Analyzing Car Thefts and Recoveries with Connections to Modeling Origin-Destination Point Patterns

TL;DR

This paper develops models to analyze and predict car theft and recovery locations using origin-destination point pattern analysis, addressing partial recovery data and applying methods to datasets from Mexico and Brazil.

Contribution

It introduces novel modeling approaches for paired point patterns with partial observations, linking theft and recovery locations in a spatial context.

Findings

Effective models for theft and recovery location prediction.

Insights into the dependence between theft and recovery points.

Application of models to real-world datasets from Mexico and Brazil.

Abstract

For a given region, we have a dataset composed of car theft locations along with a linked dataset of recovery locations which, due to partial recovery, is a relatively small subset of the set of theft locations. For an investigator seeking to understand the behavior of car thefts and recoveries in the region, several questions are addressed. Viewing the set of theft locations as a point pattern, can we propose useful models to explain the pattern? What types of predictive models can be built to learn about recovery location given theft location? Can the dependence between theft locations and recovery locations be formalized? Can the flow between theft sites and recovery sites be captured? Origin-destination modeling offers a natural framework for such problems. However, here the data is not for areal units but rather is a pair of point patterns, with the recovery point pattern only partially observed. We offer modeling approaches for investigating the questions above and apply the approaches to two datasets. One is small from the state of Neza in Mexico with areal covariate information regarding population features and crime type. A second, much larger one, is from Belo Horizonte in Brazil but lacks covariates.