households where household income is at or below - 200% of the federal poverty level. - + Percent of a census tract's population in households where household income is at or below + 200% of the federal poverty level. +
      • @@ -54,7 +54,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Census's American Community Survey. + Census's American Community Survey
      • @@ -79,12 +79,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
        - Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing - the agriculture value at risk in a census tract by the total agriculture value in that census - tract. Fourteen natural hazards that have some link to climate change include: avalanche, - coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, - riverine flooding, strong wind, tornado, wildfire, and winter weather. - + Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing + the agriculture value at risk in a census tract by the total agriculture value in that census + tract. Fourteen natural hazards that have some link to climate change include: avalanche, + coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, + riverine flooding, strong wind, tornado, wildfire, and winter weather. +
        • @@ -121,12 +121,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
          - Percent of building value at risk from losses due to natural hazards. Calculated by dividing the - building value at risk in a census tract by the total building value in that census tract. - Fourteen natural hazards that have some link to climate change include: avalanche, coastal flooding, - cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine flooding, strong - wind, tornado, wildfire, and winter weather. - + Percent of building value at risk from losses due to natural hazards. Calculated by dividing the + building value at risk in a census tract by the total building value in that census tract. + Fourteen natural hazards that have some link to climate change include: avalanche, + coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, + riverine flooding, strong wind, tornado, wildfire, and winter weather. +
          • @@ -163,19 +163,18 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
            - Rate relative to the population in fatalities and injuries due to natural hazards each year. - Fourteen natural hazards that have some link to climate change include: avalanche, coastal - flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine - flooding, strong wind, tornado, wildfire, and winter weather. - Population loss is defined as the Spatial Hazard Events and Losses or National Centers - for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the - hazard occurrence. To combine fatalities and injuries for the computation of population loss value, - an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database - classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries - and fatalities are counted as population loss. This total number of injuries and fatalities - is then divided by the population in the census tract to get a per-capita rate of population risk. - - + Rate relative to the population in fatalities and injuries due to natural hazards each year. + Fourteen natural hazards that have some link to climate change include: avalanche, coastal + flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine + flooding, strong wind, tornado, wildfire, and winter weather. + Population loss is defined as the Spatial Hazard Events and Losses or National Centers + for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the + hazard occurrence. To combine fatalities and injuries for the computation of population loss value, + an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database + classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries + and fatalities are counted as population loss. This total number of injuries and fatalities + is then divided by the population in the census tract to get a per-capita rate of population risk. +
            • @@ -247,8 +246,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis PM2.5 in the air
              - Fine inhalable particles, with diameters that are generally - 2.5 micrometers and smaller. + + Fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller. +
              • @@ -260,8 +260,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) Office of Air - and Radiation (OAR) fusion of model and monitor data as compiled by EPA's EJSCREEN + + Environmental Protection Agency (EPA) Office of Air and Radiation (OAR) fusion of model and monitor + data as compiled by EPA's EJSCREEN, sourced from EPA National Air Toxics Assessment (NATA), 2017 + U.S. Department of Transportation (DOT) traffic data +
              • @@ -285,7 +288,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Diesel particulate matter exposure
                - Mixture of particles that is part of diesel exhaust in the air. + + Mixture of particles that is part of diesel exhaust in the air. +
                • @@ -297,8 +302,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA) - as compiled by EPA's EJSCREEN + + Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA) + as compiled by EPA's EJSCREEN +
                • @@ -322,8 +329,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Traffic proximity and volume
                  - Count of vehicles (average annual daily traffic) at major roads - within 500 meters, divided by distance in meters (not km). + + Count of vehicles (average annual daily traffic) at major roads + within 500 meters, divided by distance in meters (not km). +
                  • @@ -335,7 +344,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN + + Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN +
                  • @@ -360,9 +371,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                    - The percent of households in a census tract that are both earning less than 80% of HUD Area Median - Family Income by county and are paying greater than 30% of their income to housing costs. - + The percent of households in a census tract that are both earning less than 80% of HUD Area Median + Family Income by county and are paying greater than 30% of their income to housing costs. +
                    • @@ -374,8 +385,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Department of Housing & Urban Development’s - (HUD) Comprehensive Housing Affordability Strategy dataset + + Department of Housing & Urban Development’s + (HUD) Comprehensive Housing Affordability Strategy dataset +
                    • @@ -400,8 +413,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                      - Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in - tracts with median home values less than 90th percentile + Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in + tracts with median home values less than 90th percentile +
                      • @@ -437,7 +451,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Low median home value
                        - Median home value of owner-occupied housing units in the census tract. + + Median home value of owner-occupied housing units in the census tract. +
                        • @@ -474,9 +490,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                          - Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large - Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers. - + Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large + Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers. +
