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Access dataset files directly from scripts, code, or AI agents.
Each file has a stable URL (r-link) that you can use directly in scripts, apps, or AI agents. These URLs are permanent and safe to hardcode.
Start with these files — they give you everything you need to understand and access the dataset.
- 1. Fetch datapackage.json to inspect schema and resources
- 2. Download data resources listed in datapackage.json
- 3. Read README.md for full context
Data Views
Distribution of Exposure Segregation Across U.S. Counties
Exposure Segregation vs. Bridging Index by Metro Area
Data Files
Explore with AISocioeconomic Exposure Segregation by County
| Field | Type | Description |
|---|---|---|
| exposure_segregation | number | Raw exposure segregation index. Higher values indicate greater socioeconomic segregation in daily mobility patterns. |
| fips_code | integer | County FIPS code (5-digit Federal Information Processing Standard) |
| county_name | string | County name |
| exposure_segregation_smoothed | number | Smoothed exposure segregation index (0–1 scale). Values smoothed to account for small-area estimation noise. |
Download
Download CSVAbout
- Socioeconomic exposure segregation index for 2,828 U.S. counties, derived from anonymized cell phone mobility data (SafeGraph, 2017). Measures the degree to which lower- and higher-income residents encounter each other in their daily movements.
- Last updated
- 17 March 2026
- Total rows
- ...
- Format
- CSV
- File size
- 132 kB
Socioeconomic Exposure Segregation by Metropolitan Area
| Field | Type | Description |
|---|---|---|
| exposure_segregation | number | Exposure segregation index. Higher values indicate greater socioeconomic segregation in daily mobility patterns. |
| msa | string | Metropolitan Statistical Area name (e.g. 'New York, NY') |
| bridging_index | number | Bridging index (0–1). Measures cross-socioeconomic connectivity — how much lower-income residents encounter higher-income residents relative to a random baseline. Higher values indicate more bridging across economic groups. |
Download
Download CSVAbout
- Socioeconomic exposure segregation for 382 U.S. metropolitan statistical areas (MSAs), with bridging index. Derived from 1.6 billion person-to-person encounters among 9.6 million individuals (SafeGraph mobility data, 2017). Key finding: exposure segregation is 67% higher in the ten largest metros than in small MSAs.
- Last updated
- 17 March 2026
- Total rows
- ...
- Format
- CSV
- File size
- 23.2 kB
About this dataset
Segregation Tracking Project
Comprehensive tracking of segregation across U.S. neighborhoods and schools — a collaboration between USC and Stanford.
Overview
The Segregation Tracking Project provides data on racial, ethnic, and economic segregation across every U.S. neighborhood and school. Maintained by Sean Reardon (Stanford) and Ann Owens (UCLA).
This dataset includes the publicly available exposure-segregation data from the companion research paper (Nilforoshan et al., Nature 2023), which measures socioeconomic segregation through anonymized mobility patterns.
Data
| File | Description | Rows |
|---|---|---|
data/exposure-segregation-county.csv | Exposure segregation by county | 2,828 |
data/exposure-segregation-msa.csv | Exposure segregation by metro area | 382 |
County-level data
| Field | Description |
|---|---|
exposure_segregation | Raw exposure segregation index |
fips_code | County FIPS code |
county_name | County name |
exposure_segregation_smoothed | Smoothed index (0–1 scale) |
MSA-level data
| Field | Description |
|---|---|
exposure_segregation | Exposure segregation index |
msa | Metropolitan Statistical Area name |
bridging_index | Cross-group connectivity index (0–1) |
Key Finding
Socioeconomic exposure segregation is 67% higher in the ten largest metropolitan areas than in small MSAs with fewer than 100,000 residents — contradicting the assumption that large, diverse cities promote economic mixing.
Methodology
- Based on 1.6 billion person-to-person encounters among 9.6 million individuals
- Data source: Anonymized cell phone location records (SafeGraph, 2017, 3-month window)
- Socioeconomic status inferred from nighttime home location and local rental prices
- Exposure Segregation: measures how much lower-income and higher-income residents encounter each other in daily movements
- Bridging Index: normalized measure (0–1) of cross-income encounters relative to a random baseline
Full Dataset Access
The complete Segregation Tracking Project dataset — covering racial/ethnic and economic segregation across neighborhoods and schools from 1970 to the present — is available at:
https://edopportunity.org/segregation/data/
Registration (email + data use agreement) required. Free for research use; commercial use prohibited.
Source Paper
Nilforoshan, H., Looi, W., Pierson, E., Villanueva, B., Fishman, N., Chen, Y., Sholar, J., Redbird, B., Grusky, D., & Leskovec, J. (2023). Human mobility networks reveal increased segregation in large cities. Nature, 623, 71–77. https://doi.org/10.1038/s41586-023-06757-3
Project Credits
Segregation Tracking Project: Sean Reardon (Stanford), Ann Owens (UCLA), Demetra Kalogrides (Stanford). Funded by Russell Sage Foundation, Robert Wood Johnson Foundation, and Bill & Melinda Gates Foundation.
Exposure-Segregation Research: Hamed Nilforoshan, Wenli Looi, Emma Pierson, Blanca Villanueva, Nic Fishman, Yiling Chen, John Sholar, Beth Redbird, David Grusky, Jure Leskovec — Stanford SNAP Lab.