Tong, X;
Ni, P;
Li, Q;
Yuan, Q;
Liu, J;
Lu, H;
Li, G;
(2021)
Urban Crime Trends Analysis and Occurrence Possibility Prediction based on Light Gradient Boosting Machine.
In:
2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021.
(pp. pp. 98-103).
IEEE: Qingdao, China.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Urban Crime Trends Analysis and Occurrence Possibility Prediction based on Light Gradient Boosting Machine |
| Event: | 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI) |
| Dates: | 2 Jul 2021 - 4 Jul 2021 |
| ISBN-13: | 9781665412704 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/BDAI52447.2021.9515252 |
| Publisher version: | https://doi.org/10.1109/BDAI52447.2021.9515252 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Light Gradient Boosting Machine, Crime Forecasting, Data Analysis, Random Forest |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159888 |
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