Estimation of space-time self-exciting point process models using multi-dimensional Gaussian-type exponent approximation

Space-time self-exciting point process models are introduced to capture the clustering features in crime datasets. It does particularly well in modeling social network datasets, crime and security datasets, financial datasets, and seismic datasets. However, there has been limited analysis of large crime datasets using the space-time self-exciting point process models due to a lack of flexibility in the estimation of the conditional intensity function and computational challenges associated with large datasets. To explore the applicability of these models for crime data, we propose a multi-dimensional Gaussian-type exponent approximation method. This method addresses computational difficulties associated with large datasets and enables flexible estimation of the conditional intensity function. We evaluate the proposed method through simulations and apply it to study the space-time patterns of burglaries in Chicago, Illinois, United States. The results demonstrate that the proposed method is flexible, has overcome computational difficulties, and reveals a strong clustering phenomenon in the burglary data.