@inproceedings{fd250d96d6004d28b55a479fa68e4d1d,
title = "Urban Crime Trends Analysis and Occurrence Possibility Prediction based on Light Gradient Boosting Machine",
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.",
keywords = "Crime Forecasting, Data Analysis, Light Gradient Boosting Machine, Random Forest",
author = "Xiangzhi Tong and Pin Ni and Qingge Li and Qiao Yuan and Junru Liu and Hanzhe Lu and Gangmin Li",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021 ; Conference date: 02-07-2021 Through 04-07-2021",
year = "2021",
month = jul,
day = "2",
doi = "10.1109/BDAI52447.2021.9515252",
language = "English",
series = "2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "98--103",
booktitle = "2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021",
}