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Publikation im Journal "Manufacturing and Service Operations Management" (MSOM)

18. Mai 2021

Unsere Studie "Prescriptive Analytics in Urban Policing Operations" wurde in das Journal "Manufacturing and Service Operations Management" (MSOM) aufgenommen.

Autoren: T. Brandt, O. Dlugosch, A. Abdelwahed, P. van den Berg, D. Neumann



Problem definition. We consider the case of prescriptive policing, i.e. 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, while 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. While optimization integrating all relevant crime types is theoretically possible, it is practically challenging since 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.