                          • @@ -489,16 +505,16 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis target="_blank" > - Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities - (TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN - + Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities + (TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN +
                          • Date range: - 2015-2020 + 2020
                          • @@ -516,8 +532,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                            - Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one - beyond 5 km), each divided by distance in kilometers. + Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one + beyond 5 km), each divided by distance in kilometers. +
                            • @@ -529,7 +546,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN + + Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN +
                            • @@ -554,8 +573,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                              - Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest - one beyond 5 km), each divided by distance in kilometers. + Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest + one beyond 5 km), each divided by distance in kilometers. +
                              • @@ -567,7 +587,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN + + Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN +
                              • @@ -580,7 +602,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Affordable and sustainable housing methodology + Reduction and remediation of legacy pollution methodology
                              @@ -591,8 +613,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Wastewater discharge
                              - Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at - stream segments within 500 meters, divided by distance in kilometers (km). + + Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at + stream segments within 500 meters, divided by distance in kilometers (km). +
                              • @@ -604,8 +628,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) Risk-Screening - Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN + + Environmental Protection Agency (EPA) Risk-Screening + Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN +
                              • @@ -629,10 +655,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Asthma
                                - Weighted percent of people who answer “yes” both - to both of the following questions: “Have you ever been told by a doctor, - nurse, or other health professional that you have asthma?” and the question - “Do you still have asthma?” + + Weighted percent of people who answer “yes” to both of the following questions: “Have you ever + been told by a doctor, nurse, or other health professional that you have asthma?” and the question + “Do you still have asthma?” +
                                • @@ -644,7 +671,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Centers for Disease Control and Prevention (CDC) PLACES + + Centers for Disease Control and Prevention (CDC) PLACES +
                                • @@ -668,9 +697,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Diabetes
                                  - Weighted percent of people ages 18 years and older who report having ever been - told by a doctor, nurse, or other health professionals that they have - diabetes other than diabetes during pregnancy. + + Weighted percent of people ages 18 years and older who report having ever been + told by a doctor, nurse, or other health professionals that they have + diabetes other than diabetes during pregnancy. +
                                  • @@ -682,7 +713,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Centers for Disease Control and Prevention (CDC) PLACES + + Centers for Disease Control and Prevention (CDC) PLACES +
                                  • @@ -706,9 +739,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Heart disease
                                    - Weighted percent of people ages 18 years and older who report ever having been told - by a doctor, nurse, or other health professionals that they had angina or - coronary heart disease. + + Weighted percent of people ages 18 years and older who report ever having been told + by a doctor, nurse, or other health professionals that they had angina or + coronary heart disease. +
                                    • @@ -720,7 +755,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Centers for Disease Control and Prevention (CDC) PLACES + + Centers for Disease Control and Prevention (CDC) PLACES +
                                    • @@ -745,13 +782,22 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                      - Average number of years of life a person who has attained a given age can expect to live. - Note: Unlike most of the other datasets, high values of this indicator indicate low burdens. - For percentile calculations, the percentile is calculated in reverse order, so that the tract with - the highest median income relative to area median income (lowest burden on this measure) is at the - 0th percentile, and the tract with the lowest median income relative to area median income - (highest burden on this measure) is at the 100th percentile. - + Average number of years of life a person who has attained a given age can expect to live. + +

                                      + + Note: + + + Unlike most of the other datasets, high values of this indicator indicate low burdens. + For percentile calculations, the percentile is calculated in reverse order, so that the tract with + the highest median income relative to area median income (lowest burden on this measure) is at the + 0th percentile, and the tract with the lowest median income relative to area median income + (highest burden on this measure) is at the 100th percentile. + +

                                      + +
                                      • @@ -763,7 +809,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP) + + CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP) +
                                      • @@ -787,7 +835,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Low median Income
                                        - Median income of the census tract calculated as a percent of the area’s median income. + + Median income of the census tract calculated as a percent of the area’s median income. +
                                        • @@ -812,7 +862,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        @@ -824,8 +874,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                        - The percent of limited speaking households, which are households where no one over age 14 speaks English well. - + The percent of limited speaking households, which are households where no one over age 14 speaks English well. +
                                        • @@ -850,7 +900,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        @@ -861,7 +911,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Unemployment
                                        - Number of unemployed people as a percentage of the civilian labor force + + Number of unemployed people as a percentage of the civilian labor force +
                                        • @@ -886,7 +938,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        @@ -897,7 +949,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Poverty
                                        - Percent of a tract's population in households where the household income is at or below 100% of the federal poverty level. + + Percent of a tract's population in households where the household income is at or below 100% of + the federal poverty level. +
