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Crime Rate Inference with Big Data

Authors:

Zhenhui LiAuthors Info & Claims

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 635 - 644

https://doi.org/10.1145/2939672.2939736

Published: 13 August 2016 Publication History

Abstract

Crime is one of the most important social problems in the country, affecting public safety, children development, and adult socioeconomic status. Understanding what factors cause higher crime is critical for policy makers in their efforts to reduce crime and increase citizens' life quality. We tackle a fundamental problem in our paper: crime rate inference at the neighborhood level. Traditional approaches have used demographics and geographical influences to estimate crime rates in a region. With the fast development of positioning technology and prevalence of mobile devices, a large amount of modern urban data have been collected and such big data can provide new perspectives for understanding crime. In this paper, we used large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL in the USA. We observed significantly improved performance in crime rate inference compared to using traditional features. Such an improvement is consistent over multiple years. We also show that these new features are significant in the feature importance analysis.

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Cited By

Karpavicius TStavytskyy AGiedraitis VUlvidienė EKharlamova GKavaliauskaite B(2024)What Determines the Crime Rate? A Macroeconomic Case StudyEconomies10.3390/economies1209025012:9(250)Online publication date: 17-Sep-2024
https://doi.org/10.3390/economies12090250
Sandeep PSurbhi S(2024)Crime Stats: A Log of Different Crimes in IndiaSSRN Electronic Journal10.2139/ssrn.4493510Online publication date: 2024
https://doi.org/10.2139/ssrn.4493510
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
Show More Cited By

Index Terms

Crime Rate Inference with Big Data
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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Published In

cover image ACM Conferences

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2016

2176 pages

ISBN:9781450342322

DOI:10.1145/2939672

General Chairs:
Balaji Krishnapuram
IBM
,
Mohak Shah
Bosch
,
Program Chairs:
Alex Smola
Amazon
,
Charu Aggarwal
IBM
,
Dou Shen
Baidu
,
Rajeev Rastogi
Amazon

Copyright © 2016 ACM.

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Published: 13 August 2016

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KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2016

California, San Francisco, USA

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KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.3390/economies12090250
Sandeep PSurbhi S(2024)Crime Stats: A Log of Different Crimes in IndiaSSRN Electronic Journal10.2139/ssrn.4493510Online publication date: 2024
https://doi.org/10.2139/ssrn.4493510
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
Deng MZhang WZhao JWang ZZhou MLuo JChen C(2024)A Novel Framework for Joint Learning of City Region Partition and RepresentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365285720:7(1-23)Online publication date: 17-Mar-2024
https://dl.acm.org/doi/10.1145/3652857
Yang KYang YGao QZhong TWang YZhou FHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Self-Explainable Next POI RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657967(2619-2623)Online publication date: 10-Jul-2024
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https://doi.org/10.1109/TKDE.2023.3348550
Malik KPandey MKhan ASrivastav MGera AChauhan S(2024)Crime Prediction by Comparing Machine Learning and Deep Learning Algorithms2024 2nd International Conference on Disruptive Technologies (ICDT)10.1109/ICDT61202.2024.10489578(215-219)Online publication date: 15-Mar-2024
https://doi.org/10.1109/ICDT61202.2024.10489578
Nascimento MBarreto L(2024)Improving crime count forecasts in the city of Rio de Janeiro via reconciliationSecurity Journal10.1057/s41284-024-00433-537:4(1597-1618)Online publication date: 9-Jun-2024
https://doi.org/10.1057/s41284-024-00433-5
Feng JLiang YHao QXu KQiu W(2024)Comparing effectiveness of point of interest data and land use data in theft crime modelling: A case study in BeijingLand Use Policy10.1016/j.landusepol.2024.107357147(107357)Online publication date: Dec-2024
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Ning YLiu HWang HZeng ZXiong HOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)UUKGProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668849(62442-62456)Online publication date: 10-Dec-2023
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