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Smart Patrolling Based on Spatial-Temporal Information Using Machine Learning

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  • Cesar Guevara

    (The Institute of Mathematical Sciences (ICMAT-CSIC), DataLab, 28049 Madrid, Spain
    Centro de Investigación en Mecatrónica y Sistemas Interactivos—MIST, Universidad Indoamérica, Machala y Sabanilla, Quito 170103, Ecuador)

  • Matilde Santos

    (Institute of Knowledge Technology, Complutense University of Madrid, 28040 Madrid, Spain)

Abstract

With the aim of improving security in cities and reducing the number of crimes, this research proposes an algorithm that combines artificial intelligence (AI) and machine learning (ML) techniques to generate police patrol routes. Real data on crimes reported in Quito City, Ecuador, during 2017 are used. The algorithm, which consists of four stages, combines spatial and temporal information. First, crimes are grouped around the points with the highest concentration of felonies, and future hotspots are predicted. Then, the probability of crimes committed in any of those areas at a time slot is studied. This information is combined with the spatial way-points to obtain real surveillance routes through a fuzzy decision system, that considers distance and time (computed with the OpenStreetMap API), and probability. Computing time has been analized and routes have been compared with those proposed by an expert. The results prove that using spatial–temporal information allows the design of patrolling routes in an effective way and thus, improves citizen security and decreases spending on police resources.

Suggested Citation

  • Cesar Guevara & Matilde Santos, 2022. "Smart Patrolling Based on Spatial-Temporal Information Using Machine Learning," Mathematics, MDPI, vol. 10(22), pages 1-27, November.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4368-:d:978458
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    References listed on IDEAS

    as
    1. Víctor San Juan & Matilde Santos & José Manuel Andújar, 2018. "Intelligent UAV Map Generation and Discrete Path Planning for Search and Rescue Operations," Complexity, Hindawi, vol. 2018, pages 1-17, April.
    2. Md Amiruzzaman & Andrew Curtis & Ye Zhao & Suphanut Jamonnak & Xinyue Ye, 2021. "Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach," Journal of Computational Social Science, Springer, vol. 4(2), pages 813-837, November.
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