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Modeling Temporal-Spatial Correlations for Crime Prediction

Authors:

Xiangyu Zhao and

Jiliang TangAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

Pages 497 - 506

https://doi.org/10.1145/3132847.3133024

Published: 06 November 2017 Publication History

Abstract

Crime prediction plays a crucial role in improving public security and reducing the financial loss of crimes. The vast majority of traditional algorithms performed the prediction by leveraging demographic data, which could fail to capture the dynamics of crimes in urban. In the era of big data, we have witnessed advanced ways to collect and integrate fine-grained urban, mobile, and public service data that contains various crime-related sources and rich temporal-spatial information. Such information provides better understandings about the dynamics of crimes and has potentials to advance crime prediction. In this paper, we exploit temporal-spatial correlations in urban data for crime prediction. In particular, we validate the existence of temporal-spatial correlations in crime and develop a principled approach to model these correlations into the coherent framework TCP for crime prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of temporal-spatial correlations in crime prediction.

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Cited By

Zhou BZhou HWang WChen LMa JZheng Z(2024)HDM-GNN: A Heterogeneous Dynamic Multi-view Graph Neural Network for Crime PredictionACM Transactions on Sensor Networks10.1145/3665141Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3665141
Lee YBradford BPosch K(2024)The Effectiveness of Big Data-Driven Predictive Policing: Systematic ReviewJustice Evaluation Journal10.1080/24751979.2024.2371781(1-34)Online publication date: 5-Jul-2024
https://doi.org/10.1080/24751979.2024.2371781
Nascimento MBarreto L(2024)Improving crime count forecasts in the city of Rio de Janeiro via reconciliationSecurity Journal10.1057/s41284-024-00433-5Online publication date: 9-Jun-2024
https://doi.org/10.1057/s41284-024-00433-5
Show More Cited By

Index Terms

Modeling Temporal-Spatial Correlations for Crime Prediction
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems
      2. Location based services

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cover image ACM Conferences

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

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Published: 06 November 2017

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November 6 - 10, 2017

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Cited By

Zhou BZhou HWang WChen LMa JZheng Z(2024)HDM-GNN: A Heterogeneous Dynamic Multi-view Graph Neural Network for Crime PredictionACM Transactions on Sensor Networks10.1145/3665141Online publication date: 14-May-2024
https://dl.acm.org/doi/10.1145/3665141
Lee YBradford BPosch K(2024)The Effectiveness of Big Data-Driven Predictive Policing: Systematic ReviewJustice Evaluation Journal10.1080/24751979.2024.2371781(1-34)Online publication date: 5-Jul-2024
https://doi.org/10.1080/24751979.2024.2371781
Nascimento MBarreto L(2024)Improving crime count forecasts in the city of Rio de Janeiro via reconciliationSecurity Journal10.1057/s41284-024-00433-5Online publication date: 9-Jun-2024
https://doi.org/10.1057/s41284-024-00433-5
A. Alexander Adhavan M Dinesh E Maroof Salih (2023)Predicting Poverty Level From Satellite Imagery Using Ensemble LearningInternational Journal of Scientific Research in Science, Engineering and Technology10.32628/IJSRSET23103175(626-633)Online publication date: 10-Jun-2023
https://doi.org/10.32628/IJSRSET23103175
Zhang ZZhao XLiu QZhang CMa QWang WZhao HWang YLiu ZFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615016(3195-3205)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615016
Zhang ZHuang ZHu ZZhao XWang WLiu ZZhang JQin SZhao HFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)MLPST: MLP is All You Need for Spatio-Temporal PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614969(3381-3390)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614969
Ma QZhang ZZhao XLi HZhao HWang YLiu ZWang WFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic ForecastingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614910(1756-1765)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614910
Rayhan YHashem T(2023)AIST: An Interpretable Attention-Based Deep Learning Model for Crime PredictionACM Transactions on Spatial Algorithms and Systems10.1145/35822749:2(1-31)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1145/3582274
Zhou BChen LZhao SLi SZheng ZPan G(2023)Unsupervised Domain Adaptation for Crime Risk Prediction Across CitiesIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.320798710:6(3217-3227)Online publication date: Dec-2023
https://doi.org/10.1109/TCSS.2022.3207987
Liang WCao JChen LWang YWu JBeheshti ATang J(2023)Crime Prediction With Missing Data Via Spatiotemporal Regularized Tensor DecompositionIEEE Transactions on Big Data10.1109/TBDATA.2023.32830989:5(1392-1407)Online publication date: Oct-2023
https://doi.org/10.1109/TBDATA.2023.3283098
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