@article{ref1,
title="Learning about spatial and temporal proximity using tree-based methods",
journal="Statistics, politics and policy",
year="2022",
author="Levin, Ines",
volume="13",
number="1",
pages="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.<p /> <p>Language: en</p>",
language="en",
issn="2194-6299",
doi="10.1515/spp-2021-0031",
url="http://dx.doi.org/10.1515/spp-2021-0031"
}