Spatial Justice Test for Race and Income

You can test if race and income changes as you move closer or further to a set of points.
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.
Sample Data File (US nuclear plants - old data)

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

This website will return results that show how income and race change with distance from your data. The calculations can take several minutes. For instance analyzing a thousand points with the 'more distances' option can take 5 minutes. Doing more than 10,000-15,000 points is likely to time out. The current time limit is 100 minutes. Email me if you need more time.

Note: your results are saved in a file that is shared with the public.



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)


If you have any problems or suggestions you can Email me

Powered by JusticeMap.org and using Census data (2010 for race, and 2010-2014 for income).
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.

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.