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From Prediction to Action: A Constraint-Based Approach to Predictive Policing

Authors Younes Mechqrane , Ismail Elabbassi

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ACM Subject Classification
  • Applied computing → Law, social and behavioral sciences
Keywords
  • Inductive Constraint Programming (ICON) Loop
  • Next Frame Prediction
  • PredRNN++

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Abstract

Crime prevention in urban environments demands both accurate crime forecasting and the efficient deployment of limited law enforcement resources. In this paper, we present an integrated framework that combines a machine learning module (i.e. PredRNN++ [Wang et al., 2018]) for spatiotemporal crime prediction with a constraint programming module for patrol route optimization. Our approach operates within the ICON loop framework [Bessiere et al., 2017], facilitating iterative refinement of predictions and immediate adaptation of patrol strategies. We validate our method using the City of Chicago Crime Dataset. Experimental results show that routes informed by crime predictions significantly outperform strategies relying solely on historical patterns or operational constraints. These findings illustrate how coupling predictive analytics with constraint programming can substantially enhance resource allocation and overall crime deterrence.

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Younes Mechqrane and Ismail Elabbassi. From Prediction to Action: A Constraint-Based Approach to Predictive Policing. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 29:1-29:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.CP.2025.29

Author Details

Younes Mechqrane
  • Ai movement - International Artificial Intelligence Center of Morocco, University Mohammed VI Polytechnic, Rabat, Morocco
Ismail Elabbassi
  • Ai movement - International Artificial Intelligence Center of Morocco, University Mohammed VI Polytechnic, Rabat, Morocco

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