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d5fbb802e8* 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) |
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| .. | ||
| experiment_4_weighting | ||
| home_foreclosures_analysis_nhpd | ||
| ACS Validate.ipynb | ||
| agricultural_loss_indicator.ipynb | ||
| census_explore.ipynb | ||
| compare_two_score_files_for_differences.ipynb | ||
| county_lookup.ipynb | ||
| EJScreen Validate.ipynb | ||
| ejscreen_load.ipynb | ||
| generate_fuds_test_data.ipynb | ||
| Score Validate.ipynb | ||
| Score_Dissolve_Script.ipynb | ||
| score_explore.ipynb | ||
| scoring_comparison.ipynb | ||
| TractArea.ipynb | ||
| urban_vs_rural.ipynb | ||