Optimal Intervention for Self-triggering Spatial Networks with Application to Urban Crime Analytics

TL;DR

This paper develops an optimal intervention model for self-triggering spatial networks, extending Hawkes process models to spatiotemporal data, and demonstrates its effectiveness in reducing urban crime through simulations and real-world data analysis.

Contribution

It introduces a novel spatiotemporal Hawkes network intervention model and applies it to urban crime data for strategic policing.

Findings

Intervention reduces network activity more effectively than heuristics.

Model successfully identifies critical neighborhoods for crime mitigation.

Demonstrates practical application in urban crime prevention.

Abstract

In many network systems, events at one node trigger further activity at other nodes, e.g., social media users reacting to each other's posts or the clustering of criminal activity in urban environments. These systems are typically referred to as self-exciting networks. In such systems, targeted intervention at critical nodes can be an effective strategy for mitigating undesirable consequences such as further propagation of criminal activity or the spreading of misinformation on social media. In our work, we develop an optimal network intervention model to explore how targeted interventions at critical nodes can mitigate cascading effects throughout a Spatiotemporal Hawkes network. Similar models have been studied previously in the literature in purely temporal Hawkes networks, but in our work, we extend them to a spatiotemporal setup and demonstrate the efficacy of our methods by comparing the post-intervention reduction in intensity to other heuristic strategies in simulated networks. Subsequently, we use our method on crime data from the LA police department database to find neighborhoods for strategic intervention to demonstrate an application in predictive policing.