research-article

Understanding the link between social and spatial distance in the crime world

Authors:

Mohammad A. Tayebi,

Richard Frank,

Uwe GlässerAuthors Info & Claims

SIGSPATIAL '12: Proceedings of the 20th International Conference on Advances in Geographic Information Systems

Pages 550 - 553

https://doi.org/10.1145/2424321.2424412

Published: 06 November 2012 Publication History

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Abstract

Individuals frequently have routine daily activities that require commuting between several places, such as their home, work, shopping centres and recreational facilities. According to Crime Pattern Theory, offenders most likely commit opportunistic crimes, including serial and violent crimes, within their Activity Space, that is the space that they visit most frequently during the course of their daily routine activities, since they are aware of the opportunities and risks within these spaces. However, others within the social network of an offender can introduce the offender to new opportunities outside of his Activity Space, a phenomenon that Crime Pattern Theory does not address. This paper explores an important and interesting question about social networks: What is the relation between social and spatial distance of actors? We study this question in the context of crime and co-offending networks to better understand the impact of an offender's social network on the Activity Space of the offender. Our experiments on real-life crime data show that there is a strong correlation between social and spatial distance of offenders: offenders who are socially close are also spatially close.

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Cited By

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Al-Ghushami ASyed DSessa JZainab A(2022)Intelligent Automation of Crime Prediction using Data Mining2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)10.1109/ISIE51582.2022.9831620(245-252)Online publication date: 1-Jun-2022
https://doi.org/10.1109/ISIE51582.2022.9831620
Curtis-Ham SBernasco WMedvedev OPolaschek D(2021)A National Examination of the Spatial Extent and Similarity of Offenders’ Activity Spaces Using Police DataISPRS International Journal of Geo-Information10.3390/ijgi1002004710:2(47)Online publication date: 23-Jan-2021
https://doi.org/10.3390/ijgi10020047
Pathak ATasin ASania SAdil MMunna A(2021)Belief Rule-Based Expert System to Identify the Crime ZonesIntelligent Computing and Optimization10.1007/978-3-030-68154-8_24(237-249)Online publication date: 8-Feb-2021
https://doi.org/10.1007/978-3-030-68154-8_24
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Index Terms

Understanding the link between social and spatial distance in the crime world
1. Information systems
  1. Information systems applications
    1. Data mining

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Information

Published In

SIGSPATIAL '12: Proceedings of the 20th International Conference on Advances in Geographic Information Systems

November 2012

642 pages

ISBN:9781450316910

DOI:10.1145/2424321

General Chairs:
Isabel Cruz
University of Illinois at Chicago
,
Craig Knoblock
University of Southern California
,
Program Chairs:
Peer Kröger
Ludwig-Maximilians-Universität München, Germany
,
Egemen Tanin
University of Melbourne, Australia
,
Peter Widmayer
ETH Zürich, Switzerland

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2012

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SIGSPATIAL'12

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SIGSPATIAL

SIGSPATIAL'12: SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems

November 6 - 9, 2012

Redondo Beach, California

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

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Al-Ghushami ASyed DSessa JZainab A(2022)Intelligent Automation of Crime Prediction using Data Mining2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)10.1109/ISIE51582.2022.9831620(245-252)Online publication date: 1-Jun-2022
https://doi.org/10.1109/ISIE51582.2022.9831620
Curtis-Ham SBernasco WMedvedev OPolaschek D(2021)A National Examination of the Spatial Extent and Similarity of Offenders’ Activity Spaces Using Police DataISPRS International Journal of Geo-Information10.3390/ijgi1002004710:2(47)Online publication date: 23-Jan-2021
https://doi.org/10.3390/ijgi10020047
Pathak ATasin ASania SAdil MMunna A(2021)Belief Rule-Based Expert System to Identify the Crime ZonesIntelligent Computing and Optimization10.1007/978-3-030-68154-8_24(237-249)Online publication date: 8-Feb-2021
https://doi.org/10.1007/978-3-030-68154-8_24
Tayebi MShahir HGlasser UBrantingham P(2018)Spatial Patterns of Offender Groups2018 IEEE International Conference on Intelligence and Security Informatics (ISI)10.1109/ISI.2018.8587365(13-18)Online publication date: Nov-2018
https://doi.org/10.1109/ISI.2018.8587365
Mukherjee ADe SBhattacharyya SPlatos J(2018)Chicago Crime Data Analysis Using PIG in Hadoop2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)10.1109/ICRCICN.2018.8718725(242-247)Online publication date: Nov-2018
https://doi.org/10.1109/ICRCICN.2018.8718725
TAYEBI MGLÄSSER UESTER MBRANTINGHAM P(2016)Personalized crime location predictionEuropean Journal of Applied Mathematics10.1017/S095679251600014027:03(422-450)Online publication date: 28-Apr-2016
https://doi.org/10.1017/S0956792516000140
Tayebi MGlässer UTayebi MGlässer U(2016)Personalized Crime Location PredictionSocial Network Analysis in Predictive Policing10.1007/978-3-319-41492-8_7(99-126)Online publication date: 12-Oct-2016
https://doi.org/10.1007/978-3-319-41492-8_7
Tayebi MGlausser UBrantingham P(2015)Learning where to inspect: Location learning for crime prediction2015 IEEE International Conference on Intelligence and Security Informatics (ISI)10.1109/ISI.2015.7165934(25-30)Online publication date: May-2015
https://doi.org/10.1109/ISI.2015.7165934
Tayebi MEster MGlässer UBrantingham PJia YRokne JWu XEster MXu G(2014)CrimetracerProceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3191835.3191930(472-480)Online publication date: 17-Aug-2014
https://dl.acm.org/doi/10.5555/3191835.3191930
Tayebi MEster MGlässer UBrantingham PMacskassy SPerlich CLeskovec JWang WGhani R(2014)Spatially embedded co-offence prediction using supervised learningProceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2623330.2623353(1789-1798)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1145/2623330.2623353
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