Atlanta Gun Violence Modeling via Nonstationary Spatio-temporal Point Processes

TL;DR

This paper introduces a neural network-based non-stationary spatiotemporal point process model to better understand and predict the complex spread of gun violence in Atlanta, surpassing traditional stationary models in performance and interpretability.

Contribution

It develops a novel non-stationary kernel using neural networks for modeling gun violence, capturing complex dynamics more effectively than existing stationary Hawkes process models.

Findings

Enhanced predictive accuracy over stationary models

Insights into spatiotemporal propagation patterns

Effective modeling of non-homogeneous influence spread

Abstract

Analysis of gun violence in the United States has utilized various models based on spatiotemporal point processes. Previous studies have identified a contagion effect in gun violence, characterized by bursts of diffusion across urban environments, which can be effectively represented using the self-excitatory spatiotemporal Hawkes process. The Hawkes process and its variants have been successful in modeling self-excitatory events, including earthquakes, disease outbreaks, financial market movements, neural activity, and the viral spread of memes on social networks. However, existing Hawkes models applied to gun violence often rely on simplistic stationary kernels, which fail to account for the complex, non-homogeneous spread of influence and impact over space and time. To address this limitation, we adopt a non-stationary spatiotemporal point process model that incorporates a neural network-based kernel to better represent the varied correlations among events of gun violence. Our study analyzes a comprehensive dataset of approximately 16,000 gunshot events in the Atlanta metropolitan area from 2021 to 2023. The cornerstone of our approach is the innovative non-stationary kernel, designed to enhance the model's expressiveness while preserving its interpretability. This approach not only demonstrates strong predictive performance but also provides insights into the spatiotemporal dynamics of gun violence and its propagation within urban settings.