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A Data-Driven Algorithm to Redefine the U.S. Rural Landscape: Affinity Propagation as a Mixed-Data/Mixed-Method Tool
Resource type
Authors/contributors
- Heumann, Benjamin W. (Author)
- Graziano, Marcello (Author)
- Fiaschetti, Maurizio (Author)
Title
A Data-Driven Algorithm to Redefine the U.S. Rural Landscape: Affinity Propagation as a Mixed-Data/Mixed-Method Tool
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.
Publication
Economic Development Quarterly
Publisher
SAGE Publications Inc
Date
2022-06-03
Pages
08912424221103556
Journal Abbr
Economic Development Quarterly
Citation Key
heumannDataDrivenAlgorithmRedefine2022
Accessed
6/16/22, 1:28 PM
ISSN
0891-2424
Short Title
A Data-Driven Algorithm to Redefine the U.S. Rural Landscape
Language
en
Library Catalog
SAGE Journals
Extra
0 citations (Crossref) [2023-10-31]
Citation
Heumann, B. W., Graziano, M., & Fiaschetti, M. (2022). A Data-Driven Algorithm to Redefine the U.S. Rural Landscape: Affinity Propagation as a Mixed-Data/Mixed-Method Tool. Economic Development Quarterly, 08912424221103556. https://doi.org/10.1177/08912424221103556
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