research-article

DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction

Authors:

Nitesh V. ChawlaAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 1423 - 1432

https://doi.org/10.1145/3269206.3271793

Published: 17 October 2018 Publication History

Abstract

As urban crimes (e.g., burglary and robbery) negatively impact our everyday life and must be addressed in a timely manner, predicting crime occurrences is of great importance for public safety and urban sustainability. However, existing methods do not fully explore dynamic crime patterns as factors underlying crimes may change over time. In this paper, we develop a new crime prediction framework--DeepCrime, a deep neural network architecture that uncovers dynamic crime patterns and carefully explores the evolving inter-dependencies between crimes and other ubiquitous data in urban space. Furthermore, our DeepCrime framework is capable of automatically capturing the relevance of crime occurrences across different time periods. In particular, our DeepCrime framework enables predicting crime occurrences of different categories in each region of a city by i) jointly embedding all spatial, temporal, and categorical signals into hidden representation vectors, and ii) capturing crime dynamics with an attentive hierarchical recurrent network. Extensive experiments on real-world datasets demonstrate the superiority of our framework over many competitive baselines across various settings.

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Cited By

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https://doi.org/10.1109/JIOT.2024.3427429
Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Wang SZhang YPiao XLin XHu YYin B(2025)MRAGNN: Refining urban spatio-temporal prediction of crime occurrence with multi-type crime correlation learningExpert Systems with Applications10.1016/j.eswa.2024.125940265(125940)Online publication date: Mar-2025
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Index Terms

DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction
1. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Copyright © 2018 ACM.

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Published: 17 October 2018

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National Science Foundation (NSF) Grants
National Natural Science Foundation of China

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CIKM '18

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CIKM '18: The 27th ACM International Conference on Information and Knowledge Management

October 22 - 26, 2018

Torino, Italy

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CIKM '18 Paper Acceptance Rate 147 of 826 submissions, 18%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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https://doi.org/10.1109/JIOT.2024.3427429
Zou XYan YHao XHu YWen HLiu EZhang JLi YLi TZheng YLiang Y(2025)Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlookInformation Fusion10.1016/j.inffus.2024.102606113(102606)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102606
Wang SZhang YPiao XLin XHu YYin B(2025)MRAGNN: Refining urban spatio-temporal prediction of crime occurrence with multi-type crime correlation learningExpert Systems with Applications10.1016/j.eswa.2024.125940265(125940)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125940
Taha K(2024)Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive SurveyACM Transactions on Intelligent Systems and Technology10.1145/369951516:2(1-75)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3699515
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
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Wang SPan XRuan SHan HWang ZYuan HZhu JLi QBaeza-Yates RBonchi F(2024)DiffCrime: A Multimodal Conditional Diffusion Model for Crime Risk Map InferenceProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671843(3212-3221)Online publication date: 25-Aug-2024
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Yu CXie XXiao ZWu YZhang JXu ZMai Z(2024)Crime Prediction Using Spatial-Temporal Synchronous Graph Convolutional Networks2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI)10.1109/ISCMI63661.2024.10851671(129-133)Online publication date: 22-Nov-2024
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