<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Schroeder JP</submitter><funding>Eunice Kennedy Shriver National Institute of Child Health and Human Development</funding><funding>NICHD NIH HHS</funding><pagination>131-154</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8323948</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>9(1)</volume><pubmed_abstract>Microdata from U.S. decennial censuses and the American Community Survey are a key resource for social science and policy analysis, enabling researchers to investigate relationships among all reported characteristics for individual respondents and their households. To protect privacy, the Census Bureau restricts the detail of geographic information in public use microdata, and this complicates how researchers can investigate and account for variations across levels of urbanization when analyzing microdata. One option is to focus on metropolitan status, which can be determined exactly for most microdata records and approximated for others, but a binary metro/nonmetro classification is still coarse and limited on its own, emphasizing one aspect of rural-urban variation and discounting others. To address these issues, we compute two continuous indices for public use microdata-average tract density and average metro/micro-area population-using population-weighted geometric means. We show how these indices correspond to two key dimensions of urbanization-concentration and size-and we demonstrate their utility through an examination of disparities in poverty throughout the rural-urban universe. Poverty rates vary across settlement types in nonlinear ways: rates are lowest in moderately dense parts of major metro areas, and rates are higher in both low- and high-density areas, as well as in smaller commuting systems. Using the two indices also reveals that correlations between poverty and demographic characteristics vary considerably across settlement types. Both indices are now available for recent census microdata via IPUMS USA (https://usa.ipums.org).</pubmed_abstract><journal>Spatial demography</journal><pubmed_title>Across the Rural-Urban Universe: Two Continuous Indices of Urbanization for U.S. Census Microdata.</pubmed_title><pmcid>PMC8323948</pmcid><funding_grant_id>P2C HD041023</funding_grant_id><funding_grant_id>R01HD043392</funding_grant_id><funding_grant_id>R01 HD043392</funding_grant_id><funding_grant_id>R01 HD057929</funding_grant_id><funding_grant_id>R24 HD041023</funding_grant_id><pubmed_authors>Pacas JD</pubmed_authors><pubmed_authors>Schroeder JP</pubmed_authors></additional><is_claimable>false</is_claimable><name>Across the Rural-Urban Universe: Two Continuous Indices of Urbanization for U.S. Census Microdata.</name><description>Microdata from U.S. decennial censuses and the American Community Survey are a key resource for social science and policy analysis, enabling researchers to investigate relationships among all reported characteristics for individual respondents and their households. To protect privacy, the Census Bureau restricts the detail of geographic information in public use microdata, and this complicates how researchers can investigate and account for variations across levels of urbanization when analyzing microdata. One option is to focus on metropolitan status, which can be determined exactly for most microdata records and approximated for others, but a binary metro/nonmetro classification is still coarse and limited on its own, emphasizing one aspect of rural-urban variation and discounting others. To address these issues, we compute two continuous indices for public use microdata-average tract density and average metro/micro-area population-using population-weighted geometric means. We show how these indices correspond to two key dimensions of urbanization-concentration and size-and we demonstrate their utility through an examination of disparities in poverty throughout the rural-urban universe. Poverty rates vary across settlement types in nonlinear ways: rates are lowest in moderately dense parts of major metro areas, and rates are higher in both low- and high-density areas, as well as in smaller commuting systems. Using the two indices also reveals that correlations between poverty and demographic characteristics vary considerably across settlement types. Both indices are now available for recent census microdata via IPUMS USA (https://usa.ipums.org).</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Apr</publication><modification>2025-04-04T22:33:58.265Z</modification><creation>2025-02-19T03:24:13.831Z</creation></dates><accession>S-EPMC8323948</accession><cross_references><pubmed>34337141</pubmed><doi>10.1007/s40980-021-00081-y</doi></cross_references></HashMap>