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Learning about Spatial and Temporal Proximity using Tree-Based Methods

Author

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  • Levin Ines

    (Department of Political Science, University of California at Irvine, 3151 Social Science Plaza, Irvine, 92697, CA, USA)

Abstract

Learning about the relationship between distance to landmarks and events and phenomena of interest is a multi-faceted problem, as it may require taking into account multiple dimensions, including: spatial position of landmarks, timing of events taking place over time, and attributes of occurrences and locations. Here I show that tree-based methods are well suited for the study of these questions as they allow exploring the relationship between proximity metrics and outcomes of interest in a non-parametric and data-driven manner. I illustrate the usefulness of tree-based methods vis-à-vis conventional regression methods by examining the association between: (i) distance to border crossings along the US-Mexico border and support for immigration reform, and (ii) distance to mass shootings and support for gun control.

Suggested Citation

  • Levin Ines, 2022. "Learning about Spatial and Temporal Proximity using Tree-Based Methods," Statistics, Politics and Policy, De Gruyter, vol. 13(1), pages 73-95, March.
    • Handle: RePEc:bpj:statpp:v:13:y:2022:i:1:p:73-95:n:1
    DOI: 10.1515/spp-2021-0031
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