UCSC-SOE-10-09: Modeling and Anomaly Detection for Event Occurrences Following an Inhomogeneous Spatio-Temporal Poisson Process

Waley Liang, Jacob Colvin, Bruno Sansó, and Herbert Lee
02/19/2010 09:00 AM
Applied Mathematics & Statistics
This paper presents a model that describes spatial variations of the intensity of events that occur at random geographical locations. An inhomogeneous Poisson process is used to model the intensity over a spatial region with multiplicative spatial and temporal covariate effects. Dynamic temporal effects are incorporated into the model allowing changes in the intensity structure over time. Additionally, anomaly detection in the event rates is developed based on exceedance probabilities. The methods are demonstrated on data of major crimes in Cincinnati during 2006.