Abstract
Problem definition: We consider the case of prescriptive policing, that is, the data-driven assignment of police cars to different areas of a city. We analyze key problems with respect to prediction, optimization, and evaluation as well as trade-offs between different quality measures and crime types. Academic/practical relevance: Data-driven prescriptive analytics is gaining substantial attention in operations management research, and effective policing is at the core of the operations of almost every city in the world. Given the vast amounts of data increasingly collected within smart city initiatives and the growing safety challenges faced by urban centers worldwide, our work provides novel insights on the development and evaluation of prescriptive analytics applications in an urban context. Methodology: We conduct a computational study using crime and auxiliary data on the city of San Francisco. We analyze both strong and weak prediction methods along with two optimization formulations representing the deterrence and response time impact of police vehicle allocations. We analyze trade-offs between these effects and between different crime types. Results: We find that even weaker prediction methods can produce Pareto-efficient outcomes with respect to deterrence and response time. We identify three different archetypes of combinations of prediction methods and optimization objectives that constitute the Pareto frontier among the configurations we analyze. Furthermore, optimizing for multiple crime types biases allocations in a way that generally decreases single-type performance along one outcome metric but can improve it along the other. Managerial implications: Although optimization integrating all relevant crime types is theoretically possible, it is practically challenging because each crime type requires a collectively consistent weight. We present a framework combining prediction and optimization for a subset of key crime types with exploring the impact on the remaining types to support implementation of operations-focused smart city solutions in practice.
Highlights
In today’s data-driven world, prescriptive analytics has been gaining traction in both research and practice (Bertsimas and Kallus 2019)
Given the vast amounts of data increasingly collected within smart city initiatives and the growing safety challenges faced by urban centers worldwide, our work provides novel insights on the development and evaluation of prescriptive analytics applications in an urban context
Combined approaches that leverage the prediction method with the best prediction quality (PQ) generally lead to the best decision quality (DQ) according to the optimization objective, this is not necessarily always the case
Summary
In today’s data-driven world, prescriptive analytics has been gaining traction in both research and practice (Bertsimas and Kallus 2019). Recent examples can be found in a variety of business domains, such as optimizing customer segmentation and targeting based on customer data (Nair et al 2017) or creating decision support tools that optimize maintenance assignments (Angalakudati et al 2014) Such leveraging of data-driven algorithms to improve. Algorithmic, data-driven support for operations in both police forces and the judicial system has substantially grown in recent years, ranging from software supporting probation decisions and sentencing to improved targeting of police resources (Metz and Satariano 2020, Sassaman 2020) The impact of this development is heavily debated with concerns over, for example, racial and sociodemographic biases, policing intensity, and accountability shaping that discussion (Ferguson 2017, Shapiro 2017). Preventive and proactive measures that strategically leverage police presence in hot spot areas to quickly react to and possibly prevent crime have become an important goal for research and practice (Braga et al 2014)
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