Climate and Economic Justice Screening Tool (cloud-native formats)

The Justice40 initiative and CEJST were announced in the Executive Order on Tackling the Climate Crisis at Home and Abroad in January 2021. The CEJST includes interactive maps and an initial draft scorecard which federal agencies can use to prioritize historically overburdened and underserved communities for benefits in their programs. https://screeningtool.geoplatform.gov/, https://github.com/usds/justice40-tool

Product Details

Visibility: Public
Owner: Carl Boettiger
Created: 25 Apr 2024
Last Updated: 3 Apr 2025

Product Contents

root

Disadvantaged_Communities.gdb

disadvantaged-communities.parquet

disadvantaged-communities.pmtiles

justice40.html

map.py

README.md

README

Climate and Economic Justice Screening Tool

The official tool is hosted at https://screeningtool.geoplatform.gov

This source.coop repository provides access to this data as PMTiles and geoparquet. PMTiles provides static vector tiles enabling many popular client-side libraries to quickly and easily render interactive maps involving very large vector datasets. The Geoparquet format is optimized for cloud-based (i.e. streaming) analysis workflows without the need to download and store the data.

Using PMTiles

example map

See map.py for soruce code

Using geoparquet

1import ibis # pip install ibis-framework
2from ibis import _
3
4summary = (ibis
5    .read_parquet("https://data.source.coop/cboettig/justice40/disadvantaged-communities.parquet")
6    .group_by(_.StateName)
7    .agg(fraction_disadvantaged = (_.Disadvan * _.SHAPE_Area).sum() / _.SHAPE_Area.sum())
8    .order_by(ibis.desc(_.fraction_disadvantaged))
9    .to_pandas()
10)
11
12summary
1import ibis # pip install ibis-framework
2from ibis import _
3
4summary = (ibis
5    .read_parquet("https://data.source.coop/cboettig/justice40/disadvantaged-communities.parquet")
6    .group_by(_.StateName)
7    .agg(fraction_disadvantaged = (_.Disadvan * _.SHAPE_Area).sum() / _.SHAPE_Area.sum())
8    .order_by(ibis.desc(_.fraction_disadvantaged))
9    .to_pandas()
10)
11
12summary