Generalized Evolutionary Point Processes: Model Specifications and Model Comparison

TL;DR

This paper introduces a generalized class of evolutionary point process models, extending nonlinear Hawkes processes, with Bayesian inference and model comparison strategies, demonstrated through simulations and real-world violent crime data analysis.

Contribution

It develops a Bayesian framework for generalized evolutionary point processes, including model comparison methods and extensions of the log Gaussian Cox process, with applications to real data.

Findings

Models can distinguish excitation and inhibition in point patterns

Evolutionary log Gaussian Cox process outperforms simpler models

Real data shows mild self-excitation after accounting for seasonality

Abstract

Generalized evolutionary point processes offer a class of point process models that allows for either excitation or inhibition based upon the history of the process. In this regard, we propose modeling which comprises generalization of the nonlinear Hawkes process. Working within a Bayesian framework, model fitting is implemented through Markov chain Monte Carlo. This entails discussion of computation of the likelihood for such point patterns. Furthermore, for this class of models, we discuss strategies for model comparison. Using simulation, we illustrate how well we can distinguish these models from point pattern specifications with conditionally independent event times, e.g., Poisson processes. Specifically, we demonstrate that these models can correctly identify true relationships (i.e., excitation or inhibition/control). Then, we consider a novel extension of the log Gaussian Cox process that incorporates evolutionary behavior and illustrate that our model comparison approach prefers the evolutionary log Gaussian Cox process compared to simpler models. We also examine a real dataset consisting of violent crime events from the 11th police district in Chicago from the year 2018. This data exhibits strong daily seasonality and changes across the year. After we account for these data attributes, we find significant but mild self-excitation, implying that event occurrence increases the intensity of future events.