Public beta - This website will be continuously updated
Limited data sources — This tool currently includes 16 datasets. Over time, datasets could be added, updated, or removed. The datasets come from a variety of sources based on availability, quality, and relevance to environmental, energy, and climate issues. Each dataset has limitations, such as how recently the data was updated.
Methodology
The Just Progress tool combines demographic, environmental, and socio-economic data to generate a cumulative index score, referred to as the Just Progress Index. The tool currently utilizes national, publically-available data from the United States Census Bureau’s American Community Survey (ACS) and the EPA’s EJScreen tool.
The various inputs into the Just Progress Index are averaged into 2 categories: Pollution Burden and Demographics.
Pollution Burden: health risks arising from proximity and potential exposures to pollution and other adverse environmental conditions
Demographics: sensitive populations and socioeconomic factors that make a community more vulnerable
Pollution Burden average x Demographics average = Just Progress Index
Datasets used in cumulative score
The datasets come from a variety of sources and were selected after considering relevance, availability, recency and quality.
Poverty
- Data resolution: Census block group
- Data source: U.S. Census Bureau
- Data date range: 5-year estimates, 2015-2019
Education (less than high school)
- Data resolution: Census block group
- Data source: U.S. Census Bureau
- Data date range: 5-year estimates, 2015-2019
Linguistic isolation
- Data resolution: Census block group
- Data source: U.S. Census Bureau
- Data date range: 5-year estimates, 2015-2019
Unemployment rate
- Data resolution: Census block group
- Data source: U.S. Census Bureau
- Data date range: 5-year estimates, 2015-2019
Housing burden
- Data resolution: Census block group
- Data source: U.S. Census Bureau
- Data date range: 5-year estimates, 2015-2019
Gather datasets
Data inputs
The cumulative index score includes the following equally weighted inputs.
- Poverty
- Less than high school education
- Linguistic isolation
- Unemployment rate
- Housing burden
Combining data from different geographic units
Some data is not available at the census block group level and is instead only available for larger units such as census tracts or counties. In these cases, all census block groups will get an even contribution from the larger unit. For example, if a census tract scores 90th percentile on an indicator, then all census block groups within that tract will receive a value of 90th percentile.
Normalizing data
The range of the data that makes up the score varies, so the data must be normalized so that each data indicator can be more equally weighted. Min-max normalization is utilized, where the minimum value in the range of values for each dataset is set at 0, the maximum value is set at 1, and every other value is transformed into a decimal between 0 and 1. For example, if the minimum value for unemployment was 10 and the maximum value was 30, a value of 20 would be transformed to 0.5 since it is halfway between 10 and 30.
Calculate cumulative index score
To combine all variables into a single cumulative index score, we average the normalized values across indicators.
Dataset 1 + Dataset 2 + ... + Dataset N# of datasets=Cumulative index scoreAssign priority
Census block groups are sorted by their cumulative index score from highest to lowest. Census block groups that are in the top 25 percentile (i.e. have a cumulative index score in the 75 - 100th percentile) will be considered the prioritized communities.