2 Overview of the Spatial Equity Data Tool
This chapter provides an overview of the Spatial Equity Data Tool (SEDT) through a series of frequently asked questions.
How does the tool work?
This tool uses data from the five-year American Community Survey (ACS) to assess demographic and spatial disparities in your data. Once you upload data, the tool compares the distribution of the uploaded data with the distribution of baseline variables from the ACS. The tool returns two sets of disparity scores: a geographic disparity score and a demographic disparity score.
The tool constructs geographic disparity scores by taking the difference between the percentage of the resource dataset and the percentage of the selected baseline population that falls into each state (national-level tool), county (state-level tool), or tract (county- and city-level tool).
For the demographic disparity scores, the tool first estimates the demographic groups your resource data depicts by taking the average demographics across all the tracts your data come from weighted by the amount of resource data in a given tract. We compare that average with the overall demographics of the selected baseline population and report the difference as the demographic disparity score. For more details on the demographic and geographic disparity scores, see Chapter 8.
We use the ACS formulas (US Census Bureau 2020) to calculate 95 percent confidence intervals for both the geographic and the demographic disparity scores. If the interval contains zero, we mark that disparity as not statistically significant. For more information about the specific methodology and variables used and how they vary by geography, please see Chapter 14.
For the demographic disparity chart, we use “Latinx” to refer to people of Latino or Hispanic origin as asked by the census. We recognize that this gender-neutral term may not be the preferred identifier for many, but we use it to remain inclusive of gender nonconforming and nonbinary people. The regions users may select on the chart correspond to US census regions.
For the national-level tool, we use “national” to refer to the 50 states plus the District of Columbia. We realize this excludes US territories from analysis and hope to include them in future iterations of this tool.
Where do the baseline and demographic data come from?
The data come from the ACS five-year tract-, county-, state-, and national level estimates. The API allows users to choose whether to compare their data against the 2015–19 ACS, 2017–21 ACS, or 2018–2022 ACS the most up-to-date data that is available as of the most recent update (see Chapter 18). The web tool compares all uploaded data against the 2018–22 ACS.
We chose these demographic variables and baseline populations because we believe they are common variables and target populations that municipalities and service providers are interested in, based on conversations with key stakeholders and early beta testers.
Who should use the tool?
Anyone interested in understanding the representativeness of data or programs in the US could use this tool. We think it has two main uses.
First, the Spatial Equity Data Tool can identify whether a given dataset is representative of the target population. It’s important to check data are representative before using them for analysis or decisionmaking. For example, a government official interested in using 311 request data to target public works spending could use this tool to learn whether any neighborhoods or groups are underrepresented in the 311 data.
Second, the tool can identify whether place-based interventions are equitably distributed. Such interventions include any program or service that can be tied to a physical location: parks, bike-share stations, Wi-Fi hotspots, electric-vehicle charging stations, food-distribution sites—and many more! Government officials can use this tool to examine whether a planned intervention equitably reaches affected neighborhoods and groups. Community organizations and residents can use this tool to advocate for more equitable distribution of resources. Nonprofits can use this tool to target their programs to areas underserved by other programs.
What do I need to use this tool?
Users have the choice of either using sample data built into the tool (such as low-income housing tax credit projects in the state of Alabama) or to upload their own point spatial data (such as park locations), which we call a resource dataset, to explore with the tool.
Users who would like to upload their own dataset should ensure that the data file of interest meets the requirements outlined in Chapter 6.
What is the difference between the website tool and the API?
Researchers and web developers at the Urban Institute developed an application programming interface (API) to expand access to Urban’s Spatial Equity Data Tool (SEDT). Both the API and the web tool allow users to upload point spatial data, which we refer to as a resource dataset for analysis to produce both the geographic and demographic disparity scores. The API introduces three new pieces of functionality that are not available in the web tool:
The API allows users to upload supplemental demographic datasets that contain information on additional demographic groups to include in the calculation of demographic disparity scores.
The API allows users to upload supplemental geographic datasets that contain information on additional target populations to use in the calculation of geographic disparity scores.
The API allows users to choose whether to compare their data against the 2015–19 ACS, 2017–21 ACS, or 2018–2022 ACS the most up-to-date data that is available as of the most recent update (see {#sec-version-history}). The web tool compares all uploaded data against the 2018–22 ACS.
Users should perform any filtering or preprocessing on their data before submitting to the API (e.g, removing certain rows of data or generating latitude and longitude, respectively). Unlike the web tool, the API does not allow for users to filter records in the resource dataset.
Where can I get more information?
For more information about our sample datasets, see our entry in Urban Institute’s Data Catalog.
For more information about the data and methods used in our tool, please see Chapter 4.
The code can also be found on GitHub.
For other questions, please contact sedt@urban.org.