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Backend release branch to main (#1822)
* Create deploy_be_staging.yml (#1575) * Imputing income using geographic neighbors (#1559) Imputes income field with a light refactor. Needs more refactor and more tests (I spotchecked). Next ticket will check and address but a lot of "narwhal" architecture is here. * Adding HOLC indicator (#1579) Added HOLC indicator (Historic Redlining Score) from NCRC work; included 3.25 cutoff and low income as part of the housing burden category. * Update backend for Puerto Rico (#1686) * Update PR threshold count to 10 We now show 10 indicators for PR. See the discussion on the github issue for more info: https://github.com/usds/justice40-tool/issues/1621 * Do not use linguistic iso for Puerto Rico Closes 1350. Co-authored-by: Shelby Switzer <shelbyswitzer@gmail.com> * updating * Do not drop Guam and USVI from ETL (#1681) * Remove code that drops Guam and USVI from ETL * Add back code for dropping rows by FIPS code We may want this functionality, so let's keep it and just make the constant currently be an empty array. Co-authored-by: Shelby Switzer <shelbyswitzer@gmail.com> * Emma nechamkin/holc patch (#1742) Removing HOLC calculation from score narwhal. * updating ejscreen data, try two (#1747) * Rescaling linguistic isolation (#1750) Rescales linguistic isolation to drop puerto rico * adds UST indicator (#1786) adds leaky underground storage tanks * Changing LHE in tiles to a boolean (#1767) also includes merging / clean up of the release * added indoor plumbing to chas * added indoor plumbing to score housing burden * added indoor plumbing to score housing burden * first run through * Refactor DOE Energy Burden and COI to use YAML (#1796) * added tribalId for Supplemental dataset (#1804) * Setting zoom levels for tribal map (#1810) * NRI dataset and initial score YAML configuration (#1534) * update be staging gha * NRI dataset and initial score YAML configuration * checkpoint * adding data checks for release branch * passing tests * adding INPUT_EXTRACTED_FILE_NAME to base class * lint * columns to keep and tests * update be staging gha * checkpoint * update be staging gha * NRI dataset and initial score YAML configuration * checkpoint * adding data checks for release branch * passing tests * adding INPUT_EXTRACTED_FILE_NAME to base class * lint * columns to keep and tests * checkpoint * PR Review * renoving source url * tests * stop execution of ETL if there's a YAML schema issue * update be staging gha * adding source url as class var again * clean up * force cache bust * gha cache bust * dynamically set score vars from YAML * docsctrings * removing last updated year - optional reverse percentile * passing tests * sort order * column ordening * PR review * class level vars * Updating DatasetsConfig * fix pylint errors * moving metadata hint back to code Co-authored-by: lucasmbrown-usds <lucas.m.brown@omb.eop.gov> * Correct copy typo (#1809) * Add basic test suite for COI (#1518) * Update COI to use new yaml (#1518) * Add tests for DOE energy budren (1518 * Add dataset config for energy budren (1518) * Refactor ETL to use datasets.yml (#1518) * Add fake GEOIDs to COI tests (#1518) * Refactor _setup_etl_instance_and_run_extract to base (#1518) For the three classes we've done so far, a generic _setup_etl_instance_and_run_extract will work fine, for the moment we can reuse the same setup method until we decide future classes need more flexibility --- but they can also always subclass so... * Add output-path tests (#1518) * Update YAML to match constant (#1518) * Don't blindly set float format (#1518) * Add defaults for extract (#1518) * Run YAML load on all subclasses (#1518) * Update description fields (#1518) * Update YAML per final format (#1518) * Update fixture tract IDs (#1518) * Update base class refactor (#1518) Now that NRI is final I needed to make a small number of updates to my refactored code. * Remove old comment (#1518) * Fix type signature and return (#1518) * Update per code review (#1518) Co-authored-by: Jorge Escobar <83969469+esfoobar-usds@users.noreply.github.com> Co-authored-by: lucasmbrown-usds <lucas.m.brown@omb.eop.gov> Co-authored-by: Vim <86254807+vim-usds@users.noreply.github.com> * Update etl_score_geo.py Yikes! Fixing merge messup! * Create deploy_be_staging.yml (#1575) * Imputing income using geographic neighbors (#1559) Imputes income field with a light refactor. Needs more refactor and more tests (I spotchecked). Next ticket will check and address but a lot of "narwhal" architecture is here. * Adding HOLC indicator (#1579) Added HOLC indicator (Historic Redlining Score) from NCRC work; included 3.25 cutoff and low income as part of the housing burden category. * Update backend for Puerto Rico (#1686) * Update PR threshold count to 10 We now show 10 indicators for PR. See the discussion on the github issue for more info: https://github.com/usds/justice40-tool/issues/1621 * Do not use linguistic iso for Puerto Rico Closes 1350. Co-authored-by: Shelby Switzer <shelbyswitzer@gmail.com> * updating * Do not drop Guam and USVI from ETL (#1681) * Remove code that drops Guam and USVI from ETL * Add back code for dropping rows by FIPS code We may want this functionality, so let's keep it and just make the constant currently be an empty array. Co-authored-by: Shelby Switzer <shelbyswitzer@gmail.com> * Emma nechamkin/holc patch (#1742) Removing HOLC calculation from score narwhal. * updating ejscreen data, try two (#1747) * Rescaling linguistic isolation (#1750) Rescales linguistic isolation to drop puerto rico * adds UST indicator (#1786) adds leaky underground storage tanks * Changing LHE in tiles to a boolean (#1767) also includes merging / clean up of the release * added indoor plumbing to chas * added indoor plumbing to score housing burden * added indoor plumbing to score housing burden * first run through * Refactor DOE Energy Burden and COI to use YAML (#1796) * added tribalId for Supplemental dataset (#1804) * Setting zoom levels for tribal map (#1810) * NRI dataset and initial score YAML configuration (#1534) * update be staging gha * NRI dataset and initial score YAML configuration * checkpoint * adding data checks for release branch * passing tests * adding INPUT_EXTRACTED_FILE_NAME to base class * lint * columns to keep and tests * update be staging gha * checkpoint * update be staging gha * NRI dataset and initial score YAML configuration * checkpoint * adding data checks for release branch * passing tests * adding INPUT_EXTRACTED_FILE_NAME to base class * lint * columns to keep and tests * checkpoint * PR Review * renoving source url * tests * stop execution of ETL if there's a YAML schema issue * update be staging gha * adding source url as class var again * clean up * force cache bust * gha cache bust * dynamically set score vars from YAML * docsctrings * removing last updated year - optional reverse percentile * passing tests * sort order * column ordening * PR review * class level vars * Updating DatasetsConfig * fix pylint errors * moving metadata hint back to code Co-authored-by: lucasmbrown-usds <lucas.m.brown@omb.eop.gov> * Correct copy typo (#1809) * Add basic test suite for COI (#1518) * Update COI to use new yaml (#1518) * Add tests for DOE energy budren (1518 * Add dataset config for energy budren (1518) * Refactor ETL to use datasets.yml (#1518) * Add fake GEOIDs to COI tests (#1518) * Refactor _setup_etl_instance_and_run_extract to base (#1518) For the three classes we've done so far, a generic _setup_etl_instance_and_run_extract will work fine, for the moment we can reuse the same setup method until we decide future classes need more flexibility --- but they can also always subclass so... * Add output-path tests (#1518) * Update YAML to match constant (#1518) * Don't blindly set float format (#1518) * Add defaults for extract (#1518) * Run YAML load on all subclasses (#1518) * Update description fields (#1518) * Update YAML per final format (#1518) * Update fixture tract IDs (#1518) * Update base class refactor (#1518) Now that NRI is final I needed to make a small number of updates to my refactored code. * Remove old comment (#1518) * Fix type signature and return (#1518) * Update per code