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Across the Rural-Urban Universe: Two Continuous Indices of Urbanization for U.S. Census Microdata.

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Summary
This summary is machine-generated.

New indices for average tract density and metro/micro-area population improve analysis of U.S. census microdata. These measures reveal complex poverty variations across rural-urban settlement types, aiding social science research.

Keywords:
Census microdataPopulation densityPovertyUnited StatesUrban/rural

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Area of Science:

  • Social Science
  • Demography
  • Urban Studies

Background:

  • U.S. decennial census and American Community Survey microdata are vital for social science and policy analysis.
  • Privacy restrictions on geographic detail in microdata limit analysis of urbanization variations.
  • Existing binary metro/nonmetro classifications are coarse and do not capture the full spectrum of rural-urban differences.

Purpose of the Study:

  • To develop continuous indices for analyzing urbanization dimensions in public use microdata.
  • To assess the utility of these indices in understanding poverty disparities across the rural-urban continuum.
  • To provide researchers with enhanced tools for accounting for settlement variations in microdata analysis.

Main Methods:

  • Computed two continuous indices: average tract density and average metro/micro-area population, using population-weighted geometric means.
  • Applied these indices to U.S. census microdata to examine poverty variations.
  • Demonstrated how indices capture concentration and size dimensions of urbanization.

Main Results:

  • Poverty rates exhibit nonlinear variation across settlement types, being lowest in moderately dense metro areas and higher in very low- and high-density areas, and smaller commuting systems.
  • Correlations between poverty and demographic characteristics differ significantly across settlement types.
  • The new indices provide a more nuanced understanding of rural-urban poverty gradients.

Conclusions:

  • The developed continuous indices offer a significant improvement over binary classifications for analyzing urbanization in microdata.
  • These indices reveal complex, nonlinear relationships between settlement characteristics and poverty.
  • The indices are available for recent census microdata via IPUMS USA, facilitating further research.