Opportunistic Crime Security Games: Assisting Police to Control Urban Crime Using Real World Data


Crime in urban areas plagues every city in all countries. A notable characteristic of urban crime, distinct from organized terrorist attacks, is that most urban crimes are opportunistic in nature, i.e., criminals do not plan their attacks in detail, rather they seek opportunities for committing crime and are agile in their execution of the crime. In order to deter such crimes, police officers conduct patrols with the aim of preventing crime. However, by observing on the spot the actual presence of patrol units, the criminals can adapt their strategy by seeking crime opportunity in less effectively patrolled location. The problem of where and how much to patrol is therefore important. My thesis focuses on addressing such opportunistic crime by introducing a new gametheoretic framework and algorithms. I first introduce the Opportunistic Security Game (OSG), a computational framework to recommend deployment strategies for defenders to control opportunistic crimes. I propose a new exact algorithm EOSG to optimize defender strategies given our opportunistic adversaries. Then I develop a fast heuristic algorithm to solve large-scale OSG problems, exploiting a compact representation. The next contribution in my thesis is a Dynamic Bayesian Network (DBN) to learn the OSG model from real-world criminal activity. Standard Algorithm such as EM can be applied to learn the parameters. Also, I propose a sequence of modifications that allows for a compact representation of the model resulting in better learning accuracy and increased speed of learning of the EM algorithm. Finally, I propose a game abstraction framework that can handle opportunistic crimes in large-scale urban areas. I propose a planning algorithm that recommends a mixed strategy against opportunistic criminals in this abstraction framework. As part of our collaboration with local police departments, we apply our model in two large scale urban problems: USC campus and the city of Nashville. Our approach provides high prediction accuracy in the real datasets; furthermore, we project significant crime rate reduction using our planning strategy compared to current police strategy
See also: 2016