review (#1518) Co-authored-by: Jorge Escobar <83969469+esfoobar-usds@users.noreply.github.com> Co-authored-by: lucasmbrown-usds <lucas.m.brown@omb.eop.gov> Co-authored-by: Vim <86254807+vim-usds@users.noreply.github.com> * Update etl_score_geo.py Yikes! Fixing merge messup! * updated to fix linting errors (#1818) Cleans and updates base branch * Adding back MapComparison video * Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871) * Disable markdown check for link * Adding DOT composite to travel score (#1820) This adds the DOT dataset to the ETL and to the score. Note that currently we take a percentile of an average of percentiles. * Adding first street foundation data (#1823) Adding FSF flood and wildfire risk datasets to the score. * first run -- adding NCLD data to the ETL, but not yet to the score * Add abandoned mine lands data (#1824) * Add notebook to generate test data (#1780) * Add Abandoned Mine Land data (#1780) Using a similar structure but simpler apporach compared to FUDs, add an indicator for whether a tract has an abandonded mine. * Adding some detail to dataset readmes Just a thought! * Apply feedback from revieiw (#1780) * Fixup bad string that broke test (#1780) * Update a string that I should have renamed (#1780) * Reduce number of threads to reduce memory pressure (#1780) * Try not running geo data (#1780) * Run the high-memory sets separately (#1780) * Actually deduplicate (#1780) * Add flag for memory intensive ETLs (#1780) * Document new flag for datasets (#1780) * Add flag for new datasets fro rebase (#1780) Co-authored-by: Emma Nechamkin <97977170+emma-nechamkin@users.noreply.github.com> * Adding NLCD data (#1826) Adding NLCD's natural space indicator end to end to the score. * Add donut hole calculation to score (#1828) Adds adjacency index to the pipeline. Requires thorough QA * Adding eamlis and fuds data to legacy pollution in score (#1832) Update to add EAMLIS and FUDS data to score * Update to use new FSF files (#1838) backend is partially done! * Quick fix to kitchen or plumbing indicator Yikes! I think I messed something up and dropped the pctile field suffix from when the KP score gets calculated. Fixing right quick. * Fast flag update (#1844) Added additional flags for the front end based on our conversation in stand up this morning. * Tiles fix (#1845) Fixes score-geo and adds flags * Update etl_score_geo.py * Issue 1827: Add demographics to tiles and download files (#1833) * Adding demographics for use in sidebar and download files * Updates backend constants to N (#1854) * updated to show T/F/null vs T/F for AML and FUDS (#1866) * fix markdown * just testing that the boolean is preserved on gha * checking drop tracts works * OOPS! Old changes persisted * adding a check to the agvalue calculation for nri * updated with error messages * updated error message * tuple type * Score tests (#1847) * update Python version on README; tuple typing fix * Alaska tribal points fix (#1821) * Bump mistune from 0.8.4 to 2.0.3 in /data/data-pipeline (#1777) Bumps [mistune](https://github.com/lepture/mistune) from 0.8.4 to 2.0.3. - [Release notes](https://github.com/lepture/mistune/releases) - [Changelog](https://github.com/lepture/mistune/blob/master/docs/changes.rst) - [Commits](https://github.com/lepture/mistune/compare/v0.8.4...v2.0.3) --- updated-dependencies: - dependency-name: mistune dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * poetry update * initial pass of score tests * add threshold tests * added ses threshold (not donut, not island) * testing suite -- stopping for the day * added test for lead proxy indicator * Refactor score tests to make them less verbose and more direct (#1865) * Cleanup tests slightly before refactor (#1846) * Refactor score calculations tests * Feedback from review * Refactor output tests like calculatoin tests (#1846) (#1870) * Reorganize files (#1846) * Switch from lru_cache to fixture scorpes (#1846) * Add tests for all factors (#1846) * Mark smoketests and run as part of be deply (#1846) * Update renamed var (#1846) * Switch from named tuple to dataclass (#1846) This is annoying, but pylint in python3.8 was crashing parsing the named tuple. We weren't using any namedtuple-specific features, so I made the type a dataclass just to get pylint to behave. * Add default timout to requests (#1846) * Fix type (#1846) * Fix merge mistake on poetry.lock (#1846) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: Jorge Escobar <jorge.e.escobar@omb.eop.gov> Co-authored-by: Jorge Escobar <83969469+esfoobar-usds@users.noreply.github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matt Bowen <83967628+mattbowen-usds@users.noreply.github.com> Co-authored-by: matt bowen <matthew.r.bowen@omb.eop.gov> * just testing that the boolean is preserved on gha (#1867) * updated with hopefully a fix; coercing aml, fuds, hrs to booleans for the raw value to preserve null character. * Adding tests to ensure proper calculations (#1871) * just testing that the boolean is preserved on gha * checking drop tracts works * adding a check to the agvalue calculation for nri * updated with error messages * tribal tiles fix (#1874) * Alaska tribal points fix (#1821) * tribal tiles fix * disabling child opportunity * lint * removing COI * removing commented out code * Pipeline tile tests (#1864) * temp update * updating with fips check * adding check on pfs * updating with pfs test * Update test_tiles_smoketests.py * Fix lint errors (#1848) * Add column names test (#1848) * Mark tests as smoketests (#1848) * Move to other score-related tests (#1848) * Recast Total threshold criteria exceeded to int (#1848) In writing tests to verify the output of the tiles csv matches the final score CSV, I noticed TC/Total threshold criteria exceeded was getting cast from an int64 to a float64 in the process of PostScoreETL. I tracked it down to the line where we merge the score dataframe with constants.DATA_CENSUS_CSV_FILE_PATH --- there where > 100 tracts in the national census CSV that don't exist in the score, so those ended up with a Total threshhold count of np.nan, which is a float, and thereby cast those columns to float. For the moment I just cast it back. * No need for low memeory (#1848) * Add additional tests of tiles.csv (#1848) * Drop pre-2010 rows before computing score (#1848) Note this is probably NOT the optimal place for this change; it might make more sense for each source to filter its own tracts down to the acceptable tract list. However, that would be a pretty invasive change, where this is central and plenty of other things are happening in score transform that could be moved to sources, so for today, here's where the change will live. * Fix typo (#1848) * Switch from filter to inner join (#1848) * Remove no-op lines from tiles (#1848) * Apply feedback from review, linter (#1848) * Check the values oeverything in the frame (#1848) * Refactor checker class (#1848) * Add test for state names (#1848) * cleanup from reviewing my own code (#1848) * Fix lint error (#1858) * Apply Emma's feedback from review (#1848) * Remove refs to national_df (#1848) * Account for new, fake nullable bools in tiles (#1848) To handle a geojson limitation, Emma converted some nullable boolean colunms to float64 in the tiles export with the values {0.0, 1.0, nan}, giving us the same expressiveness. Sadly, this broke my assumption that all columns between the score and tiles csvs would have the same dtypes, so I need to account for these new, fake bools in my test. * Use equals instead of my worse version (#1848) * Missed a spot where we called _create_score_data (#1848) * Update per safety (#1848) Co-authored-by: matt bowen <matthew.r.bowen@omb.eop.gov> * Add tests to make sure each source makes it to the score correctly (#1878) * Remove unused persistent poverty from score (#1835) * Test a few datasets for overlap in the final score (#1835) * Add remaining data sources (#1853) * Apply code-review feedback (#1835) * Rearrange a little for readabililty (#1835) * Add tract test (#1835) * Add test for score values (#1835) * Check for unmatched source tracts (#1835) * Cleanup numeric code to plaintext (#1835) * Make import more obvious (#1835) * Updating traffic barriers to include low pop threshold (#1889) Changing the traffic barriers to only be included for places with recorded population * Remove no land tracts from map (#1894) remove from map * Issue 1831: missing life expectancy data from Maine and Wisconsin (#1887) * Fixing missing states and adding tests for states to all classes * Removing low pop tracts from FEMA population loss (#1898) dropping 0 population from FEMA * 1831 Follow up (#1902) This code causes no functional change to the code. It does two things: 1. Uses difference instead of - to improve code style for working with sets. 2. Removes the line EXPECTED_MISSING_STATES = ["02", "15"], which is now redundant because of the line I added (in a previous pull request) of ALASKA_AND_HAWAII_EXPECTED_IN_DATA = False. * Add tests for all non-census sources (#1899) * Refactor CDC life-expectancy (1554) * Update to new tract list (#1554) * Adjust for tests (#1848) * Add tests for cdc_places (#1848) * Add EJScreen tests (#1848) * Add tests for HUD housing (#1848) * Add tests for GeoCorr (#1848) * Add persistent poverty tests (#1848) * Update for sources without zips, for new validation (#1848) * Update tests for new multi-CSV but (#1848) Lucas updated the CDC life expectancy data to handle a bug where two states are missing from the US Overall download. Since virtually none of our other ETL classes download multiple CSVs directly like this, it required a pretty invasive new mocking strategy. * Add basic tests for nature deprived (#1848) * Add wildfire tests (#1848) * Add flood risk tests (#1848) * Add DOT travel tests (#1848) * Add historic redlining tests (#1848) * Add tests for ME and WI (#1848) * Update now that validation exists (#1848) * Adjust for validation (#1848) * Add health insurance back to cdc places (#1848) Ooops * Update tests with new field (#1848) * Test for blank tract removal (#1848) * Add tracts for clipping behavior * Test clipping and zfill behavior (#1848) * Fix bad test assumption (#1848) * Simplify class, add test for tract padding (#1848) * Fix percentage inversion, update tests (#1848) Looking through the transformations, I noticed that we were subtracting a percentage that is usually between 0-100 from 1 instead of 100, and so were endind up with some surprising results. Confirmed with lucasmbrown-usds * Add note about first street data (#1848) * Issue 1900: Tribal overlap with Census tracts (#1903) * working notebook * updating notebook * wip * fixing broken tests * adding tribal overlap files * WIP * WIP * WIP, calculated count and names * working * partial cleanup * partial cleanup * updating field names * fixing bug * removing pyogrio * removing unused imports * updating test fixtures to be more realistic * cleaning up notebook * fixing black * fixing flake8 errors * adding tox instructions * updating etl_score * suppressing warning * Use projected CRSes, ignore geom types (#1900) I looked into this a bit, and in general the geometry type mismatch changes very little about the calculation; we have a mix of multipolygons and polygons. The fastest thing to do is just not keep geom type; I did some runs with it set to both True and False, and they're the same within 9 digits of precision. Logically we just want to overlaps, regardless of how the actual geometries are encoded between the frames, so we can in this case ignore the geom types and feel OKAY. I also moved to projected CRSes, since we are actually trying to do area calculations and so like, we should. Again, the change is small in magnitude but logically more sound. * Readd CDC dataset config (#1900) * adding comments to fips code * delete unnecessary loggers Co-authored-by: matt bowen <matthew.r.bowen@omb.eop.gov> * Improve score test documentation based on Lucas's feedback (#1835) (#1914) * Better document base on Lucas's feedback (#1835) * Fix typo (#1835) * Add test to verify GEOJSON matches tiles (#1835) * Remove NOOP line (#1835) * Move GEOJSON generation up for new smoketest (#1835) * Fixup code format (#1835) * Update readme for new somketest (#1835) * Cleanup source tests (#1912) * Move test to base for broader coverage (#1848) * Remove duplicate line (#1848) * FUDS needed an extra mock (#1848) * Add tribal count notebook (#1917) (#1919) * Add tribal count notebook (#1917) * test without caching * added comment Co-authored-by: lucasmbrown-usds <lucas.m.brown@omb.eop.gov> * Add tribal overlap to downloads (#1907) * Add tribal data to downloads (#1904) * Update test pickle with current cols (#1904) * Remove text of tribe names from GeoJSON (#1904) * Update test data (#1904) * Add tribal overlap to smoketests (#1904) * Issue 1910: Do not impute income for 0 population tracts (#1918) * should be working, has unnecessary loggers * removing loggers and cleaning up * updating ejscreen tests * adding tests and responding to PR feedback * fixing broken smoke test * delete smoketest docs * updating click * updating click * Bump just jupyterlab (#1930) * Fixing link checker (#1929) * Update deps safety says are vulnerable (#1937) (#1938) Co-authored-by: matt bowen <matt@mattbowen.net> * Add demos for island areas (#1932) * Backfill population in island areas (#1882) * Update smoketest to account for backfills (#1882) As I wrote in the commend: We backfill island areas with data from the 2010 census, so if THOSE tracts have data beyond the data source, that's to be expected and is fine to pass. If some other state or territory does though, this should fail This ends up being a nice way of documenting that behavior i guess! * Fixup lint issues (#1882) * Add in race demos to 2010 census pull (#1851) * Add backfill data to score (#1851) * Change column name (#1851) * Fill demos after the score (#1851) * Add income back, adjust test (#1882) * Apply code-review feedback (#1851) * Add test for island area backfill (#1851) * Fix bad rename (#1851) * Reorder download fields, add plumbing back (#1942) * Add back lack of plumbing fields (#1920) * Reorder fields for excel (#1921) * Reorder excel fields (#1921) * Fix formating, lint errors, pickes (#1921) * Add missing plumbing col, fix order again (#1921) * Update that pickle (#1921) * refactoring tribal (#1960) * updated with scoring comparison * updated for narhwal -- leaving commented code in for now * pydantic upgrade * produce a string for the front end to ingest (#1963) * wip * i believe this works -- let's see the pipeline * updated fixtures * Adding ADJLI_ET (#1976) * updated tile data * ensuring adjli_et in * Add back income percentile (#1977) * Add missing field to download (#1964) * Remove pydantic since it's unused (#1964) * Add percentile to CSV (#1964) * Update downloadable pickle (#1964) * Issue 105: Configure and run `black` and other pre-commit hooks (clean branch) (#1962) * Configure and run `black` and other pre-commit hooks Co-authored-by: matt bowen <matthew.r.bowen@omb.eop.gov> * Removing fixed python version for black (#1985) * Fixup TA_COUNT and TA_PERC (#1991) * Change TA_PERC, change TA_COUNT (#1988, #1989) - Make TA_PERC_STR back into a nullable float following the rules requestsed in #1989 - Move TA_COUNT to be TA_COUNT_AK, also add a null TA_COUNT_C for CONUS that we can fill in later. * Fix typo comment (#1988) * Issue 1992: Do not impute income for null population tracts (#1993) * Hotfix for DOT data source DNS issue (#1999) * Make tribal overlap set score N (#2004) * Add "Is a Tribal DAC" field (#1998) * Add tribal DACs to score N final (#1998) * Add new fields to downloads (#1998) * Make a int a float (#1998) * Update field names, apply feedback (#1998) * Add assertions around codebook (#2014) * Add assertion around codebook (#1505) * Assert csv and excel have same cols (#1505) * Remove suffixes from tribal lands (#1974) (#2008) * Data source location (#2015) * data source location * toml * cdc_places * cdc_svi_index * url updates * child oppy and dot travel * up to hud_recap * completed ticket * cache bust * hud_recap * us_army_fuds * Remove vars the frontend doesn't use (#2020) (#2022) I did a pretty rough and simple analysis of the variables we put in the tiles and grepped the frontend code to see if (1) they're ever accessed and (2) if they're used, even if they're read once. I removed everything I noticed was not accessed. * Disable file size limits