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; Hanzhe Lu

doi:10.1109/BDAI52447.2021.9515252

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
, Hanzhe Lu

Institute for Research in Applicable Computing (IRAC)

Xi'an Jiaotong-Liverpool University
The University of Auckland

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

1 Downloads (Pure)

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.

Original language	English
Title of host publication	2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	98-103
Number of pages	6
ISBN (Electronic)	9781665412704
ISBN (Print)	9781665412704
DOIs	https://doi.org/10.1109/BDAI52447.2021.9515252
Publication status	Published - 20 Aug 2021
Event	2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI) - Qingdao Duration: 2 Jul 2021 → 4 Jul 2021

Publication series

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

Conference

Conference	2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)
City	Qingdao
Period	2/07/21 → 4/07/21
Other	2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI) (02/07/2021-04/07/2021, Qingdao)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

Crime Forecasting
Data analysis
Light Gradient Boosting Machine
Random Forest
Data Analysis

ASJC Scopus subject areas

Signal Processing
Information Systems and Management
Artificial Intelligence

Access to Document

10.1109/BDAI52447.2021.9515252

Urban Crime Trends Analysis and Occurrence Possibility Prediction based on Light Gradient Boosting MachineAccepted author manuscript, 2.7 MBLicence: CC BY-NC-ND

https://ieeexplore.ieee.org/document/9515252

Handle.net

Persistent link

Cite this

Li, G., Tong, X., Ni, P., Li, Q., Yuan, Q., Liu, J., & Lu, H. (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. 98-103). (2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BDAI52447.2021.9515252

Li, Gangmin ; Tong, Xiangzhi ; Ni, Pin et al. / Urban crime trends analysis and occurrence possibility prediction based on light gradient boosting machine. 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 98-103 (2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021).

@inproceedings{51b37ac7a1014da6809fa6c7dd28265f,

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, Data Analysis",

author = "Gangmin Li and Xiangzhi Tong and Pin Ni and Qingge Li and QiAo Yuan and Junru Liu and Hanzhe Lu",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI) ; Conference date: 02-07-2021 Through 04-07-2021",

year = "2021",

month = aug,

day = "20",

doi = "10.1109/BDAI52447.2021.9515252",

language = "English",

isbn = "9781665412704",

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",

address = "United States",

}

Li, G, Tong, X, Ni, P, Li, Q, Yuan, Q, Liu, J & Lu, H 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. 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021, Institute of Electrical and Electronics Engineers Inc., pp. 98-103, 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI), Qingdao, 2/07/21. https://doi.org/10.1109/BDAI52447.2021.9515252

Urban crime trends analysis and occurrence possibility prediction based on light gradient boosting machine. / Li, Gangmin; Tong, Xiangzhi; Ni, Pin et al.
2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 98-103 (2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Urban crime trends analysis and occurrence possibility prediction based on light gradient boosting machine

AU - Li, Gangmin

AU - Tong, Xiangzhi

AU - Ni, Pin

AU - Li, Qingge

AU - Yuan, QiAo

AU - Liu, Junru

AU - Lu, Hanzhe

PY - 2021/8/20

Y1 - 2021/8/20

N2 - 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.

AB - 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.

KW - Crime Forecasting

KW - Data analysis

KW - Light Gradient Boosting Machine

KW - Random Forest

KW - Data Analysis

UR - https://www.scopus.com/pages/publications/85114465804

U2 - 10.1109/BDAI52447.2021.9515252

DO - 10.1109/BDAI52447.2021.9515252

M3 - Conference contribution

SN - 9781665412704

T3 - 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021

SP - 98

EP - 103

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)

Y2 - 2 July 2021 through 4 July 2021

ER -

Li G, Tong X, Ni P, Li Q, Yuan Q, Liu J et al. 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. Institute of Electrical and Electronics Engineers Inc. 2021. p. 98-103. (2021 IEEE 4th International Conference on Big Data and Artificial Intelligence, BDAI 2021). doi: 10.1109/BDAI52447.2021.9515252

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

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Handle.net

Other files and links

Fingerprint

Cite this