IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i22p4368-d978458.html
   My bibliography  Save this article

Smart Patrolling Based on Spatial-Temporal Information Using Machine Learning

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/22/4368/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/22/4368/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Md Amiruzzaman & Ye Zhao & Stefanie Amiruzzaman & Aryn C. Karpinski & Tsung Heng Wu, 2023. "An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables," Journal of Computational Social Science, Springer, vol. 6(1), pages 315-337, April.
    2. Suphanut Jamonnak & Deepshikha Bhati & Md Amiruzzaman & Ye Zhao & Xinyue Ye & Andrew Curtis, 2022. "VisualCommunity: a platform for archiving and studying communities," Journal of Computational Social Science, Springer, vol. 5(2), pages 1257-1279, November.
    3. M. R. Mahendrini Fernando Ariyachandra & Gayan Wedawatta, 2023. "Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
    4. Huafang Xie & Lin Liu & Han Yue, 2022. "Modeling the Effect of Streetscape Environment on Crime Using Street View Images and Interpretable Machine-Learning Technique," IJERPH, MDPI, vol. 19(21), pages 1-22, October.
    5. Lufeng Luo & Hanjin Wen & Qinghua Lu & Haojie Huang & Weilin Chen & Xiangjun Zou & Chenglin Wang, 2018. "Collision-Free Path-Planning for Six-DOF Serial Harvesting Robot Based on Energy Optimal and Artificial Potential Field," Complexity, Hindawi, vol. 2018, pages 1-12, November.
    6. Moisés Lodeiro-Santiago & Pino Caballero-Gil & Ricardo Aguasca-Colomo & Cándido Caballero-Gil, 2019. "Secure UAV-Based System to Detect Small Boats Using Neural Networks," Complexity, Hindawi, vol. 2019, pages 1-11, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4368-:d:978458. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.