Learning about Spatial and Temporal Proximity using Tree-Based Methods

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

Statistics, Politics and Policy, 2022, vol. 13, issue 1, 73-95

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.

Keywords: spatial proximity; distance measures; machine learning; decision trees; ensemble methods; immigration reform; gun control (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/spp-2021-0031

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