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A Data-Driven Algorithm to Redefine the U.S. Rural Landscape: Affinity Propagation as a Mixed-Data/Mixed-Method Tool

Author

Listed:
  • Benjamin W. Heumann
  • Marcello Graziano
  • Maurizio Fiaschetti

Abstract

This study demonstrates the application of affinity propagation as a data-driven approach to identifying and mapping typologies of place along the urban-rural continuum. The authors characterize Zip Code Tabulation Areas using demographic, economic, land cover, and accessibility to transportation infrastructure, which results in 22 clusters, 15 of which have a major rural component. The spatial pattern of these clusters varies, reflecting the heterogeneity in U.S. rurality. Rural is not a single concept that can be simply defined by population density. By comparing three economic indicators before and after the global financial crisis of 2007 to 2012, the authors find that the degree of economic recovery is captured by rural typologies. They compare both the methodological results and analysis of socioeconomic resilience to two of the most used threshold-based regional typologies, one developed by the U.S. Department of Agriculture Economic Research Service and one used by the American Communities Project.

Suggested Citation

  • Benjamin W. Heumann & Marcello Graziano & Maurizio Fiaschetti, 2022. "A Data-Driven Algorithm to Redefine the U.S. Rural Landscape: Affinity Propagation as a Mixed-Data/Mixed-Method Tool," Economic Development Quarterly, , vol. 36(3), pages 294-316, August.
    • Handle: RePEc:sae:ecdequ:v:36:y:2022:i:3:p:294-316
    DOI: 10.1177/08912424221103556
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