Log-Gaussian Cox Processes on Networks for Crime Hotspot Analysis

Music City Center 

Current spatial point process modeling of crime data primarily relies on Euclidean distances, while criminal incidents such as robbery or vehicle crime only occur on the streets of cities. This study utilizes the recently proposed Log-Gaussian Cox Processes (LGCPs) on metric graphs to analyze crime data from the UK. The purpose is to study the effect of explanatory variables such as population density, education levels, and socioeconomic factors and to find hotspots of crime. We also compare the LGCPs on the networks with LGCPs defined in Euclidean space to investigate the effect of taking the network structure into account.

Metric Graphs

Log-Gaussian Cox Processes

Crime Hotspot Analysis

Network-Based Modeling 

Section on Bayesian Statistical Science