on tiles (#2031) * Disable file size limits on tiles * Remove print debugs I know. * Update file name pattern (#2037) (#2038) * Update file name pattern (#2037) * Remove ETL from generation (2037) I looked more carefully, and this ETL step isn't used in the score, so there's no need to run it every time. Per previous steps, I removed it from constants so the code is there it won't run by default. * Round ALL the float fields for the tiles (#2040) * Round ALL the float fields for the tiles (#2033) * Floor in a simpler way (#2033) Emma pointed out that all teh stuff we're doing in floor_series is probably unnecessary for this case, so just use the built-in floor. * Update pickle I missed (#2033) * Clean commit of just aggregate burden notebook (#1819) added a burden notebook * Update the dockerfile (#2045) * Update so the image builds (#2026) * Fix bad dict (2026) * Rename census tract field in downloads (#2068) * Change tract ID field name (2060) * Update lockfile (#2061) * Bump safety, jupyter, wheel (#2061) * DOn't depend directly on wheel (2061) * Bring narwhal reqs in line with main * Update tribal area counts (#2071) * Rename tribal area field (2062) * Add missing file (#2062) * Add checks to create version (#2047) (#2052) * Fix failing safety (#2114) * Ignore vuln that doesn't affect us 2113 https://nvd.nist.gov/vuln/detail/CVE-2022-42969 landed recently and there's no fix in py (which is maintenance mode). From my analysis, that CVE cannot hurt us (famous last words), so we'll ignore the vuln for now. * 2113 Update our gdal ppa * that didn't work (2113) * Don't add the PPA, the package exists (#2113) * Fix type (#2113) * Force an update of wheel 2113 * Also remove PPA line from create-score-versions * Drop 3.8 because of wheel 2113 * Put back 3.8, use newer actions * Try another way of upgrading wheel 2113 * Upgrade wheel in tox too 2113 * Typo fix 2113 Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: Emma Nechamkin <97977170+emma-nechamkin@users.noreply.github.com> Co-authored-by: Shelby Switzer <shelby.c.switzer@omb.eop.gov> Co-authored-by: Shelby Switzer <shelbyswitzer@gmail.com> Co-authored-by: Emma Nechamkin <Emma.J.Nechamkin@omb.eop.gov> Co-authored-by: Matt Bowen <83967628+mattbowen-usds@users.noreply.github.com> Co-authored-by: Jorge Escobar <83969469+esfoobar-usds@users.noreply.github.com> Co-authored-by: lucasmbrown-usds <lucas.m.brown@omb.eop.gov> Co-authored-by: Jorge Escobar <jorge.e.escobar@omb.eop.gov> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: matt bowen <matthew.r.bowen@omb.eop.gov> Co-authored-by: matt bowen <matt@mattbowen.net>
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# How to add variables to a score
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So, there's a variable you want to add to the score! Once you have the data source created in `etl/sources`, what should you do? There are 6 steps across a minimum of 7 files.
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__Updating `field_names.py`__
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Per indicator, you need to make (usually) three variables to get used in other files.
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- raw variable: this is the name of the variable's raw data, not scaled into a percentile
|
||||
- variable with threshold exceeded: this is a boolean for whether the tract meets the threshold for the indicator alone
|
||||
- variable with threshold exceeded and socioeconomic criterion exceeded: this is whether the tract will be a DAC based on the socioeconomic criterion and the indicator
|
||||
|
||||
__Updating `etl_score.py`__
|
||||
- add the dataframe from the source to the ScoreETL constructor and add a line to read the dataframe into memory
|
||||
- then, add the dataframe into the list of `census_tract_dfs`
|
||||
- finally, add columns you want to include as percentiles to the `numeric_columns` list
|
||||
|
||||
__Updating `score_narwhal.py`__ (or whatever the score file is)
|
||||
- per factor, add the columns that show the threshold and socioeconomic criterion is exceeded to the `eligibility_columns` list
|
||||
- construct all columns specified in `field_names`, using the factor method as a guide
|
||||
|
||||
__Updating `constants.py`__
|
||||
- add the columns' shortnames to the tiles dictionary (using Vim's UI sheet to guide short names)
|
||||
- add the floats to the list of floats
|
||||
|
||||
__Updating `csv.yml` and `excel.yml`__
|
||||
- make sure each column you want to be in the downloadable files is listed here
|
||||
|
||||
__Update the fixtures__
|
||||
Follow the instructions on the repo to modify tiles so that `test_etl_post.py` doesn't fail. Then, confirm results.
|
||||
|
|
@ -1,60 +1,29 @@
|
|||
# Suffixes
|
||||
PERCENTILE_FIELD_SUFFIX = " (percentile)"
|
||||
PERCENTILE_URBAN_RURAL_FIELD_SUFFIX = " (percentile urban/rural)"
|
||||
MIN_MAX_FIELD_SUFFIX = " (min-max normalized)"
|
||||
TOP_25_PERCENTILE_SUFFIX = " (top 25th percentile)"
|
||||
ISLAND_AREAS_PERCENTILE_ADJUSTMENT_FIELD = " for island areas"
|
||||
ADJACENT_MEAN_SUFFIX = " (based on adjacency index and low income alone)"
|
||||
ADJACENCY_INDEX_SUFFIX = " (average of neighbors)"
|
||||
ISLAND_AREA_BACKFILL_SUFFIX = " in 2009"
|
||||
|
||||
# Geographic field names
|
||||
GEOID_TRACT_FIELD = "GEOID10_TRACT"
|
||||
STATE_FIELD = "State/Territory"
|
||||
COUNTY_FIELD = "County Name"
|
||||
|
||||
# Score file field names
|
||||
SCORE_A = "Score A"
|
||||
SCORE_B = "Score B"
|
||||
SCORE_C = "Score C"
|
||||
C_SOCIOECONOMIC = "Socioeconomic Factors"
|
||||
C_SENSITIVE = "Sensitive populations"
|
||||
C_ENVIRONMENTAL = "Environmental effects"
|
||||
C_EXPOSURES = "Exposures"
|
||||
SCORE_D = "Score D"
|
||||
SCORE_E = "Score E"
|
||||
SCORE_F_COMMUNITIES = "Score F (communities)"
|
||||
SCORE_G = "Score G"
|
||||
SCORE_G_COMMUNITIES = "Score G (communities)"
|
||||
SCORE_H = "Score H"
|
||||
SCORE_H_COMMUNITIES = "Score H (communities)"
|
||||
SCORE_I = "Score I"
|
||||
SCORE_I_COMMUNITIES = "Score I (communities)"
|
||||
SCORE_K = "NMTC (communities)"
|
||||
SCORE_K_COMMUNITIES = "Score K (communities)"
|
||||
|
||||
# Definition L fields
|
||||
SCORE_L = "Definition L"
|
||||
SCORE_L_COMMUNITIES = "Definition L (communities)"
|
||||
L_CLIMATE = "Climate Factor (Definition L)"
|
||||
L_ENERGY = "Energy Factor (Definition L)"
|
||||
L_TRANSPORTATION = "Transportation Factor (Definition L)"
|
||||
L_HOUSING = "Housing Factor (Definition L)"
|
||||
L_POLLUTION = "Pollution Factor (Definition L)"
|
||||
L_WATER = "Water Factor (Definition L)"
|
||||
L_HEALTH = "Health Factor (Definition L)"
|
||||
L_WORKFORCE = "Workforce Factor (Definition L)"
|
||||
L_NON_WORKFORCE = "Any Non-Workforce Factor (Definition L)"
|
||||
|
||||
# Definition M fields
|
||||
SCORE_M = "Definition M"
|
||||
SCORE_M_COMMUNITIES = "Definition M (communities)"
|
||||
M_CLIMATE = "Climate Factor (Definition M)"
|
||||
M_ENERGY = "Energy Factor (Definition M)"
|
||||
M_TRANSPORTATION = "Transportation Factor (Definition M)"
|
||||
M_HOUSING = "Housing Factor (Definition M)"
|
||||
M_POLLUTION = "Pollution Factor (Definition M)"
|
||||
M_WATER = "Water Factor (Definition M)"
|
||||
M_HEALTH = "Health Factor (Definition M)"
|
||||
M_WORKFORCE = "Workforce Factor (Definition M)"
|
||||
M_NON_WORKFORCE = "Any Non-Workforce Factor (Definition M)"
|
||||
# Definition Narwhal fields
|
||||
SCORE_N_COMMUNITIES = "Definition N (communities)"
|
||||
N_CLIMATE = "Climate Factor (Definition N)"
|
||||
N_ENERGY = "Energy Factor (Definition N)"
|
||||
N_TRANSPORTATION = "Transportation Factor (Definition N)"
|
||||
N_HOUSING = "Housing Factor (Definition N)"
|
||||
N_POLLUTION = "Pollution Factor (Definition N)"
|
||||
N_WATER = "Water Factor (Definition N)"
|
||||
N_HEALTH = "Health Factor (Definition N)"
|
||||
N_WORKFORCE = "Workforce Factor (Definition N)"
|
||||
N_NON_WORKFORCE = "Any Non-Workforce Factor (Definition N)"
|
||||
FINAL_SCORE_N_BOOLEAN = (
|
||||
"Definition N community, including adjacency index tracts"
|
||||
)
|
||||
|
||||
PERCENTILE = 90
|
||||
MEDIAN_HOUSE_VALUE_PERCENTILE = 90
|
||||
|
|
@ -72,30 +41,24 @@ WORKFORCE_SOCIO_INDICATORS_EXCEEDED = (
|
|||
"Both workforce socioeconomic indicators exceeded"
|
||||
)
