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Spatial Equity Data Tool: Level

Measure Disparities in Your Data

Assess demographic and spatial disparities to see if resources are equitably distributed.

Demographic distribution of your data compared with the
in the
Hover over the dots to see how demographic groups are over- and underrepresented in your data?
Underrepresented
No significant difference
Overrepresented
Download chart image (PNG)
Download chart data (CSV)
Notes: Demographic categories for Asian, Black, White, and all other races and ethnicities include Latinx and non-Latinx residents, unless noted otherwise. When using “children under 18” as a baseline, the “uninsured” category includes 18-year-olds (i.e., children under 19).
Geographic distribution of your data compared with the
in the
Disparity Score
See which are over- and underrepresented in your data?
This contains:
of your data points
of the
has percent of the data points than we’d expect if the data were distributed in accordance with your baseline.
No statistically significant difference between your data and the
Data comparison
See your data side by side with the total population in the
Disparity Score
Percent of data / Percent of baseline
Download map data
Download map image (PNG)
Download map image (PNG)

Spatial Equity Data Tool

Measure Disparities in Your Data

Last updated March 19, 2024

Are libraries evenly located across the country? Do all residents in your state—regardless of race or income—have equitable access to polling places? Does your dataset accurately reflect your county’s or city’s population?

This tool can help government agencies, policymakers, and community advocates easily answer such questions by assessing demographic and spatial disparities in their data.

Choose a geography below to get started. Then check out our sample datasets or upload your own data.

Upload your own data
Use sample data
National-level data
Upload your own data
Use sample data
State-level data
Upload your own data
Use sample data
County-level data
Upload your own data
Use sample data
City-level data
Sample data
Your data

Use sample data

These are examples of -level datasets that can be evaluated with the tool. Choose a dataset to explore how the tool works or download the data to see what a compatible file looks like.

You can run the analysis on the sample dataset with our preset options, or you can add custom filters and weights by selecting advanced options below.

For help, see our documentation.

Public Wi-Fi hotspots
(2020)
New York, NY
311 requests
(2012–2018)
New Orleans, LA
Bike share stations
(2021)
Minneapolis, MN
Playgrounds
(2020)
Miami-Dade County, FL
Polling places
(2021)
Bucks County, PA
Substance use and mental health facilities (2021)
Washington
Low-Income Housing Tax Credit projects (2019)
Alabama
Electric vehicle charging stations (2021)
United States
Public libraries
(2019)
United States
Preset advanced options
Your advanced options
Advanced options
Run analysis
Sample data
Your data

Upload your data

Upload a CSV file of geographic point data to see how well your dataset represents population.

For help, see our documentation.

Choose a file
or drag it here
Upload a CSV file

Instructions

Your CSV file must have unique column headers.
Two columns must correspond to longitude and latitude.
Your file size must not exceed 200 MB
Note: The national tool can only analyze data from the entire US (50 states plus the District of Columbia). If your file contains data for only a subset of the US (e.g., a few states) it will be compared against the entire US, so results may not be accurate. Instead, you may want to choose a different geography from the home page to analyze the state, county, or city that appears most frequently in your data.
Note: The state tool can only analyze data for US states (the 50 states plus the District of Columbia) and for one state at a time.
  • If your file contains data from multiple states, only the state that appears most frequently in the data will be analyzed.
  • If your file contains data for a subset of the state (e.g., a few counties or a few cities), it will be compared against the entire state, so results may not be accurate. Instead, you may want to choose a different geography from the home page to analyze the county or city that appears most frequently in your data.
Note: The county tool can only analyze data for US counties (within the 50 states and the District of Columbia) and for one county at a time. If your file contains data from multiple counties, only the county that appears most frequently in the data will be analyzed.
Note: The city tool can only analyze data for US cities with populations above 50,000 and for one city at a time. If your file contains data from multiple cities, only the city that appears most frequently in the data will be analyzed.

Which columns in your file represent longitude and latitude?

Longitude
Latitude
Advanced options
Run analysis
Filter data
Filters applied (0)

Advanced options: Filter data

Choose a column to filter by and the tool will detect which values—text, numbers, or dates—are in that column. The filters will allow you to focus on data you want to use and hide data you don’t. Columns with both text and numbers will be treated as text fields.

For help, see our documentation.

Choose a column to filter by

The values in this column look like text. If they’re not, you may need to reformat this column.
The values in this column look like numbers. If they’re not, you may need to reformat this column.
The values in this column look like dates. If they’re not, you may need to reformat this column.
List one or more values separated by commas (e.g., elementary,middle). The values are case-sensitive and must match the data exactly.
Start date
to
End date
✓
Date range
apply filter
Applied filters
Cancel
Save
Weight data

Advanced options: Weight data

You can weight the rows by a numeric column in your data when calculating representativeness. For example, in a dataset of library locations, if you choose “sq_ft” as your weight column, higher–square footage libraries will be given more weight in assessing representativeness.

For help, see our documentation.

Choose a weight column

Selected weight

none selected
Cancel
Save

Sit tight! We’re analyzing your data.

Processing rows of data
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