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Crime, a universal phenomenon rooted in social dynamics, requires innovative approaches to public security. This paper presents a method that integrates Process Mining, LSTM (Long Short Term Memory), and Markov Chains to predict crime types, supporting resource allocation and preventive strategies. Using 512,657 crime records from Denver, Colorado, the method follows six stages: (i) data preparation, (ii) DFG (Directly Follows Graph) extraction, (iii) transition matrix construction (Markov Chains), (iv) LSTM-based prediction, (v) model evaluation, and (vi) visualization for decision making. The proposed method achieved 94% accuracy, with precision and recall around 95% (Top-1) in the LSTM model, significantly outperforming Markov Chains. This approach provides interpretable visualizations that enhance strategic decision-making and highlights the potential of combining machine learning and statistical modeling for more effective public security policies.

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An LSTM and Markov Chain-Based Approach for Urban Crime Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Fernanda Parizotto Padilha,
  • Eduardo Borsa,
  • Edson Emilio Scalabrin

摘要

Crime, a universal phenomenon rooted in social dynamics, requires innovative approaches to public security. This paper presents a method that integrates Process Mining, LSTM (Long Short Term Memory), and Markov Chains to predict crime types, supporting resource allocation and preventive strategies. Using 512,657 crime records from Denver, Colorado, the method follows six stages: (i) data preparation, (ii) DFG (Directly Follows Graph) extraction, (iii) transition matrix construction (Markov Chains), (iv) LSTM-based prediction, (v) model evaluation, and (vi) visualization for decision making. The proposed method achieved 94% accuracy, with precision and recall around 95% (Top-1) in the LSTM model, significantly outperforming Markov Chains. This approach provides interpretable visualizations that enhance strategic decision-making and highlights the potential of combining machine learning and statistical modeling for more effective public security policies.