  To obtain operational insights regarding the crime of burglary in London we
consider the estimation of effects of covariates on the intensity of spatial
point patterns. By taking into account localised properties of criminal
behaviour, we propose a spatial extension to model-based clustering methods
from the mixture modelling literature. The proposed Bayesian model is a finite
mixture of Poisson generalised linear models such that each location is
probabilistically assigned to one of the clusters. Each cluster is
characterised by the regression coefficients which we subsequently use to
interpret the localised effects of the covariates. Using a blocking structure
of the study region, our approach allows specifying spatial dependence between
nearby locations. We estimate the proposed model using Markov Chain Monte Carlo
methods and provide a Python implementation.
