Citation

Levin I. Stat. Politics Policy 2022; 13(1): 73-95.

Copyright

(Copyright © 2022, Walter De Gruyter)

DOI

10.1515/spp-2021-0031

PMID

unavailable

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.

Language: en

Keywords

decision trees; distance measures; ensemble methods; gun control; immigration reform; machine learning; spatial proximity