|
||||
|
||||
# For now, these are not used. Will delete after following up with Vim.
|
||||
POLLUTION_SOCIO_INDICATORS_EXCEEDED = (
|
||||
"Both pollution socioeconomic indicators exceeded"
|
||||
)
|
||||
CLIMATE_SOCIO_INDICATORS_EXCEEDED = (
|
||||
"Both climate socioeconomic indicators exceeded"
|
||||
)
|
||||
ENERGY_SOCIO_INDICATORS_EXCEEDED = (
|
||||
"Both energy socioeconomic indicators exceeded"
|
||||
)
|
||||
HOUSING_SOCIO_INDICATORS_EXCEEDED = (
|
||||
"Both housing socioeconomic indicators exceeded"
|
||||
)
|
||||
WATER_SOCIO_INDICATORS_EXCEEDED = "Both water socioeconomic indicators exceeded"
|
||||
|
||||
HEALTH_SOCIO_INDICATORS_EXCEEDED = (
|
||||
"Both health socioeconomic indicators exceeded"
|
||||
)
|
||||
|
||||
# Poverty / Income
|
||||
POVERTY_FIELD = "Poverty (Less than 200% of federal poverty line)"
|
||||
|
||||
# this is the raw, unadjusted variable
|
||||
POVERTY_LESS_THAN_200_FPL_FIELD = (
|
||||
"Percent of individuals below 200% Federal Poverty Line"
|
||||
)
|
||||
|
||||
# this is for use in the donuts
|
||||
ADJUSTED_POVERTY_LESS_THAN_200_PERCENT_FPL_FIELD_NAME = (
|
||||
"Adjusted percent of individuals < 200% Federal Poverty Line"
|
||||
)
|
||||
|
||||
# this is what gets used in the score
|
||||
POVERTY_LESS_THAN_200_FPL_IMPUTED_FIELD = "Percent of individuals below 200% Federal Poverty Line, imputed and adjusted"
|
||||
IMPUTED_INCOME_FLAG_FIELD_NAME = (
|
||||
"Income data has been estimated based on neighbor income"
|
||||
)
|
||||
POVERTY_LESS_THAN_150_FPL_FIELD = (
|
||||
"Percent of individuals < 150% Federal Poverty Line"
|
||||
)
|
||||
|
|
@ -122,6 +85,27 @@ LOW_MEDIAN_INCOME_AS_PERCENT_OF_AMI_FIELD = (
|
|||
"Low median household income as a percent of area median income"
|
||||
)
|
||||
|
||||
# Additional ACS demographic fields.
|
||||
PERCENT_PREFIX = "Percent "
|
||||
|
||||
PERCENT_BLACK_FIELD_NAME = PERCENT_PREFIX + "Black or African American"
|
||||
PERCENT_AMERICAN_INDIAN_FIELD_NAME = (
|
||||
PERCENT_PREFIX + "American Indian / Alaska Native"
|
||||
)
|
||||
PERCENT_ASIAN_FIELD_NAME = PERCENT_PREFIX + "Asian"
|
||||
PERCENT_HAWAIIAN_FIELD_NAME = PERCENT_PREFIX + "Native Hawaiian or Pacific"
|
||||
PERCENT_TWO_OR_MORE_RACES_FIELD_NAME = PERCENT_PREFIX + "two or more races"
|
||||
PERCENT_NON_HISPANIC_WHITE_FIELD_NAME = PERCENT_PREFIX + "White"
|
||||
PERCENT_HISPANIC_FIELD_NAME = PERCENT_PREFIX + "Hispanic or Latino"
|
||||
# Note that `other` is lowercase because the whole field will show up in the download
|
||||
# file as "Percent other races"
|
||||
PERCENT_OTHER_RACE_FIELD_NAME = PERCENT_PREFIX + "other races"
|
||||
|
||||
# Age
|
||||
PERCENT_AGE_UNDER_10 = "Percent age under 10"
|
||||
PERCENT_AGE_10_TO_64 = "Percent age 10 to 64"
|
||||
PERCENT_AGE_OVER_64 = "Percent age over 64"
|
||||
|
||||
# Climate
|
||||
FEMA_RISK_FIELD = "FEMA Risk Index Expected Annual Loss Score"
|
||||
EXPECTED_BUILDING_LOSS_RATE_FIELD = (
|
||||
|
|
@ -133,6 +117,8 @@ EXPECTED_AGRICULTURE_LOSS_RATE_FIELD = (
|
|||
EXPECTED_POPULATION_LOSS_RATE_FIELD = (
|
||||
"Expected population loss rate (Natural Hazards Risk Index)"
|
||||
)
|
||||
FUTURE_FLOOD_RISK_FIELD = "Share of properties at risk of flood in 30 years"
|
||||
FUTURE_WILDFIRE_RISK_FIELD = "Share of properties at risk of fire in 30 years"
|
||||
|
||||
# Environment
|
||||
DIESEL_FIELD = "Diesel particulate matter exposure"
|
||||
|
|
@ -147,11 +133,16 @@ TSDF_FIELD = "Proximity to hazardous waste sites"
|
|||
NPL_FIELD = "Proximity to NPL sites"
|
||||
AIR_TOXICS_CANCER_RISK_FIELD = "Air toxics cancer risk"
|
||||
RESPIRATORY_HAZARD_FIELD = "Respiratory hazard index"
|
||||
UST_FIELD = "Leaky underground storage tanks"
|
||||
|
||||
LOW_INCOME_THRESHOLD = "Exceeds FPL200 threshold"
|
||||
|
||||
# Housing
|
||||
HOUSING_BURDEN_FIELD = "Housing burden (percent)"
|
||||
NO_KITCHEN_OR_INDOOR_PLUMBING_FIELD = (
|
||||
"Share of homes with no kitchen or indoor plumbing (percent)"
|
||||
)
|
||||
|
||||
HT_INDEX_FIELD = (
|
||||
"Housing + Transportation Costs % Income for the Regional Typical Household"
|
||||
)
|
||||
|
|
@ -280,7 +271,17 @@ EJSCREEN_AREAS_OF_CONCERN_STATE_95TH_PERCENTILE_COMMUNITIES_FIELD = (
|
|||
"EJSCREEN Areas of Concern, State, 95th percentile (communities)"
|
||||
)