                                        • @@ -922,7 +977,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        @@ -933,8 +988,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis High school degree achievement rate
                                        - Percent of people ages 25 years or older in a census tract whose - education level is less than a high school diploma. + + Percent (not percentile) of people ages 25 years or older in a census tract whose + education level is less than a high school diploma. +
                                        • @@ -959,7 +1016,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                        diff --git a/client/src/components/Indicator/Indicator.tsx b/client/src/components/Indicator/Indicator.tsx index e19e6c9c..8d397196 100644 --- a/client/src/components/Indicator/Indicator.tsx +++ b/client/src/components/Indicator/Indicator.tsx @@ -43,9 +43,12 @@ const Indicator = ({indicator}:IIndicator) => {
                                        {readablePercentile(indicator.value)} - - {getSuperscriptOrdinal(readablePercentile(indicator.value))} - + {indicator.isPercent ? + {`%`} : + + {getSuperscriptOrdinal(readablePercentile(indicator.value))} + + }
                                        • diff --git a/client/src/components/J40Header/__snapshots__/J40Header.test.tsx.snap b/client/src/components/J40Header/__snapshots__/J40Header.test.tsx.snap index 3c9912ca..7e8d5369 100644 --- a/client/src/components/J40Header/__snapshots__/J40Header.test.tsx.snap +++ b/client/src/components/J40Header/__snapshots__/J40Header.test.tsx.snap @@ -150,8 +150,8 @@ exports[`rendering of the J40Header checks if component renders 1`] = ` This is a beta site. - It is an early, in-progress version of the tool with limited datasets - that will be continuously updated. + It is an early, in-progress version of the tool with limited datasets that will + be regularly updated. diff --git a/client/src/data/copy/common.tsx b/client/src/data/copy/common.tsx index 689c8584..48d3fa2d 100644 --- a/client/src/data/copy/common.tsx +++ b/client/src/data/copy/common.tsx @@ -9,8 +9,8 @@ export const BETA_BANNER = defineMessages({ }, INFO: { id: 'banner.beta.info', - defaultMessage: `It is an early, in-progress version of the tool with limited datasets - that will be continuously updated.`, + defaultMessage: `It is an early, in-progress version of the tool with limited datasets that will + be regularly updated.`, description: 'the main info of the beta banner', }, }); diff --git a/client/src/data/copy/explore.tsx b/client/src/data/copy/explore.tsx index ccb380de..e55b1ff4 100644 --- a/client/src/data/copy/explore.tsx +++ b/client/src/data/copy/explore.tsx @@ -237,6 +237,16 @@ export const COMMUNITY = { }; export const SIDE_PANEL_CATEGORY = defineMessages({ + INDICATOR: { + id: 'areaDetail.category.header.indicator', + defaultMessage: 'Indicator', + description: 'header for each category', + }, + PERCENTILE: { + id: 'areaDetail.category.header.percentile', + defaultMessage: 'Percentile (0-100)', + description: 'header for each category', + }, CLIMATE: { id: 'areaDetail.indicator.title.climate', defaultMessage: 'Climate change', @@ -550,7 +560,8 @@ export const SIDE_PANEL_INDICATOR_DESCRIPTION = defineMessages({ HIGH_SKL: { id: 'areaDetail.indicator.description.high.school', defaultMessage: ` - Percent of people ages 25 years or older whose education level is less than a high school diploma + Percent (not a percentile) of people ages 25 years or older whose education level is less than a + high school diploma `, description: 'Percent of people ages 25 years or older whose education level is less than a high school diploma', }, @@ -597,7 +608,7 @@ export const NOTE_ON_TERRITORIES = { PARA_1: , + }, CLIMATE_CHANGE: { + METHODOLOGY: , TITLE: , }, CLEAN_ENERGY: { + METHODOLOGY: , TITLE: , }, CLEAN_TRANSPORT: { + METHODOLOGY: , TITLE: , }, AFFORDABLE_HOUSING: { + METHODOLOGY: , TITLE: , }, LEGACY_POLLUTION: { + METHODOLOGY: , TITLE: , }, CLEAN_WATER: { + METHODOLOGY: , TITLE: , }, HEALTH_BURDENS: { + METHODOLOGY: , TITLE: , }, WORKFORCE_DEV: { + METHODOLOGY: , TITLE: + {`Census's American Community Survey`} + , + }} + />, + FEMA: + {`Federal Emergency Management Agency (FEMA)`} + , + }} + />, + DOE: + {`Department of Energy (DOE) LEAD Score`} + , + }} + />, + EPA_OAR: + {` + Environmental Protection Agency (EPA) Office of Air and Radiation (OAR) fusion of model and monitor + data as compiled by EPA's EJSCREEN, sourced from EPA National Air Toxics Assessment (NATA), 2017 + U.S. Department of Transportation (DOT) traffic data + `} + , + }} + />, + EPA_NATA: + {` + Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA) + as compiled by EPA's EJSCREEN + `} + , + }} + />, + DOT_EPA: + {` + Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN + `} + , + }} + />, + HUD: + {` + Department of Housing & Urban Development’s + (HUD) Comprehensive Housing Affordability Strategy dataset + `} + , + }} + />, + EPA_TSDF: + {` + Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities + (TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN + `} + , + }} + />, + EPA_CERCLIS: + {` + Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN + `} + , + }} + />, + EPA_RMP: + {` + Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN + `} + , + }} + />, + EPA_RSEI: + {` + Environmental Protection Agency (EPA) Risk-Screening + Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN + `} + , + }} + />, + CDC_PLACES: + {` + Centers for Disease Control and Prevention (CDC) PLACES + `} + , + }} + />, + CDC_SLEEP: + {` + CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP) + `} + , + }} + />, +}; + +export const DATE_RANGE = { + TEN_PLUS_5: '2010-2015', + FOURTEEN: '2014', + FOURTEEN_PLUS_4: '2014-2018', + FOURTEEN_PLUS_7: '2014-2021', + FIFETEEN_PLUS_4: '2015-2019', + SIXTEEN_PLUS_3: '2016-2019', + SIXTEEN_PLUS_4: '2016-2020', + SEVENTEEN: '2017', + EIGHTEEN: '2018', + TWENTY: '2020', +}; + export const INDICATORS = [ { domID: 'low-income', indicator: 'Low income', - description: ` - Percent of a block group's population in households where household income is at or below - 200% of the federal poverty level. - `, - usedIn: - , - respPartyLabel: - , - dataSourceURL: `https://www.census.gov/programs-surveys/acs`, - dateRange: `2015-2019`, + description: , + usedIn: CATEGORIES.ALL.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS, + dateRange: DATE_RANGE.FIFETEEN_PLUS_4, }, { domID: 'exp-agr-loss-rate', indicator: 'Expected agriculture loss rate', - description: ` - Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing - the agriculture value at risk in a census tract by the total agriculture value in that census - tract. Fourteen natural hazards that have some link to climate change include: avalanche, - coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, - riverine flooding, strong wind, tornado, wildfire, and winter weather. - `, - usedIn: `Climate change methodology`, - respPartyLabel: `Federal Emergency Management Agency (FEMA)`, - dataSourceURL: `https://hazards.fema.gov/nri/expected-annual-loss`, - dateRange: `2014-2021`, + description: , + usedIn: CATEGORIES.CLIMATE_CHANGE.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.FEMA, + dateRange: DATE_RANGE.FOURTEEN_PLUS_7, }, { domID: 'exp-bld-loss-rate', indicator: 'Expected building loss rate', - description: ` - Percent of building value at risk from losses due to natural hazards. Calculated by dividing the - building value at risk in a census tract by the total building value in that census tract. - Fourteen natural hazards that have some link to climate change include: avalanche, coastal flooding, - cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine flooding, strong - wind, tornado, wildfire, and winter weather. - `, - usedIn: `Climate change methodology`, - respPartyLabel: `Federal Emergency Management Agency (FEMA)`, - dataSourceURL: `https://hazards.fema.gov/nri/expected-annual-loss`, - dateRange: `2014-2021`, + description: , + usedIn: CATEGORIES.CLIMATE_CHANGE.