Self-exciting point process modelling of crimes on linear networks

Abstract: Although there are recent developments in analysing first and second-order characteristics of point processes on networks, there are very few attempts to introduce models for network data. Motivated by the analysis of crime data in Bucaramanga (Colombia), we propose a spatio-temporal Hawkes point process model adapted to events living on linear networks. We first consider a non-parametric modelling strategy, for which we follow a non-parametric estimation of both the background and the triggering components. Then we consider a semi-parametric version, including a parametric estimation of the background based on covariates. Our network model outperforms a planar version, improving the fitting of the self-exciting point process model, and can be easily adapted to multi-type processes.

machine learningprobabilitystatistics theory

Audience: researchers in the discipline

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk).