2010 United States Census Tract Community Type Classification and Neighborhood Social and Economic Environment Score for 2000 and 2010, from the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network (ICPSR 38645)
Version Date: Mar 7, 2023 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
Leslie A. McClure, Drexel University;
Annemarie G. Hirsch, Geisinger Medical Center;
Brian S. Schwartz, Johns Hopkins University;
Lorna E. Thorpe, New York University. Grossman School of Medicine;
Brian Elbel, New York University. Grossman School of Medicine;
April Carson, University of Alabama at Birmingham;
D. Leann Long, University of Alabama at Birmingham
https://doi.org/10.3886/ICPSR38645.v1
Version V1
Summary View help for Summary
This dataset contains two measures designed to be used in tandem to characterize United States census tracts, originally developed for use in stratified analyses of the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network. The first measure is a 2010 tract-level community type categorization based on a modification of Rural-Urban Commuting Area (RUCA) Codes that incorporates census-designated urban areas and tract land area, with five categories: higher density urban, lower density urban, suburban/small town, rural, and undesignated (McAlexander, et al., 2022). The second measure is a neighborhood social and economic environment (NSEE) score, a community-type stratified z-score sum of 6 US census-derived variables, with sums scaled between 0 and 100, computed for the year 2000 and 2010. A tract with a higher NSEE z-score sum indicates more socioeconomic disadvantage compared to a tract with a lower z-score sum. Analysts should not compare NSEE scores across LEAD community types, as values have been computed and scaled within community type.
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Subject Terms View help for Subject Terms
Geographic Coverage View help for Geographic Coverage
Smallest Geographic Unit View help for Smallest Geographic Unit
Census Tract
Distributor(s) View help for Distributor(s)
Time Period(s) View help for Time Period(s)
Date of Collection View help for Date of Collection
Study Purpose View help for Study Purpose
The goal of the LEAD Network was to elucidate the role community-level factors play in observed geographic differences in diabetes incidence and prevalence across the US.
Study Design View help for Study Design
Data used to create the community type measure included Census tract land area from the 2010 Decennial Census (SF1 100% Data, G001 geographic tables), and 2010 Rural-Urban Commuting Area codes from the United States Department of Agriculture (version last updated 7/3/2019).
Data for the 6 Census variables used to compute the Neighborhood Social and Economic Environment Measure were downloaded from the SF3 file of the 2000 Decennial Census and the 2006-2010 American Community Survey (ACS), for the 2000 and 2010 measures respectively. The year 2000 tract data was converted to the 2010 tract boundaries using the interpolation tool from Brown University's Longitudinal Tract Data Base (LTDB), as described in Logan, et al., "Interpolating U.S. Decennial Census Tract Data from as Early as 1970 to 2010: A Longitudinal Tract Database" (2014).
Data for both measures were then mapped to align with the 2016 TIGER Line version of the 2010 tract boundaries from the US Census Bureau's National Sub-State Geography Geodatabase.
Universe View help for Universe
Census tracts in the contiguous United States.
Unit(s) of Observation View help for Unit(s) of Observation
Data Type(s) View help for Data Type(s)
Description of Variables View help for Description of Variables
This dataset contains four main variables: a geographic ID for census tracts, a community type classification, and the two Neighborhood Social and Economic Environment (NSEE) variables. The NSEE measure is a community-type stratified, z-score sum of 6 US Census-derived variables, with sums scaled between 0 and 100.
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The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.