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.FEMA, + dateRange: DATE_RANGE.FOURTEEN_PLUS_7, }, { domID: 'exp-pop-loss-rate', indicator: 'Expected population loss rate', - description: ` - Rate relative to the population in fatalities and injuries due to natural hazards each year. - Fourteen natural hazards that have some link to climate change include: avalanche, coastal - flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine - flooding, strong wind, tornado, wildfire, and winter weather. - Population loss is defined as the Spatial Hazard Events and Losses or National Centers - for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the - hazard occurrence. To combine fatalities and injuries for the computation of population loss value, - an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database - classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries - and fatalities are counted as population loss. This total number of injuries and fatalities - is then divided by the population in the census tract to get a per-capita rate of population risk. - - `, - usedIn: `Climate change methodology`, - respPartyLabel: `Federal Emergency Management Agency (FEMA)`, - dataSourceURL: `https://hazards.fema.gov/nri/expected-annual-loss`, - dateRange: `2014-2021`, + description: , + usedIn: CATEGORIES.CLIMATE_CHANGE.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.FEMA, + dateRange: DATE_RANGE.FOURTEEN_PLUS_7, }, { domID: 'energy-burden', indicator: 'Energy cost burden', - description: `Average annual energy cost ($) divided by household income.`, - usedIn: `Clean energy and energy efficiency methodology`, - respPartyLabel: `Department of Energy (DOE) LEAD Score`, - dataSourceURL: `https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool`, - dateRange: `2018`, + description: , + usedIn: CATEGORIES.CLEAN_ENERGY.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.DOE, + dateRange: DATE_RANGE.EIGHTEEN, }, { domID: 'pm-25', indicator: 'PM2.5 in the air', - description: `Fine inhalable particles, with diameters that are generally - 2.5 micrometers and smaller.`, - usedIn: `Clean energy and energy efficiency methodology`, - respPartyLabel: `Environmental Protection Agency (EPA) Office of Air - and Radiation (OAR) fusion of model and monitor data as compiled by EPA's EJSCREEN`, - dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`, - dateRange: `2017`, + description: , + usedIn: CATEGORIES.CLEAN_ENERGY.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.EPA_OAR, + dateRange: DATE_RANGE.SEVENTEEN, }, { domID: 'diesel-pm', indicator: 'Diesel particulate matter exposure', - description: `Mixture of particles that is part of diesel exhaust in the air.`, - usedIn: `Clean transportation methodology`, - respPartyLabel: `Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA) - as compiled by EPA's EJSCREEN`, - dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`, - dateRange: `2014`, + description: , + usedIn: CATEGORIES.CLEAN_TRANSPORT.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.EPA_NATA, + dateRange: DATE_RANGE.FOURTEEN, }, { domID: 'traffic-vol', indicator: 'Traffic proximity and volume', - description: `Count of vehicles (average annual daily traffic) at major roads - within 500 meters, divided by distance in meters (not km).`, - usedIn: `Clean transportation methodology`, - respPartyLabel: `Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN`, - dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`, - dateRange: `2017`, + description: , + usedIn: CATEGORIES.CLEAN_TRANSPORT.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.DOT_EPA, + dateRange: DATE_RANGE.SEVENTEEN, }, { domID: 'house-burden', indicator: 'Housing cost burden', - description: ` - The percent of households in a census tract that are both earning less than 80% of HUD Area Median - Family Income by county and are paying greater than 30% of their income to housing costs. - `, - usedIn: `Affordable and sustainable housing methodology`, - respPartyLabel: `Department of Housing & Urban Development’s - (HUD) Comprehensive Housing Affordability Strategy dataset`, - dataSourceURL: `https://www.huduser.gov/portal/datasets/cp.html`, - dateRange: `2014-2018`, + description: , + usedIn: CATEGORIES.AFFORDABLE_HOUSING.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.HUD, + dateRange: DATE_RANGE.FOURTEEN_PLUS_4, }, { domID: 'lead-paint', indicator: 'Lead paint', - description: ` - Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in - tracts with median home values less than 90th percentile `, - usedIn: `Affordable and sustainable housing methodology`, - respPartyLabel: `Census's American Community Survey`, - dataSourceURL: `https://www.census.gov/programs-surveys/acs`, - dateRange: `2015-2019`, + description: , + usedIn: CATEGORIES.AFFORDABLE_HOUSING.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS, + dateRange: DATE_RANGE.FIFETEEN_PLUS_4, }, { domID: 'median-home', indicator: 'Low median home value', - description: `Median home value of owner-occupied housing units in the census tract.`, - usedIn: `Affordable and sustainable housing methodology`, - respPartyLabel: `Census's American Community Survey`, - dataSourceURL: `https://www.census.gov/programs-surveys/acs`, - dateRange: `2015-2019`, + description: , + usedIn: CATEGORIES.AFFORDABLE_HOUSING.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS, + dateRange: DATE_RANGE.FIFETEEN_PLUS_4, }, { domID: 'prox-haz', indicator: 'Proximity to hazardous waste facilities', - description: ` - Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large - Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers. - `, - usedIn: `Reduction and remediation of legacy pollution methodology`, - respPartyLabel: ` - Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities - (TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN - `, - dataSourceURL: `https://enviro.epa.gov/facts/rcrainfo/search.html`, - dateRange: `2015-2020`, + description: , + usedIn: CATEGORIES.LEGACY_POLLUTION.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.EPA_TSDF, + dateRange: DATE_RANGE.TWENTY, }, { domID: 'prox-npl', indicator: 'Proximity to National Priorities List (NPL) sites', - description: ` - Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one - beyond 5 km), each divided by distance in kilometers.`, - usedIn: `Reduction and remediation of legacy pollution methodology`, - respPartyLabel: `Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN`, - dataSourceURL: `https://enviro.epa.gov/facts/rcrainfo/search.html`, - dateRange: `2020`, + description: , + usedIn: CATEGORIES.LEGACY_POLLUTION.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.EPA_CERCLIS, + dateRange: DATE_RANGE.TWENTY, }, { domID: 'prox-rmp', indicator: 'Proximity to Risk Management Plan (RMP) facilities', - description: ` - Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest - one beyond 5 km), each divided by distance in kilometers.`, - usedIn: `Affordable and sustainable housing methodology`, - respPartyLabel: `Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN`, - dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`, - dateRange: `2020`, + description: , + usedIn: CATEGORIES.LEGACY_POLLUTION.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.EPA_RMP, + dateRange: DATE_RANGE.TWENTY, }, { domID: 'waste-water', indicator: 'Wastewater discharge', - description: `Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at - stream segments within 500 meters, divided by distance in kilometers (km).