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Award ID contains: 2015787

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  1. ; ; ( , INFORMS Journal on Applied Analytics)
    We present a data-driven optimization framework for redesigning police patrol zones in an urban environment. The objectives are to rebalance police workload along geographical areas and to reduce response time to emergency calls. We develop a stochastic model for police emergency response by integrating multiple data sources, including police incident reports, demographic surveys, and traffic data. Using this stochastic model, we optimize zone-redesign plans using mixed-integer linear programming. Our proposed design was implemented by the Atlanta Police Department in March 2019. By analyzing data before and after the zone redesign, we show that the new design has reduced the response time to high-priority 911 calls by 5.8% and the imbalance of police workload among Atlanta’s zones by 43%. 
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  2. ; ( , The Annals of Applied Statistics)
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  3. ; ; ; ; ( , KDD 2021 Workshop on Data Science for Social Good)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ( , KDD Workshop on Data Science for Social Good)
    Accepted Manuscript Full Text Available