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Modeling Temporal-Spatial Correlations for Crime Prediction

Authors:

Jiliang TangAuthors Info & Claims

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

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

Liu YZou M(2024)Research on the method of predicting the trend of criminal activities based on time series analysis from the perspective of criminal procedure lawApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-21799:1Online publication date: 5-Aug-2024
https://doi.org/10.2478/amns-2024-2179
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
Zhu YYu JZhao XLiu QYe YChen WZhang ZWei XLiang YBaeza-Yates RBonchi F(2024)ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion ModelProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671866(4676-4687)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671866
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

Liu YZou M(2024)Research on the method of predicting the trend of criminal activities based on time series analysis from the perspective of criminal procedure lawApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-21799:1Online publication date: 5-Aug-2024
https://doi.org/10.2478/amns-2024-2179
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
Zhu YYu JZhao XLiu QYe YChen WZhang ZWei XLiang YBaeza-Yates RBonchi F(2024)ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion ModelProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671866(4676-4687)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671866
Xie YQiu TXiong YHuang XGao XChen CWang QLi HBaeza-Yates RBonchi F(2024)Weather Knows What Will Occur: Urban Public Nuisance Events Prediction and Control with Meteorological AssistanceProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671639(6037-6048)Online publication date: 25-Aug-2024
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