`, - usedIn: `Critical clean water and waste infrastructure`, - respPartyLabel: `Environmental Protection Agency (EPA) Risk-Screening - Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN`, - dataSourceURL: `https://www.epa.gov/ejscreen/technical-documentation-ejscreen`, - dateRange: `2020`, + description: , + usedIn: CATEGORIES.CLEAN_WATER.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.EPA_RSEI, + dateRange: DATE_RANGE.TWENTY, }, { domID: 'asthma', indicator: 'Asthma', - description: `Weighted percent of people who answer “yes” both - to both of the following questions: “Have you ever been told by a doctor, - nurse, or other health professional that you have asthma?” and the question - “Do you still have asthma?”`, - usedIn: `Health burdens methodology`, - respPartyLabel: `Centers for Disease Control and Prevention (CDC) PLACES`, - dataSourceURL: `https://www.cdc.gov/places/index.html`, - dateRange: `2016-2019`, + description: , + usedIn: CATEGORIES.HEALTH_BURDENS.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CDC_PLACES, + dateRange: DATE_RANGE.SIXTEEN_PLUS_3, }, { domID: 'diabetes', indicator: 'Diabetes', - description: `Weighted percent of people ages 18 years and older who report having ever been - told by a doctor, nurse, or other health professionals that they have - diabetes other than diabetes during pregnancy.`, - usedIn: `Health burdens methodology`, - respPartyLabel: `Centers for Disease Control and Prevention (CDC) PLACES`, - dataSourceURL: `https://www.cdc.gov/places/index.html`, - dateRange: `2016-2019`, + description: , + usedIn: CATEGORIES.HEALTH_BURDENS.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CDC_PLACES, + dateRange: DATE_RANGE.SIXTEEN_PLUS_3, }, { domID: 'heart-disease', indicator: 'Heart disease', - description: `Weighted percent of people ages 18 years and older who report ever having been told - by a doctor, nurse, or other health professionals that they had angina or - coronary heart disease.`, - usedIn: `Health burdens methodology`, - respPartyLabel: `Centers for Disease Control and Prevention (CDC) PLACES`, - dataSourceURL: `https://www.cdc.gov/places/index.html`, - dateRange: `2016-2019`, + description: , + usedIn: CATEGORIES.HEALTH_BURDENS.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CDC_PLACES, + dateRange: DATE_RANGE.SIXTEEN_PLUS_3, }, { domID: 'life-exp', indicator: 'Low life expectancy', - description: ` - Average number of years of life a person who has attained a given age can expect to live. - Note: Unlike most of the other datasets, high values of this indicator indicate low burdens. - For percentile calculations, the percentile is calculated in reverse order, so that the tract with - the highest median income relative to area median income (lowest burden on this measure) is at the - 0th percentile, and the tract with the lowest median income relative to area median income - (highest burden on this measure) is at the 100th percentile. - `, - usedIn: `Health burdens methodology`, - respPartyLabel: `CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP)`, - dataSourceURL: `https://www.cdc.gov/nchs/nvss/usaleep/usaleep.html#data`, - dateRange: `2010-2015`, + description: Note:{` + Unlike most of the other datasets, high values of this indicator indicate low burdens. + For percentile calculations, the percentile is calculated in reverse order, so that the tract with + the highest median income relative to area median income (lowest burden on this measure) is at the + 0th percentile, and the tract with the lowest median income relative to area median income + (highest burden on this measure) is at the 100th percentile. + `}

                                        , + }} + />, + usedIn: CATEGORIES.HEALTH_BURDENS.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CDC_SLEEP, + dateRange: DATE_RANGE.TEN_PLUS_5, }, { domID: 'low-med-inc', indicator: 'Low median Income', - description: `Median income of the census tract calculated as a percent of the area’s median income.`, - usedIn: `Training and workforce development`, - respPartyLabel: `Census's American Community Survey`, - dataSourceURL: `https://www.census.gov/programs-surveys/acs`, - dateRange: `2015-2019`, + description: , + usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS, + dateRange: DATE_RANGE.FIFETEEN_PLUS_4, }, { domID: 'ling-iso', indicator: 'Linguistic Isolation', - description: ` - The percent of limited speaking households, which are households where no one over age 14 speaks English well. - `, - usedIn: `Training and workforce development`, - respPartyLabel: `Census's American Community Survey`, - dataSourceURL: `https://www.census.gov/programs-surveys/acs`, - dateRange: `2015-2019`, + description: , + usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS, + dateRange: DATE_RANGE.FIFETEEN_PLUS_4, }, { domID: 'unemploy', indicator: 'Unemployment', - description: `Number of unemployed people as a percentage of the civilian labor force`, - usedIn: `Training and workforce development`, - respPartyLabel: `Census's American Community Survey`, - dataSourceURL: `https://www.census.gov/programs-surveys/acs`, - dateRange: `2015-2019`, + description: , + usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS, + dateRange: DATE_RANGE.FIFETEEN_PLUS_4, }, { domID: 'poverty', indicator: 'Poverty', - description: `Percent of a tract's population in households where the household income` + - ` is at or below 100% of the federal poverty level.`, - usedIn: `Training and workforce development`, - respPartyLabel: `Census's American Community Survey`, - dataSourceURL: `https://www.census.gov/programs-surveys/acs`, - dateRange: `2015-2019`, + description: , + usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS, + dateRange: DATE_RANGE.FIFETEEN_PLUS_4, }, { domID: 'high-school', indicator: 'High school degree achievement rate', - description: `Percent of people ages 25 years or older in a census tract whose - education level is less than a high school diploma.`, - usedIn: `Training and workforce development`, - respPartyLabel: `Census's American Community Survey`, - dataSourceURL: `https://www.census.gov/programs-surveys/acs`, - dateRange: `2015-2019`, + description: , + usedIn: CATEGORIES.WORKFORCE_DEV.METHODOLOGY, + responsibleParty: RESPONSIBLE_PARTIES.CENSUS_ACS, + dateRange: DATE_RANGE.FIFETEEN_PLUS_4, + isPercent: true, }, ]; diff --git a/client/src/pages/__snapshots__/contact.test.tsx.snap b/client/src/pages/__snapshots__/contact.test.tsx.snap index ca06f9bb..0150d5ea 100644 --- a/client/src/pages/__snapshots__/contact.test.tsx.snap +++ b/client/src/pages/__snapshots__/contact.test.tsx.snap @@ -150,8 +150,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis This is a beta site. - It is an early, in-progress version of the tool with limited datasets - that will be continuously updated. + It is an early, in-progress version of the tool with limited datasets that will + be regularly updated. diff --git a/client/src/pages/__snapshots__/index.test.tsx.snap b/client/src/pages/__snapshots__/index.test.tsx.snap index b1fc8bf6..1e1368c7 100644 --- a/client/src/pages/__snapshots__/index.test.tsx.snap +++ b/client/src/pages/__snapshots__/index.test.tsx.snap @@ -150,8 +150,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis This is a beta site. - It is an early, in-progress version of the tool with limited datasets - that will be continuously updated. + It is an early, in-progress version of the tool with limited datasets that will + be regularly updated. diff --git a/client/src/pages/__snapshots__/methodology.test.tsx.snap b/client/src/pages/__snapshots__/methodology.test.tsx.snap index 44fb8798..5a098225 100644 --- a/client/src/pages/__snapshots__/methodology.test.tsx.snap +++ b/client/src/pages/__snapshots__/methodology.test.tsx.snap @@ -150,8 +150,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis This is a beta site. - It is an early, in-progress version of the tool with limited datasets - that will be continuously updated. + It is an early, in-progress version of the tool with limited datasets that will + be regularly updated. @@ -575,7 +575,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis low median home value is at or less than - 90th percentile OR at or above the 10th percentile for the + 90th percentile OR at or above the 90th percentile for the @@ -836,9 +836,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                        - Percent of a block group's population in households where household income is at or below - 200% of the federal poverty level. - + Percent of a census tract's population in households where household income is at or below + 200% of the federal poverty level. +
