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aca226165c
* 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> |
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| .. | ||
| calenviroscreen | ||
| cdc_life_expectancy | ||
| cdc_places | ||
| cdc_svi_index | ||
| census | ||
| census_acs | ||
| census_acs_2010 | ||
| census_acs_median_income | ||
| census_decennial | ||
| child_opportunity_index | ||
| doe_energy_burden | ||
| dot_travel_composite | ||
| eamlis | ||
| ejscreen | ||
| ejscreen_areas_of_concern | ||
| energy_definition_alternative_draft | ||
| epa_rsei | ||
| fsf_flood_risk | ||
| fsf_wildfire_risk | ||
| geocorr | ||
| historic_redlining | ||
| housing_and_transportation | ||
| hud_housing | ||
| hud_recap | ||
| mapping_for_ej | ||
| mapping_inequality | ||
| maryland_ejscreen | ||
| michigan_ejscreen | ||
| national_risk_index | ||
| nlcd_nature_deprived | ||
| persistent_poverty | ||
| tree_equity_score | ||
| tribal | ||
| tribal_overlap | ||
| us_army_fuds | ||
| __init__.py | ||
| geo_utils.py | ||