Integrating Game Theory and Data Mining for Dynamic Distribution of Police to Combat Crime

This paper proposes a framework that provides a strategy for police to allocate resources to tackle crime, by integrating data mining models for dynamic crime prediction with a game theoretical approach to recognize the adversarial nature of the problem. The proposed framework is applied to a real case study from Santiago (Chile), and compared to other strategies involving game theory or data milling alone. The hybrid approach is demonstrated to lead to improved payoffs for the police and reduced payoffs for the criminals. A robustness analysis explores how accuracy of the data mining models affects the outcomes of the game, showing that the proposed approach can absorb significant forecasting errors while still producing superior outcomes for the police.