                                        • @@ -850,7 +850,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Census's American Community Survey. + Census's American Community Survey
                                        • @@ -875,12 +875,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                          - Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing - the agriculture value at risk in a census tract by the total agriculture value in that census - tract. Fourteen natural hazards that have some link to climate change include: avalanche, - coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, - riverine flooding, strong wind, tornado, wildfire, and winter weather. - + Percent of agriculture value at risk from losses due to natural hazards. Calculated by dividing + the agriculture value at risk in a census tract by the total agriculture value in that census + tract. Fourteen natural hazards that have some link to climate change include: avalanche, + coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, + riverine flooding, strong wind, tornado, wildfire, and winter weather. +
                                          • @@ -917,12 +917,12 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                            - Percent of building value at risk from losses due to natural hazards. Calculated by dividing the - building value at risk in a census tract by the total building value in that census tract. - Fourteen natural hazards that have some link to climate change include: avalanche, coastal flooding, - cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine flooding, strong - wind, tornado, wildfire, and winter weather. - + Percent of building value at risk from losses due to natural hazards. Calculated by dividing the + building value at risk in a census tract by the total building value in that census tract. + Fourteen natural hazards that have some link to climate change include: avalanche, + coastal flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, + riverine flooding, strong wind, tornado, wildfire, and winter weather. +
                                            • @@ -959,19 +959,18 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                              - Rate relative to the population in fatalities and injuries due to natural hazards each year. - Fourteen natural hazards that have some link to climate change include: avalanche, coastal - flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine - flooding, strong wind, tornado, wildfire, and winter weather. - Population loss is defined as the Spatial Hazard Events and Losses or National Centers - for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the - hazard occurrence. To combine fatalities and injuries for the computation of population loss value, - an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database - classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries - and fatalities are counted as population loss. This total number of injuries and fatalities - is then divided by the population in the census tract to get a per-capita rate of population risk. - - + Rate relative to the population in fatalities and injuries due to natural hazards each year. + Fourteen natural hazards that have some link to climate change include: avalanche, coastal + flooding, cold wave, drought, hail, heat wave, hurricane, ice storm, landslide, riverine + flooding, strong wind, tornado, wildfire, and winter weather. + Population loss is defined as the Spatial Hazard Events and Losses or National Centers + for Environmental Information’s (NCEI) reported number of fatalities and injuries caused by the + hazard occurrence. To combine fatalities and injuries for the computation of population loss value, + an injury is counted as one-tenth (1/10) of a fatality. The NCEI Storm Events Database + classifies injuries and fatalities as direct or indirect. Both direct and indirect injuries + and fatalities are counted as population loss. This total number of injuries and fatalities + is then divided by the population in the census tract to get a per-capita rate of population risk. +
                                              • @@ -1043,8 +1042,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis PM2.5 in the air
                                                - Fine inhalable particles, with diameters that are generally - 2.5 micrometers and smaller. + + Fine inhalable particles, with diameters that are generally 2.5 micrometers and smaller. +
                                                • @@ -1056,8 +1056,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) Office of Air - and Radiation (OAR) fusion of model and monitor data as compiled by EPA's EJSCREEN + + Environmental Protection Agency (EPA) Office of Air and Radiation (OAR) fusion of model and monitor + data as compiled by EPA's EJSCREEN, sourced from EPA National Air Toxics Assessment (NATA), 2017 + U.S. Department of Transportation (DOT) traffic data +
                                                • @@ -1081,7 +1084,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Diesel particulate matter exposure
                                                  - Mixture of particles that is part of diesel exhaust in the air. + + Mixture of particles that is part of diesel exhaust in the air. +
                                                  • @@ -1093,8 +1098,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA) - as compiled by EPA's EJSCREEN + + Environmental Protection Agency (EPA) National Air Toxics Assessment (NATA) + as compiled by EPA's EJSCREEN +
                                                  • @@ -1118,8 +1125,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Traffic proximity and volume
                                                    - Count of vehicles (average annual daily traffic) at major roads - within 500 meters, divided by distance in meters (not km). + + Count of vehicles (average annual daily traffic) at major roads + within 500 meters, divided by distance in meters (not km). +
                                                    • @@ -1131,7 +1140,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN + + Department of Transportation (DOT) traffic data as compiled by EPA's EJSCREEN +
                                                    • @@ -1156,9 +1167,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                      - The percent of households in a census tract that are both earning less than 80% of HUD Area Median - Family Income by county and are paying greater than 30% of their income to housing costs. - + The percent of households in a census tract that are both earning less than 80% of HUD Area Median + Family Income by county and are paying greater than 30% of their income to housing costs. +
                                                      • @@ -1170,8 +1181,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Department of Housing & Urban Development’s - (HUD) Comprehensive Housing Affordability Strategy dataset + + Department of Housing & Urban Development’s + (HUD) Comprehensive Housing Affordability Strategy dataset +
                                                      • @@ -1196,8 +1209,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                        - Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in - tracts with median home values less than 90th percentile + Percent of housing units built pre-1960, used as an indicator of potential lead paint exposure in + tracts with median home values less than 90th percentile +
                                                        • @@ -1233,7 +1247,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Low median home value
                                                          - Median home value of owner-occupied housing units in the census tract. + + Median home value of owner-occupied housing units in the census tract. +
                                                          • @@ -1270,9 +1286,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                            - Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large - Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers. - + Count of hazardous waste facilities (Treatment, Storage, and Disposal Facilities and Large + Quantity Generators) within 5 km (or nearest beyond 5 km), each divided by distance in kilometers. +
                                                            • @@ -1285,16 +1301,16 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis target="_blank" > - Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities - (TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN - + Environmental Protection Agency (EPA) Treatment Storage, and Disposal Facilities + (TSDF) data calculated from EPA RCRA info database as compiled by EPA’s EJSCREEN +
                                                            • Date range: - 2015-2020 + 2020
                                                            • @@ -1312,8 +1328,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                              - Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one - beyond 5 km), each divided by distance in kilometers. + Count of proposed or listed NPL - also known as superfund - sites within 5 km (or nearest one + beyond 5 km), each divided by distance in kilometers. +
                                                              • @@ -1325,7 +1342,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN + + Environmental Protection Agency (EPA) CERCLIS database as compiled by EPA’s EJSCREEN +
                                                              • @@ -1350,8 +1369,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                                - Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest - one beyond 5 km), each divided by distance in kilometers. + Count of RMP (potential chemical accident management plan) facilities within 5 km (or nearest + one beyond 5 km), each divided by distance in kilometers. +
                                                                • @@ -1363,7 +1383,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN + + Environmental Protection Agency (EPA) RMP database as compiled by EPA’s EJSCREEN +
                                                                • @@ -1376,7 +1398,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Affordable and sustainable housing methodology + Reduction and remediation of legacy pollution methodology
                                                                @@ -1387,8 +1409,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Wastewater discharge
                                                                - Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at - stream segments within 500 meters, divided by distance in kilometers (km). + + Risk-Screening Environmental Indicators (RSEI) modeled Toxic Concentrations at + stream segments within 500 meters, divided by distance in kilometers (km). +
                                                                • @@ -1400,8 +1424,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Environmental Protection Agency (EPA) Risk-Screening - Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN + + Environmental Protection Agency (EPA) Risk-Screening + Environmental Indicators (RSEI) Model as compiled by EPA's EJSCREEN +
                                                                • @@ -1425,10 +1451,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Asthma
                                                                  - Weighted percent of people who answer “yes” both - to both of the following questions: “Have you ever been told by a doctor, - nurse, or other health professional that you have asthma?” and the question - “Do you still have asthma?” + + Weighted percent of people who answer “yes” to both of the following questions: “Have you ever + been told by a doctor, nurse, or other health professional that you have asthma?” and the question + “Do you still have asthma?” +
                                                                  • @@ -1440,7 +1467,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Centers for Disease Control and Prevention (CDC) PLACES + + Centers for Disease Control and Prevention (CDC) PLACES +
                                                                  • @@ -1464,9 +1493,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Diabetes
                                                                    - Weighted percent of people ages 18 years and older who report having ever been - told by a doctor, nurse, or other health professionals that they have - diabetes other than diabetes during pregnancy. + + Weighted percent of people ages 18 years and older who report having ever been + told by a doctor, nurse, or other health professionals that they have + diabetes other than diabetes during pregnancy. +
                                                                    • @@ -1478,7 +1509,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Centers for Disease Control and Prevention (CDC) PLACES + + Centers for Disease Control and Prevention (CDC) PLACES +
                                                                    • @@ -1502,9 +1535,11 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Heart disease
                                                                      - Weighted percent of people ages 18 years and older who report ever having been told - by a doctor, nurse, or other health professionals that they had angina or - coronary heart disease. + + Weighted percent of people ages 18 years and older who report ever having been told + by a doctor, nurse, or other health professionals that they had angina or + coronary heart disease. +
                                                                      • @@ -1516,7 +1551,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - Centers for Disease Control and Prevention (CDC) PLACES + + Centers for Disease Control and Prevention (CDC) PLACES +
