research-article

Crime in Urban Areas:: A Data Mining Perspective

Authors:

Jiliang TangAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 20, Issue 1

Pages 1 - 12

https://doi.org/10.1145/3229329.3229331

Published: 29 May 2018 Publication History

Abstract

Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. Traditional urban crime research focused on leveraging demographic data, which is insufficient to capture the complexity and dynamics of urban crimes. 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 as well as rich environmental and social information. The availability of big urban data provides unprecedented opportunities, which enable us to conduct advanced urban crime research. Meanwhile, environmental and social crime theories from criminology provide better understandings about the behaviors of offenders and complex patterns of crime in urban. They can not only help bridge the gap from what we have (big urban data) to what we want to understand about urban crime (urban crime analysis); but also guide us to build computational models for crime. In this article, we give an overview to key theories from criminology, summarize crime analysis on urban data, review state-of-the-art algorithms for various types of computational crime tasks and discuss some appealing research directions that can bring the urban crime research into a new frontier.

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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 20, Issue 1

June 2018

59 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/3229329

Editors:
Charu Aggarwal
IBM T.J. Watson
,
Haixun Wang
Google
,
Ankur Teredesai
University of Washington Tacoma
,
Hanghang Tong
Arizona State University

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Copyright © 2018 Authors.

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Association for Computing Machinery

New York, NY, United States

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Published: 29 May 2018

Published in SIGKDD Volume 20, Issue 1

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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
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