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Urban crime trends analysis and occurrence possibility prediction based on light gradient boosting machine

  • Gangmin Li
    ,
  • Xiangzhi Tong
    ,
  • Pin Ni
    ,
  • Qingge Li
    ,
  • QiAo Yuan
    ,
  • Junru Liu
  • Xi'an Jiaotong-Liverpool University
    ,
  • The University of Auckland
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Open access

Sustainable Development Goals

  • SDG 16 - Peace, Justice and Strong Institutions
    SDG 16 Peace, Justice and Strong Institutions

Abstract

Big Data and Machine learning have been increasingly used to fight against Urban crimes. Our goal is to discover the connection between crime-related factors and the underlying complex crime pattern. Therefore, to predict the possibility of crime occurrence. Light Gradient Boosting Machine (LightGBM) Model is adopted in our study to predict the crime occurrence possibility based on actual crime information. We found that the prediction results are approximately consistent with an actual variation. We hope this work could help with crime prevention and policing.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 98-103 (6 pages)

Publication milestones

  • Published - 20/08/2021

Publication status

Published - 20/08/2021

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

Publication series

  • Publication series name: 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021

ISBN (Print)

9781665412704

ISBN (Electronic)

9781665412704

Publication IDs

  • handle.net: 10547/625923
  • Scopus: 85114465804

Host publication title

2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021

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