|
||||
# Mapping inequality data.
|
||||
REDLINED_SHARE: str = (
|
||||
"Redlined share: tract had redlining and was more than 50% Grade C or D"
|
||||
)
|
||||
HOLC_GRADE_D_TRACT_PERCENT_FIELD: str = "Percent of tract that is HOLC Grade D"
|
||||
HOLC_GRADE_C_TRACT_PERCENT_FIELD: str = "Percent of tract that is HOLC Grade C"
|
||||
HOLC_GRADE_C_OR_D_TRACT_PERCENT_FIELD: str = (
|
||||
"Percent of tract that is HOLC Grade C or HOLC Grade D"
|
||||
)
|
||||
HOLC_GRADE_C_OR_D_TRACT_50_PERCENT_FIELD: str = (
|
||||
"Tract is more than 50% Grade C or D"
|
||||
)
|
||||
HOLC_GRADE_D_TRACT_20_PERCENT_FIELD: str = "Tract is >20% HOLC Grade D"
|
||||
HOLC_GRADE_D_TRACT_50_PERCENT_FIELD: str = "Tract is >50% HOLC Grade D"
|
||||
HOLC_GRADE_D_TRACT_75_PERCENT_FIELD: str = "Tract is >75% HOLC Grade D"
|
||||
|
|
@ -293,7 +294,7 @@ MICHIGAN_EJSCREEN_PRIORITY_COMMUNITY_FIELD: str = (
|
|||
)
|
||||
|
||||
# CDC SVI INDEX percentile fields
|
||||
CDC_SVI_INDEX_SE_THEME_FIELD: str = "SVI - Socioeconomic Index"
|
||||
CDC_SVI_INDEX_SE_THEME_FIELD: str = "SVI - Social Vulnerability Index"
|
||||
CDC_SVI_INDEX_HOUSEHOLD_THEME_COMPOSITION_FIELD: str = (
|
||||
"SVI - Household Composition Index"
|
||||
)
|
||||
|
|
@ -306,27 +307,15 @@ CDC_SVI_INDEX_RPL_THEMES_OVERALL_FIELD: str = (
|
|||
)
|
||||
CDC_SVI_INDEX_THEMES_PRIORITY_COMMUNITY: str = "At or above 90 for overall percentile ranking according to Social Vulnerability Indices"
|
||||
|
||||
# DOT Travel Burden Data
|
||||
DOT_TRAVEL_BURDEN_FIELD: str = "DOT Travel Barriers Score"
|
||||
|
||||
# Maryland EJSCREEN Data.
|
||||
MARYLAND_EJSCREEN_SCORE_FIELD: str = "Maryland Environmental Justice Score"
|
||||
|
||||
MARYLAND_EJSCREEN_BURDENED_THRESHOLD_FIELD: str = (
|
||||
"Maryland EJSCREEN Priority Community"
|
||||
)
|
||||
# Child Opportunity Index data
|
||||
# Summer days with maximum temperature above 90F.
|
||||
EXTREME_HEAT_FIELD = "Summer days above 90F"
|
||||
|
||||
# Percentage households without a car located further than a half-mile from the
|
||||
# nearest supermarket.
|
||||
HEALTHY_FOOD_FIELD = "Percent low access to healthy food"
|
||||
|
||||
# Percentage impenetrable surface areas such as rooftops, roads or parking lots.
|
||||
IMPENETRABLE_SURFACES_FIELD = "Percent impenetrable surface areas"
|
||||
|
||||
# Percentage third graders scoring proficient on standardized reading tests,
|
||||
# converted to NAEP scale score points.
|
||||
READING_FIELD = "Third grade reading proficiency"
|
||||
LOW_READING_FIELD = "Low third grade reading proficiency"
|
||||
|
||||
# Alternative energy-related definition of DACs
|
||||
ENERGY_RELATED_COMMUNITIES_DEFINITION_ALTERNATIVE = (
|
||||
|
|
@ -349,6 +338,36 @@ MOBILE_HOME = "Mobile Home"
|
|||
SINGLE_PARENT = "Single Parent"
|
||||
TRANSPORTATION_COSTS = "Transportation Costs"
|
||||
|
||||
# eAMLIS and FUDS variables
|
||||
AML_BOOLEAN = "Is there at least one abandoned mine in this census tract?"
|
||||
AML_BOOLEAN_FILLED_IN = "Is there at least one abandoned mine in this census tract, where missing data is treated as False?"
|
||||
|
||||
ELIGIBLE_FUDS_BINARY_FIELD_NAME = (
|
||||
"Is there at least one Formerly Used Defense Site (FUDS) in the tract?"
|
||||
)
|
||||
ELIGIBLE_FUDS_FILLED_IN_FIELD_NAME = "Is there at least one Formerly Used Defense Site (FUDS) in the tract, where missing data is treated as False?"
|
||||
|
||||
# Tribal variables
|
||||
TRIBAL_ID = "tribalId"
|
||||
TRIBAL_LAND_AREA_NAME = "landAreaName"
|
||||
|
||||
# Tribal overlap variables
|
||||
COUNT_OF_TRIBAL_AREAS_IN_TRACT_CONUS = (
|
||||
"Number of Tribal areas within Census tract"
|
||||
)
|
||||
COUNT_OF_TRIBAL_AREAS_IN_TRACT_AK = (
|
||||
"Number of Tribal areas within Census tract for Alaska"
|
||||
)
|
||||
NAMES_OF_TRIBAL_AREAS_IN_TRACT = "Names of Tribal areas within Census tract"
|
||||
PERCENT_OF_TRIBAL_AREA_IN_TRACT = (
|
||||
"Percent of the Census tract that is within Tribal areas"
|
||||
)
|
||||
PERCENT_OF_TRIBAL_AREA_IN_TRACT_DISPLAY = (
|
||||
"Percent of the Census tract that is within Tribal areas, for display"
|
||||
)
|
||||
IS_TRIBAL_DAC = "Identified as disadvantaged due to tribal overlap"
|
||||
PERCENT_OF_TRACT_IS_DAC = "Percentage of tract that is disadvantaged"
|
||||
|
||||
#####
|
||||
# Names for individual factors being exceeded
|
||||
|
||||
|
|
@ -367,6 +386,15 @@ EXPECTED_BUILDING_LOSS_RATE_LOW_INCOME_FIELD = (
|
|||
)
|
||||
AGRICULTURAL_VALUE_BOOL_FIELD = "Contains agricultural value"
|
||||
|
||||
HIGH_FUTURE_FLOOD_RISK_LOW_INCOME_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for share of "
|
||||
"properties at risk of flood in 30 years and is low income?"
|
||||
)
|
||||
HIGH_FUTURE_WILDFIRE_RISK_LOW_INCOME_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for "
|
||||
"share of properties at risk of fire in 30 years and is low income?"
|
||||
)
|
||||
|
||||
# Clean energy and efficiency
|
||||
PM25_EXPOSURE_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for PM2.5 exposure and is low income?"
|
||||
ENERGY_BURDEN_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for energy burden and is low income?"
|
||||
|
|
@ -378,6 +406,7 @@ DIESEL_PARTICULATE_MATTER_LOW_INCOME_FIELD = (
|
|||
)
|
||||
TRAFFIC_PROXIMITY_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for traffic proximity and is low income?"
|
||||
|
||||
|
||||
# Affordable and Sustainable Housing
|
||||
LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for lead paint and"
|
||||
|
|
@ -385,6 +414,10 @@ LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
|
|||
f"percentile and is low income?"
|
||||
)
|
||||
HOUSING_BURDEN_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for housing burden and is low income?"
|
||||
NO_KITCHEN_OR_INDOOR_PLUMBING_LOW_INCOME_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for "
|
||||
+ "share of homes with no kitchen or indoor plumbing and is low income?"
|
||||
)
|
||||
|
||||
# Remediation and Reduction of Legacy Pollution
|
||||
RMP_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for proximity to RMP sites and is low income?"
|
||||
|
|
@ -394,8 +427,13 @@ HAZARDOUS_WASTE_LOW_INCOME_FIELD = (
|
|||
f" for proximity to hazardous waste facilities and is low income?"
|
||||
)
|
||||
|
||||
AML_LOW_INCOME_FIELD = "There is at least one abandoned mine in this census tract and the tract is low income."