                                                                      • @@ -1541,13 +1578,22 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                                        - Average number of years of life a person who has attained a given age can expect to live. - Note: Unlike most of the other datasets, high values of this indicator indicate low burdens. - For percentile calculations, the percentile is calculated in reverse order, so that the tract with - the highest median income relative to area median income (lowest burden on this measure) is at the - 0th percentile, and the tract with the lowest median income relative to area median income - (highest burden on this measure) is at the 100th percentile. - + Average number of years of life a person who has attained a given age can expect to live. + +

                                                                        + + Note: + + + Unlike most of the other datasets, high values of this indicator indicate low burdens. + For percentile calculations, the percentile is calculated in reverse order, so that the tract with + the highest median income relative to area median income (lowest burden on this measure) is at the + 0th percentile, and the tract with the lowest median income relative to area median income + (highest burden on this measure) is at the 100th percentile. + +

                                                                        + +
                                                                        • @@ -1559,7 +1605,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis rel="noreferrer" target="_blank" > - CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP) + + CDC’s U.S. Small-area Life Expectancy Estimates Project (USALEEP) +
                                                                        • @@ -1583,7 +1631,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Low median Income
                                                                          - Median income of the census tract calculated as a percent of the area’s median income. + + Median income of the census tract calculated as a percent of the area’s median income. +
                                                                          • @@ -1608,7 +1658,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          @@ -1620,8 +1670,8 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis
                                                                          - The percent of limited speaking households, which are households where no one over age 14 speaks English well. - + The percent of limited speaking households, which are households where no one over age 14 speaks English well. +
                                                                          • @@ -1646,7 +1696,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          @@ -1657,7 +1707,9 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Unemployment
                                                                          - Number of unemployed people as a percentage of the civilian labor force + + Number of unemployed people as a percentage of the civilian labor force +
                                                                          • @@ -1682,7 +1734,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          @@ -1693,7 +1745,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Poverty
                                                                          - Percent of a tract's population in households where the household income is at or below 100% of the federal poverty level. + + Percent of a tract's population in households where the household income is at or below 100% of + the federal poverty level. +
                                                                          • @@ -1718,7 +1773,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          @@ -1729,8 +1784,10 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis High school degree achievement rate
                                                                          - Percent of people ages 25 years or older in a census tract whose - education level is less than a high school diploma. + + Percent (not percentile) of people ages 25 years or older in a census tract whose + education level is less than a high school diploma. +
                                                                          • @@ -1755,7 +1812,7 @@ exports[`rendering of the DatasetContainer checks if various text fields are vis Used in: - Training and workforce development + Training and workforce development methodology
                                                                          diff --git a/client/src/styles/global.scss b/client/src/styles/global.scss index 26f9da82..210d40c3 100644 --- a/client/src/styles/global.scss +++ b/client/src/styles/global.scss @@ -293,6 +293,12 @@ This section will outline styles that are component specific } } +// This is to allow the first child in the accordions which is the header showing INDICATOR and +// PERCENTILE (0-100) to push into the container's margin to match mock +.usa-accordion__content > *:first-child { + margin: -10px -20px 10px -20px; +} + /* *************************************** * TIMELINE / PROCESS LIST STYLES diff --git a/docs/decisions/0002-mapping-visualization-library.md b/docs/decisions/0002-mapping-visualization-library.md index 47e87a6e..14c010b4 100644 --- a/docs/decisions/0002-mapping-visualization-library.md +++ b/docs/decisions/0002-mapping-visualization-library.md @@ -108,6 +108,7 @@ We provide more detail on these factors below. - **Popularity** : According to [NPM Trends](https://www.npmtrends.com/mapbox-gl-vs-leaflet-vs-ol-vs-arcgis-js-api-vs-maplibre-gl) Mapbox-GL is the most downloaded package among those considered. More info [here](https://www.geoapify.com/map-libraries-comparison-leaflet-vs-mapbox-gl-vs-openlayers-trends-and-statistics) ![Download Stats](./0002-files/MapDownloadCount.png) + #### Mapbox-GL JS Cons - **Licensing** : Mapbox's December 2020 [announcement](https://github.com/mapbox/mapbox-gl-js/releases/tag/v2.0.0) of version 2.0 of their software changed ther license to proprietary and changed their pricing scheme to cover tiles loaded from outside of their service. This decision was met with [some criticism](https://joemorrison.medium.com/death-of-an-open-source-business-model-62bc227a7e9b) in the open-source mapping community. diff --git a/docs/decisions/0004-client-side-framework.md b/docs/decisions/0004-client-side-framework.md index 0c2f9dd5..1491e851 100644 --- a/docs/decisions/0004-client-side-framework.md +++ b/docs/decisions/0004-client-side-framework.md @@ -49,11 +49,11 @@ Chosen option: Gatsby, because it seems to hit the balance between being simple - Fairly good [documentation](https://www.gatsbyjs.com/docs) ### Negative Consequences - + - Test development environment ran out of memory. We debugged this further and could not replicate the problem on a new machine, but the original experience was annoying to work around and this [article](https://support.gatsbyjs.com/hc/en-us/articles/360053096273-Why-did-I-hit-Out-of-Memory-errors-) suggests it could be a more widespread problem. - Local builds and refreshes feel slow compared to other frameworks on this list - Seems a little more geared toward the blog usecase - + ## Pros and Cons of the Options ### Jekyll @@ -75,7 +75,7 @@ Cons: Pros: -- By far the most commonly framework in this list - 1.4MM [total downloads](https://www.npmtrends.com/gatsby-vs-next-vs-nuxt-vs-vuepress-vs-create-react-app-vs-gridsome) as of May 2, 2021 . Gatsby, the second most-downloaded, has ~470,000 +- By far the most commonly framework in this list - 1.4MM total downloads as of May 2, 2021 . Gatsby, the second most-downloaded, has ~470,000 - Used by a number of well-known [companies and brands](https://nextjs.org/showcase) - Flexible - Mature tooling like `create-next-app`