Space and circular time log Gaussian Cox processes with application to crime event data

TL;DR

This paper develops Bayesian hierarchical models for space-time crime data, treating time as circular and comparing Poisson and Cox processes with different covariance structures, applied to San Francisco crime data.

Contribution

It introduces models that incorporate circular time and classify crime types, comparing covariance structures within a Bayesian framework for crime event analysis.

Findings

Cox process models outperform Poisson models in fit.

Nonseparable covariance functions better capture data dependencies.

Models reveal spatial and temporal crime patterns in San Francisco.

Abstract

We view the locations and times of a collection of crime events as a space-time point pattern. So, with either a nonhomogeneous Poisson process or with a more general Cox process, we need to specify a space-time intensity. For the latter, we need a \emph{random} intensity which we model as a realization of a spatio-temporal log Gaussian process. Importantly, we view time as circular not linear, necessitating valid separable and nonseparable covariance functions over a bounded spatial region crossed with circular time. In addition, crimes are classified by crime type. Furthermore, each crime event is recorded by day of the year which we convert to day of the week marks. The contribution here is to develop models to accommodate such data. Our specifications take the form of hierarchical models which we fit within a Bayesian framework. In this regard, we consider model comparison between the nonhomogeneous Poisson process and the log Gaussian Cox process. We also compare separable vs. nonseparable covariance specifications. Our motivating dataset is a collection of crime events for the city of San Francisco during the year 2012. We have location, hour, day of the year, and crime type for each event. We investigate models to enhance our understanding of the set of incidences.