|
||||
ELIGIBLE_FUDS_LOW_INCOME_FIELD = "There is at least one Formerly Used Defense Site (FUDS) in the tract and the tract is low income."
|
||||
|
||||
|
||||
# Critical Clean Water and Waste Infrastructure
|
||||
WASTEWATER_DISCHARGE_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for wastewater discharge and is low income?"
|
||||
UST_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for leaky underground storage tanks and is low income?"
|
||||
|
||||
# Health Burdens
|
||||
DIABETES_LOW_INCOME_FIELD = f"Greater than or equal to the {PERCENTILE}th percentile for diabetes and is low income?"
|
||||
|
|
@ -412,6 +450,7 @@ SCORE_M_LOW_INCOME_SUFFIX = (
|
|||
", is low income, and has a low percent of higher ed students"
|
||||
)
|
||||
|
||||
|
||||
COLLEGE_ATTENDANCE_LESS_THAN_20_FIELD = (
|
||||
"Percent higher ed enrollment rate is less than 20%"
|
||||
)
|
||||
|
|
@ -450,6 +489,10 @@ TRAFFIC_PROXIMITY_LOW_INCOME_LOW_HIGHER_ED_FIELD = (
|
|||
f"traffic proximity{SCORE_M_LOW_INCOME_SUFFIX}?"
|
||||
)
|
||||
|
||||
DOT_TRAVEL_BURDEN_LOW_INCOME_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile "
|
||||
f"for DOT transit barriers and is low income?"
|
||||
)
|
||||
# Affordable and Sustainable Housing
|
||||
LEAD_PAINT_MEDIAN_HOUSE_VALUE_LOW_INCOME_LOW_HIGHER_ED_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for lead paint,"
|
||||
|
|
@ -497,22 +540,22 @@ LOW_LIFE_EXPECTANCY_LOW_INCOME_LOW_HIGHER_ED_FIELD = (
|
|||
# Workforce
|
||||
UNEMPLOYMENT_LOW_HS_EDUCATION_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for unemployment"
|
||||
" and has low HS education?"
|
||||
" and has low HS attainment?"
|
||||
)
|
||||
|
||||
LINGUISTIC_ISOLATION_LOW_HS_EDUCATION_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for households in linguistic isolation"
|
||||
" and has low HS education?"
|
||||
" and has low HS attainment?"
|
||||
)
|
||||
|
||||
POVERTY_LOW_HS_EDUCATION_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for households at or below 100% federal poverty level"
|
||||
" and has low HS education?"
|
||||
" and has low HS attainment?"
|
||||
)
|
||||
|
||||
LOW_MEDIAN_INCOME_LOW_HS_EDUCATION_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for low median household income as a "
|
||||
f"percent of area median income and has low HS education?"
|
||||
f"percent of area median income and has low HS attainment?"
|
||||
)
|
||||
|
||||
# Score M Workforce Variables
|
||||
|
|
@ -580,6 +623,7 @@ PM25_EXCEEDS_PCTILE_THRESHOLD = (
|
|||
)
|
||||
DIESEL_EXCEEDS_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for diesel particulate matter"
|
||||
TRAFFIC_PROXIMITY_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for traffic proximity"
|
||||
DOT_BURDEN_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for DOT travel barriers"
|
||||
LEAD_PAINT_PROXY_PCTILE_THRESHOLD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for lead paint and"
|
||||
f" the median house value is less than {MEDIAN_HOUSE_VALUE_PERCENTILE}th "
|
||||
|
|
@ -588,12 +632,19 @@ LEAD_PAINT_PROXY_PCTILE_THRESHOLD = (
|
|||
HOUSING_BURDEN_PCTILE_THRESHOLD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for housing burden"
|
||||
)
|
||||
NO_KITCHEN_OR_INDOOR_PLUMBING_PCTILE_THRESHOLD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for share "
|
||||
"of homes without indoor plumbing or a kitchen"
|
||||
)
|
||||
|
||||
RMP_PCTILE_THRESHOLD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for RMP proximity"
|
||||
)
|
||||
NPL_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for NPL (superfund sites) proximity"
|
||||
TSDF_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for proximity to hazardous waste sites"
|
||||
WASTEWATER_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for wastewater discharge"
|
||||
UST_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for leaky underwater storage tanks"
|
||||
|
||||
DIABETES_PCTILE_THRESHOLD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for diabetes"
|
||||
)
|
||||
|
|
@ -610,6 +661,29 @@ LOW_LIFE_EXPECTANCY_PCTILE_THRESHOLD = (
|
|||
UNEMPLOYMENT_PCTILE_THRESHOLD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for unemployment"
|
||||
)
|
||||
HIGH_FUTURE_FLOOD_RISK_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for share of properties "
|
||||
"at risk of flood in 30 years"
|
||||
)
|
||||
HIGH_FUTURE_WILDFIRE_RISK_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for share of properties "
|
||||
"at risk of fire in 30 years"
|
||||
)
|
||||
|
||||
# NCLD Nature Deprived
|
||||
TRACT_PERCENT_NON_NATURAL_FIELD_NAME = "Share of the tract's land area that is covered by impervious surface or cropland as a percent"
|
||||
NON_NATURAL_PCTILE_THRESHOLD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for share of the tract's land area that is covered "
|
||||
"by impervious surface or cropland as a percent"
|
||||
)
|
||||
NON_NATURAL_LOW_INCOME_FIELD_NAME = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for share of the tract's land area that is covered "
|
||||
"by impervious surface or cropland as a percent and is low income?"
|
||||
)
|
||||
TRACT_ELIGIBLE_FOR_NONNATURAL_THRESHOLD = (
|
||||
"Does the tract have at least 35 acres in it?"
|
||||
)
|
||||
|
||||
LINGUISTIC_ISOLATION_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for households in linguistic isolation"
|
||||
POVERTY_PCTILE_THRESHOLD = f"Greater than or equal to the {PERCENTILE}th percentile for households at or below 100% federal poverty level"
|
||||
LOW_MEDIAN_INCOME_PCTILE_THRESHOLD = (
|
||||
|
|
@ -623,7 +697,6 @@ ISLAND_LOW_MEDIAN_INCOME_PCTILE_THRESHOLD = (
|
|||
ISLAND_UNEMPLOYMENT_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_UNEMPLOYMENT_FIELD_2009} exceeds {PERCENTILE}th percentile"
|
||||
ISLAND_POVERTY_PCTILE_THRESHOLD = f"{CENSUS_DECENNIAL_POVERTY_LESS_THAN_100_FPL_FIELD_2009} exceeds {PERCENTILE}th percentile"
|
||||
|
||||
|
||||
# Not currently used in a factor
|
||||
EXTREME_HEAT_MEDIAN_HOUSE_VALUE_LOW_INCOME_FIELD = (
|
||||
f"Greater than or equal to the {PERCENTILE}th percentile for summer days above 90F and "
|
||||
|
|
@ -651,6 +724,10 @@ THRESHOLD_COUNT = "Total threshold criteria exceeded"
|
|||
CATEGORY_COUNT = "Total categories exceeded"
|
||||
|
||||
FPL_200_SERIES = "Is low income?"
|
||||
FPL_200_SERIES_IMPUTED_AND_ADJUSTED = "Is low income (imputed and adjusted)?"
|
||||
FPL_200_SERIES_IMPUTED_AND_ADJUSTED_DONUTS = (
|
||||
"Meets the less stringent low income criterion for the adjacency index?"
|
||||
)
|
||||
FPL_200_AND_COLLEGE_ATTENDANCE_SERIES = (
|
||||
"Is low income and has a low percent of higher ed students?"
