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Spatial Equity Data Tool

Measuring Disparities in Your City

How can cities ensure that resources are equitably distributed to all residents? We created this tool to help city officials and community organizations quickly assess spatial and demographic disparities in their cities.

Geographic distribution of your data compared with the city’s
Disparity Score
See which census tracts are over- and underrepresented in your data
Data comparison
See your data side by side with the city’s total population
Neighborhood Disparity Score
Percent of data / Percent of baseline
Download map data
Download map image (.PNG)
Download map image (.PNG)
Demographic distribution of your data compared with the city’s total population
Choose demographic categories to see which groups are over- and underrepresented in your data
Race/ethnicity
AsianBlackLatinxWhiteAll other races and ethnicities
Income/wealth
Low-income residentsExtremely low–income residentsCost-burdened renter households?Unemployed residents
Education
Residents with a bachelor’s degreeResidents with less than a high school diploma
Age
Seniors (65+)Children (< 18)
Other demographics
RentersVeteransResidents with a disabilityUninsured residentsHouseholds without Internet accessHouseholds with limited English proficiency
No significant difference
Underrepresented
Overrepresented
Underrepresented
Overrepresented
Download chart image (.PNG)
Download chart data (.CSV)

Spatial Equity Data Tool

Measuring Disparities in Your City

September 24, 2020

How can cities ensure that resources are equitably distributed to all residents? We created this data tool to help city officials, community organizations, and residents quickly assess spatial and demographic disparities in their cities.

Are wi-fi hotspots unevenly located in your city? Do all residents have equitable access to bike share stations? Does your dataset accurately reflect your city’s population? What groups or neighborhoods are underrepresented?

This tool can reveal biases in the datasets policymakers use to make decisions or illustrate disparities in how resources are distributed throughout the city. Check out our sample datasets or upload your own data.

Use sample data
OR
Upload your own data
Sample data
Your data
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Use sample data

These are examples of city-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 filter or weight this sample dataset by choosing an advanced option or run the analysis with our preset options.

For help, see our FAQ.

Choose one
Public wi-fi hotspots
New York, NY
311 requests
New Orleans, LA
Bike share stations
Minneapolis, MN
Preset advanced options
Your applied advanced options
Advanced options
Run analysis
Sample data
Your data
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Upload your data

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

For help, see our FAQ.

Choose a file
or drag it here
Upload a CSV file

Instructions

Your CSV must have unique column headers.
Two columns must correspond to longitude and latitude.
Your file size must not exceed 2GB
Note: The tool can only analyze data for US cities and for one city at a time. If your file contains data from multiple cities, only the city most frequently appearing in the data will be analyzed.

Which columns in your file represent latitude and longitude?

Longitude
Latitude
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Filter data
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Choose a column

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 FAQ.

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
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Weight data
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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 public parks, if you choose “square footage” as your weight column, higher-square-footage parks will be given more weight in assessing representativeness.

For help, see our FAQ.

Choose a weight column

Selected weight

none selected
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Save

Sit tight! We’re analyzing your data.

Processing of rows of data
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Weight data
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Cost-burdened renter households
Households that pay more than 35 percent of their income on rent.