Learning about Spatial and Temporal Proximity using Tree-Based Methods
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-\`a-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.
Cite
@article{arxiv.2409.06046,
title = {Learning about Spatial and Temporal Proximity using Tree-Based Methods},
author = {Ines Levin},
journal= {arXiv preprint arXiv:2409.06046},
year = {2024}
}