Spatial Justice Test for Race and Income

Spatial Justice Test Instructions

This test lets you examine whether the race and income of people who live near a set of points is different from those who live farther away.
For instance, you might want to test if parks, schools, or landfills were disproportionately located near lower-income or higher-income people.

To test your own data you must upload a CSV data file with the format of Latitude, Longitude (a CSV file with two columns of data). The first line can include text column titles if you want. If the first line's fields are text than they will be ignored.
Example: Sample Data File (US nuclear plants - old data)

Or you can run a test using our power plant data.

The calculations can take several minutes. At regular settings it processes approximately 6 points per second. The current time limit is 100 minutes. Email me if you need more time.

What are you analyzing? Ex. landfill.
Describe your data and project (a notes field for sharing it with the public)
You can either analyze a power plant layer from Energy Justice Communities Map OR upload your own data file.
Power Plant
Power Plant Status
State (optional)
Data File (latitude, longitude). Must be .csv, .txt, or .dat.
Number of distances to analyze (more is slower)
Single distance - in miles
(if used this replaces the number of distances option above)
Accuracy (more is slower)
Weighing Results
Weight: What points to include
Saving Results

If you have any problems or suggestions you can Email me

Powered by The race data comes from the Census (2010) and the income data comes from the American Community Survey (2010-2014).
Power plant data is from Energy Justice Communities Map.

Technical Notes
This does a call to the JusticeMap API for each data point. It sums the total number of people of each race who live in that area. And it sums income by multiplying the median household income for the area by the number of people who live in that area (this introduces a small amount of error due to differences in average household size -- but this is probably a minor factor compared to the income data confidence interval). People who live near multiple facilities are counted once for each facility that they live near (and thus can be counted multiple times).

The geographical unit of analysis depends on the distance and the Accuracy option. Normal: blocks up to 5 miles, then block groups up to 50 miles, then census tracts. Higher: blocks up to 10 miles, block groups up to 100 miles, then tracts. Very High: blocks up to 25 miles, then block groups. Highest: blocks up to 50 miles, then block groups.
You can see how changing the unit of analysis will alter your results.

The geographical join type includes areas that have a centroid within the specified distance (Ex. find all the blocks with a centroid within 1 mile of a point).

Speed: the API calls are faster than doing this from your own server as this is located on the same server as the data. You can also get this script to run faster by reducing the Accuracy option, by reducing the Number of Distances option, or analyzing a smaller number of points.