|
||||
)
|
||||
|
|
@ -666,5 +743,19 @@ MAPPING_FOR_EJ_PRIORITY_COMMUNITY_FIELD = (
|
|||
"Mapping for Environmental Justice Priority Community"
|
||||
)
|
||||
|
||||
# Historic Redlining Score
|
||||
HISTORIC_REDLINING_SCORE_EXCEEDED = (
|
||||
"Tract-level redlining score meets or exceeds 3.25"
|
||||
)
|
||||
|
||||
HISTORIC_REDLINING_SCORE_EXCEEDED_LOW_INCOME_FIELD = (
|
||||
"Tract-level redlining score meets or exceeds 3.25 and is low income"
|
||||
)
|
||||
|
||||
|
||||
ADJACENT_TRACT_SCORE_ABOVE_DONUT_THRESHOLD = (
|
||||
"Is the tract surrounded by disadvantaged communities?"
|
||||
)
|
||||
|
||||
# End of names for individual factors being exceeded
|
||||
####
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import pandas as pd
|
||||
from data_pipeline.score.score import Score
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import pandas as pd
|
||||
from data_pipeline.score.score import Score
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
from collections import namedtuple
|
||||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import pandas as pd
|
||||
from data_pipeline.score.score import Score
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import pandas as pd
|
||||
from data_pipeline.score.score import Score
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import pandas as pd
|
||||
from data_pipeline.score.score import Score
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import pandas as pd
|
||||
from data_pipeline.score.score import Score
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import pandas as pd
|
||||
from data_pipeline.score.score import Score
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import pandas as pd
|
||||
from data_pipeline.score.score import Score
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,6 @@
|
|||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import pandas as pd
|
||||
from data_pipeline.score.score import Score
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
import data_pipeline.score.field_names as field_names
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
|
|||
|
|
@ -1,11 +1,11 @@
|
|||
from typing import Tuple
|
||||
|
||||
import data_pipeline.etl.score.constants as constants
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from data_pipeline.score.score import Score
|
||||
import data_pipeline.score.field_names as field_names
|
||||
from data_pipeline.utils import get_module_logger
|
||||
import data_pipeline.etl.score.constants as constants
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
|
|
|||
1072
data/data-pipeline/data_pipeline/score/score_narwhal.py
Normal file
1072
data/data-pipeline/data_pipeline/score/score_narwhal.py
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -1,17 +1,5 @@
|
|||
import pandas as pd
|
||||
from data_pipeline.score.score_a import ScoreA
|
||||
from data_pipeline.score.score_b import ScoreB
|
||||
from data_pipeline.score.score_c import ScoreC
|
||||
from data_pipeline.score.score_d import ScoreD
|
||||
from data_pipeline.score.score_f import ScoreF
|
||||
from data_pipeline.score.score_g import ScoreG
|
||||
from data_pipeline.score.score_h import ScoreH
|
||||
from data_pipeline.score.score_i import ScoreI
|
||||
from data_pipeline.score.score_k import ScoreK
|
||||
from data_pipeline.score.score_l import ScoreL
|
||||
from data_pipeline.score.score_m import ScoreM
|
||||
from data_pipeline.score import field_names
|
||||
|
||||
from data_pipeline.score.score_narwhal import ScoreNarwhal
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
|
@ -23,46 +11,6 @@ class ScoreRunner:
|
|||
self.df = df
|
||||
|
||||
def calculate_scores(self) -> pd.DataFrame:
|
||||
# Index scores
|
||||
self.df = ScoreA(df=self.df).add_columns()
|
||||
self.df = ScoreB(df=self.df).add_columns()
|
||||
self.df = ScoreC(df=self.df).add_columns()
|
||||
self.df = ScoreD(df=self.df).add_columns()
|
||||
self.df = ScoreF(df=self.df).add_columns()
|
||||
self.df = ScoreG(df=self.df).add_columns()
|
||||
self.df = ScoreH(df=self.df).add_columns()
|
||||
self.df = ScoreI(df=self.df).add_columns()
|
||||
self.df = ScoreK(df=self.df).add_columns()
|
||||
self.df = ScoreL(df=self.df).add_columns()
|
||||
self.df = ScoreM(df=self.df).add_columns()
|
||||
|
||||
# TODO do this with each score instead of in a bundle
|
||||
# Create percentiles for these index scores
|
||||
self.df = self._add_score_percentiles()
|
||||
self.df = ScoreNarwhal(df=self.df).add_columns()
|
||||
|
||||
return self.df
|
||||
|
||||
def _add_score_percentiles(self) -> pd.DataFrame:
|
||||
logger.info("Adding Score Percentiles")
|
||||
for score_field in [
|
||||
field_names.SCORE_A,
|
||||
field_names.SCORE_B,
|
||||
field_names.SCORE_C,
|
||||
field_names.SCORE_D,
|
||||
field_names.SCORE_E,
|
||||
]:
|
||||
self.df[
|
||||
f"{score_field}{field_names.PERCENTILE_FIELD_SUFFIX}"
|
||||
] = self.df[score_field].rank(pct=True)
|
||||
|
||||
for threshold in [0.25, 0.3, 0.35, 0.4]:
|
||||
fraction_converted_to_percent = int(100 * threshold)
|
||||
self.df[
|
||||
f"{score_field} (top {fraction_converted_to_percent}th percentile)"
|
||||
] = (
|
||||
self.df[
|
||||
f"{score_field}{field_names.PERCENTILE_FIELD_SUFFIX}"
|
||||
]
|
||||
>= 1 - threshold
|
||||
)
|
||||
return self.df
|
||||
|
|
|
|||
56
data/data-pipeline/data_pipeline/score/utils.py
Normal file
56
data/data-pipeline/data_pipeline/score/utils.py
Normal file
|
|
@ -0,0 +1,56 @@
|
|||
"""Utilities to help generate the score."""
|
||||
import data_pipeline.score.field_names as field_names
|
||||
import geopandas as gpd
|
||||
import pandas as pd
|
||||
from data_pipeline.etl.sources.geo_utils import get_tract_geojson
|
||||
from data_pipeline.utils import get_module_logger
|
||||
|
||||
# XXX: @jorge I am torn about the coupling that importing from
|
||||
# etl.sources vs keeping the code DRY. Thoughts?
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
||||
def calculate_tract_adjacency_scores(
|
||||
df: pd.DataFrame, score_column: str
|
||||
) -> pd.DataFrame:
|
||||
"""Calculate the mean score of each tract in df based on its neighbors
|
||||
|
||||
Args:
|
||||
df (pandas.DataFrame): A dataframe with at least the following columns:
|
||||
* field_names.GEOID_TRACT_FIELD
|
||||
* score_column
|
||||
|
||||
score_column (str): The name of the column that contains the scores
|
||||
to average
|
||||
Returns:
|
||||
df (pandas.DataFrame): A dataframe with two columns:
|
||||
* field_names.GEOID_TRACT_FIELD
|
||||
* {score_column}_ADJACENT_MEAN, which is the average of score_column for
|
||||
each tract that touches the tract identified
|
||||
in field_names.GEOID_TRACT_FIELD
|
||||
"""
|
||||
ORIGINAL_TRACT = "ORIGINAL_TRACT"
|
||||
logger.debug("Calculating tract adjacency scores")
|
||||
tract_data = get_tract_geojson()
|
||||
df: gpd.GeoDataFrame = tract_data.merge(
|
||||
df, on=field_names.GEOID_TRACT_FIELD
|
||||
)
|
||||
df = df.rename(columns={field_names.GEOID_TRACT_FIELD: ORIGINAL_TRACT})
|
||||
|
||||
logger.debug("Perfoming spatial join to find all adjacent tracts")
|
||||
adjacent_tracts: gpd.GeoDataFrame = df.sjoin(
|
||||
tract_data, predicate="touches"
|
||||
)
|
||||
|
||||
logger.debug("Calculating means based on adjacency")
|
||||
return (
|
||||
adjacent_tracts.groupby(field_names.GEOID_TRACT_FIELD)[[score_column]]
|
||||
.mean()
|
||||
.reset_index()
|
||||
.rename(
|
||||
columns={
|
||||
score_column: f"{score_column}{field_names.ADJACENCY_INDEX_SUFFIX}",
|
||||
}
|
||||
)
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue