Abstract

The Opportunity Atlas has collected contextual data by county and tract. Rather than providing contextual socioeconomic data of where people currently live, the data represents average socioeconomic indicators (e.g., earnings) of where people grew up.

Documentation

A core element of Population Health Science is that health outcomes can only be fully understood when they are studied within their context. Therefore, we have a copy of The Opportunity Atlas, a dataset that provides socioeconomic data by county and tract.

Several studies have shown that especially childhood neighborhoods drive adult outcomes and that residential areas lived in through adulthood have much smaller effects. The focus of the Opportunity Atlas is therefore on contextual data of where people grew up:

%3E Traditional measures of poverty and neighborhood conditions provide snapshots of income and other variables for residents in an area at a given point in time. But to study how economic opportunity varies across neighborhoods, we really need to follow people over many years and see how one’s outcomes depend upon family circumstances and where on grew up. The Opportunity Atlas is the first dataset that provides such longitudinal information at a detailed neighborhood level. Using the Atlas, you can see not just where the rich and poor currently live – which was possible in previously available data from the Census Bureau – but whether children in a given area tend to grow up to become rich of poor. This focus on mobility out of poverty across generations allows us to trace the roots of outcomes such as poverty and incarceration back to where kids grew up, potentially permitting much more effective interventions.

As such, The Opportunity Atlas data provides a rich source of data for researchers who wish to overlay health data with contextual data.

Methodology

Three sources of Census Bureau are linked to compute the data

1. The 2000 and 2010 Decennial Census short form
2. Federal income tax returns for 1989, 1994, 1995, 1998-2015
3. The 2000 Decennial Census long form and the 2005-2015 American Community Surveys (ACS).

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20.5 million Americans born between 1987-1983 are sampled from these data and mapped back to the Census tracts they lived in through age 23. After that step, a range of outcomes are then estimated for each of the 70,000 tracts. In order to comply with federal data disclosure standards and protect the privacy of individuals no estimates in tracts with 20 or fewer children are published and noise (small random numbers) is added to all the estimates.

For more information on the data collection and methodology, please visit:

Website

Documentation

Data availability

Some variables are available for counties only. The table below gives you an overview. Open the table in a new tab for a larger view.

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This dataset contains replication files for "The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility" by Raj Chetty, John Friedman, Nathaniel Hendren, Maggie R. Jones, and Sonya R. Porter. For more information, see https://opportunityinsights.org/paper/the-opportunity-atlas/. A summary of the related publication follows. We construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children’s earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby tracts: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, we show that the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods’ causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low-income families, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets. Click here to view the Opportunity Atlas Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The statistical summaries reported in this paper have been cleared by the Census Bureau’s Disclosure Review Board release authorization number CBDRB-FY18-319.

This table includes children's outcomes and neighborhood characteristics in adulthood by neighborhood where they grew up by US tracts.

The table All Outcomes by Census Tract, Race, Gender4 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 73278 rows across 330 variables.

All code and input files used in k-means clustering analysis of Opportunity Atlas data. This dataset is associated with the following publication: Zelasky, S., C. Martin, C. Weaver, L. Baxter, and K. Rappazzo. Identifying groups of children's social mobility opportunity for public health applications using k-means clustering. Heliyon. Elsevier B.V., Amsterdam, NETHERLANDS, 9(9): E20250, (2023).

The table All Outcomes by Census Tract, Race, Gender1 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 73278 rows across 2501 variables.

Shapefiles of the Aquaculture Opportunity Area (AOA) study developed during 2021 for the Southern California Bight. Included in this dataset are: (1) Study areas in the Southern California Bight developed based on depth and jurisdictional boundaries. Four study areas were identified (North, Central North, Central South, South). (2) Suitability modeling results for the North, Central North, Central South, and South Southern California Bight study areas are presented as categories (“Unsuitable,†“Low,†“Moderate,†“High†) (3) High-High clusters (HH) from the Aquaculture Opportunity Atlas for Southern California. Clusters were identified within each of the four study areas (North, Central North, Central South, and South). (4) Refined High-High clusters (HH) from the Aquaculture Opportunity Atlas for Southern California. Clusters were identified within each of the four study areas (North, Central North, Central South, and South). (5) Options from the Aquaculture Opportunity Atlas for Southern California. Options were identified within two of the study areas, North and Central North.

The table HII for Children from Low-Income Households by Census Tract is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 73278 rows across 65 variables.

Geospatial data representing the Aquaculture Opportunity Area (AOA) study developed during 2024 for the State of Alaska. Included in this dataset are: (1) Study areas in State of Alaska waters developed based on maximum sea ice coverage, distance from coastal populated places, existing and active aquaculture sites, and the Alaska state water boundary. Ten distinct study areas were identified (Cordova, Craig, Juneau, Ketchikan, Kodiak, Petersburg, Seward, Sitka, Valdez, and Wrangell). (2) Suitability modeling results for the ten study areas. Results between each study area should not be compared relative to outputs of another study area, as each study area was run as a discrete model and thus the outputs of the analysis cannot be compared between study areas. (3) Precision siting outputs from the Aquaculture Opportunity Atlas for the State of Alaska. Clusters were identified within each of the ten study areas (Cordova, Craig, Juneau, Ketchikan, Kodiak, Petersburg, Seward, Sitka, Valdez, and Wrangell) for two scenarios: intertidal and subtidal mariculture. (4) Final AOA Options for the state of Alaska based on selection of highest ranking polygons from the precision siting outputs for each of the ten study areas for both scenarios.

The table HII for Children from Low-Income Households by County is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 3219 rows across 64 variables.

Shapefiles of the Aquaculture Opportunity Area (AOA) study developed during 2021 for the Gulf of Mexico. Included in this dataset are: (1) Study areas in the Gulf of Mexico developed based on depth, jurisdictional boundaries, and Level III biogeographical breaks. (2) Compiled observations of Harmful Algal Blooms (Karina brevis) from 2000 to 2018 in the Gulf of Mexico and eastern Florida. (3) Suitability modeling results for the West, Central, East, and Southeast Gulf of Mexico study areas are presented as categories (“Unsuitable,†“Low,†“Moderate,†“High†) based on ocean use and conservation concerns, including: national security, natural and cultural resources, industry, navigation, transportation, aquaculture, and fishing. (4) High-High clusters (HH) identified as the most suitable areas from LISA (Local Index of Spatial Association) analysis. (5) Refined HH clusters that could accommodate at least one 500-acre AOA option. (6) Highest ranking options for each of the refined HH clusters representing a 500- to 2000-acre area between 50 to 150 meters depth that has relatively high suitability for generalized marine aquaculture based on a within cluster model evaluating logistics, vessel traffic, commercial fishing, and oceanography data. (7) Location and areal extent of options identified for each study region meeting a dispersion rule (greater than 30 nautical miles distance between locations).

OLS regression coefficients and standard errors from regressions of census tract level adult household income ranks (Opportunity atlas) and diabetes diagnoses (500 cities) on two census tract HOLC classifications.

The table Neighborhood Characteristics by Census Tract is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 74044 rows across 38 variables.

Geospatial data representing the Aquaculture Opportunity Area (AOA) study developed during 2024 for the State of Alaska. Included in this dataset are: (1) Study areas in State of Alaska waters developed based on maximum sea ice coverage, distance from coastal populated places, existing and active aquaculture sites, and the Alaska state water boundary. Ten distinct study areas were identified (Cordova, Craig, Juneau, Ketchikan, Kodiak, Petersburg, Seward, Sitka, Valdez, and Wrangell). (2) Suitability modeling results for the ten study areas. Results between each study area should not be compared relative to outputs of another study area, as each study area was run as a discrete model and thus the outputs of the analysis cannot be compared between study areas. (3) Precision siting outputs from the Aquaculture Opportunity Atlas for the State of Alaska. Clusters were identified within each of the ten study areas (Cordova, Craig, Juneau, Ketchikan, Kodiak, Petersburg, Seward, Sitka, Valdez, and Wrangell) for two scenarios: intertidal and subtidal mariculture. (4) Final AOA Options for the state of Alaska based on selection of highest ranking polygons from the precision siting outputs for each of the ten study areas for both scenarios.

This dataset contains replication files for "A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples" by Raj Chetty and John Friedman. For more information, see https://opportunityinsights.org/paper/differential-privacy/. A summary of the related publication follows. Releasing statistics based on small samples – such as estimates of social mobility by Census tract, as in the Opportunity Atlas – is very valuable for policy but can potentially create privacy risks by unintentionally disclosing information about specific individuals. To mitigate such risks, we worked with researchers at the Harvard Privacy Tools Project and Census Bureau staff to develop practical methods of reducing the risks of privacy loss when releasing such data. This paper describes the methods that we developed, which can be applied to disclose any statistic of interest that is estimated using a sample with a small number of observations. We focus on the case where the dataset can be broken into many groups (“cells”) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic’s maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the release of statistics in arbitrarily small samples by adding sufficient noise to the estimates to protect privacy. Although our method does not offer a formal privacy guarantee, it generally outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by Census tract in the Opportunity Atlas. We also provide a step-by-step guide and illustrative Stata code to implement our approach.

Building on insights from the differential privacy literature, we develop a simple noise-infusion method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on small samples. Although our method does not offer a formal privacy guarantee, it outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by census tract in the Opportunity Atlas. We provide a step-by-step guide and code to implement our approach.

The National Zoning Atlas is a collaborative project digitizing, demystifying, & democratizing ~30,000 U.S. zoning codes. It was founded by Cornell University professor Sara Bronin and has involved over 300 zoning and geospatial analysts. WHAT: Zoning laws, adopted by perhaps 30,000 local governments across the country, dictate much of what can be built in the United States. The National Zoning Atlas is helping us better understand these sometimes-opaque but incredibly influential laws by depicting their key attributes in an online, user-friendly map. As a federated academic enterprise, the National Zoning Atlas encompasses several disciplines. It is a legal research project, as it delves deeply into the regulatory frameworks that dictate so much of the way we use our land. It is a data science project, and it deploys novel systems of collecting, analyzing, and displaying geospatial and regulatory data. It is a digital humanities project, innovative in its methodology and having the potential to unlock new research on the central instrument that shapes our urban built environment, social relations and hierarchies, and geographies of opportunity. It is a social science project that will improve our understanding of our politics, society, and economy - and expand our collective ability to reimagine future, alternative, and reparative trajectories. And it is a computer science project, deploying machine learning and natural language processing to expand our understanding of how algorithms can read complex regulatory texts. WHY: Zoning laws have direct impacts on housing availability, transportation systems, the environment, economic opportunity, educational opportunity, and our food supply. Despite codes’ importance, ordinary people can’t make heads or tails of them. They are too complex and inscrutable. The National Zoning Atlas will help people better understand zoning, which would in turn broaden participation in land use decisions, identify opportunities for zoning reform, and narrow a wide information gap that currently favors land speculators, institutional investors, and homeowners over socioeconomically disadvantaged groups. It would also enable comparisons across jurisdictions, illuminate regional and statewide trends, and strengthen national planning for housing production, transportation infrastructure, and climate response. To understand the kinds of things a zoning atlas can show, review this research paper documenting the findings of the Connecticut Zoning Atlas (the first statewide atlas) and this research paper in HUD Cityscape describing the motivations of the project. HOW: To date, this project has relied on manual reviews of thousands of pages of zoning code texts and their corresponding maps. A how-to guide for these reviews is available for free download. The project is also using grant funding from the National Science Foundation and the U.S. Department of Housing and Community Development Community Block Grant Disaster Recovery Program to automate this process so we can more quickly map the 30,000 localities estimated to use zoning. Our basic operating principles are: Deploy data for the public good ​Evaluate and adapt methods and approaches ​Collaborate broadly ​Cultivate up-and-coming talent ​Assume that this is a solvable problem, worth solving ​WHO: Project participants overwhelmingly include representatives of academic institutions, nonprofits, and government agencies, with students providing important support. In addition, private partners may participate on specific geographic teams or provide data. Because this project aims to expand knowledge for the public good, its resulting online atlases will remain free to view regardless of who pitches in to create them.

The table All Outcomes by County, Race, Gender and Parental Income3 is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 3219 rows across 2505 variables.

This layer contains American Community Survey (ACS) 2016-2020 5-year estimates in order to determine if a Census tract is considered an opportunity zone/low income community. According to Tax Code Section 45D(e), low income Census Tracts are based on the following criteria:The poverty rate is at least 20 percent, ORThe median family income does not exceed 80 percent of statewide median family income or, if in a metropolitan area, the greater of 80 percent statewide median family income or 80 percent of metropolitan area median family incomeThe layer is visualized to show if a tract meets these criteria, and the pop-up provides poverty figures as well as tract, metropolitan area, and state level figures for median family income. When a tract meets the above criteria, it may also qualify for grants or findings such Opportunity Zones. These zones are designed to encourage economic development and job creation in communities throughout the country by providing tax benefits to investors who invest eligible capital into these communities. Another way this layer can be used is to gain funding through the Inflation Reduction Act of 2022. The data was downloaded on October 5, 2022 from the US Census Bureau via data.census.gov:Table B17020: Poverty Status in the Past 12 Months - TractsTable B19113: Median Family Income in the Past 12 Months (in 2020 inflation-adjusted dollars) - Tracts, Metropolitan area, StateVintage of the data: 2016-2020 American Community SurveyBoundaries used for analysis: TIGER 2020 Tract, Metro, and State Boundaries with large hydrography removed from tractsData was processed within ArcGIS Pro 3.0.2 using ModelBuilder to spatially join the metropolitan and state geographies to tracts.To see the same qualification on 2010-based Census tracts, there is also an older 2012-2016 version of the layer.

The table All Outcomes at the National Level by Race, Gender is part of the dataset The Opportunity Atlas dataset, available at https://redivis.com/datasets/eh59-bemd0fw98. It contains 100 rows across 3914 variables.