Efficient allocation of law enforcement resources using predictive police patrolling
arXiv:1811.12880
Abstract
Efficient allocation of scarce law enforcement resources is a hard problem to tackle. In a previous study (forthcoming Barreras et.al (2019)) it has been shown that a simplified version of the self-exciting point process explained in Mohler et.al (2011), performs better predicting crime in the city of Bogotá - Colombia, than other standard hotspot models such as plain KDE or ellipses models. This paper fully implements the Mohler et.al (2011) model in the city of Bogotá and explains its technological deployment for the city as a tool for the efficient allocation of police resources.
Presented at NIPS 2018 Workshop on Machine Learning for the Developing World