%0 Journal Article
%T Learning about spatial and temporal proximity using tree-based methods
%J Statistics, politics and policy
%D 2022
%A Levin, Ines
%V 13
%N 1
%P 73-95
%X 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>
%G en
%I Walter De Gruyter
%@ 2194-6299
%U http://dx.doi.org/10.1515/spp-2021-0031