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Spatially embedded co-offence prediction using supervised learning

Authors:

Mohammad A. Tayebi,

Patricia L. BrantinghamAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1789 - 1798

https://doi.org/10.1145/2623330.2623353

Published: 24 August 2014 Publication History

Abstract

Crime reduction and prevention strategies are essential to increase public safety and reduce the crime costs to society. Law enforcement agencies have long realized the importance of analyzing co-offending networks---networks of offenders who have committed crimes together---for this purpose. Although network structure can contribute significantly to co-offence prediction, research in this area is very limited. Here we address this important problem by proposing a framework for co-offence prediction using supervised learning. Considering the available information about offenders, we introduce social, geographic, geo-social and similarity feature sets which are used for classifying potential negative and positive pairs of offenders. Similar to other social networks, co-offending networks also suffer from a highly skewed distribution of positive and negative pairs. To address the class imbalance problem, we identify three types of criminal cooperation opportunities which help to reduce the class imbalance ratio significantly, while keeping half of the co-offences. The proposed framework is evaluated on a large crime dataset for the Province of British Columbia, Canada. Our experimental evaluation of four different feature sets show that the novel geo-social features are the best predictors. Overall, we experimentally show the high effectiveness of the proposed co-offence prediction framework. We believe that our framework will not only allow law enforcement agencies to improve their crime reduction and prevention strategies, but also offers new criminological insights into criminal link formation between offenders.

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Yu XHan PLiu CDuan SPan HXiang X(2021)FIGAT: Accurately Predict Individual Crime Risks with Multi-information Fusion2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC)10.1109/DSC53577.2021.00041(248-255)Online publication date: Oct-2021
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Index Terms

Spatially embedded co-offence prediction using supervised learning
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 24 August 2014

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KDD '14

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

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

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KDD '25

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https://doi.org/10.1109/TSC.2022.3206093
Hou YWang HWang H(2022)Identification of Chinese dark jargons in Telegram underground markets using context-oriented and linguistic featuresInformation Processing & Management10.1016/j.ipm.2022.10303359:5(103033)Online publication date: Sep-2022
https://doi.org/10.1016/j.ipm.2022.103033
Yu XHan PLiu CDuan SPan HXiang X(2021)FIGAT: Accurately Predict Individual Crime Risks with Multi-information Fusion2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC)10.1109/DSC53577.2021.00041(248-255)Online publication date: Oct-2021
https://doi.org/10.1109/DSC53577.2021.00041
Nurek MMichalski R(2020)Combining Machine Learning and Social Network Analysis to Reveal the Organizational StructuresApplied Sciences10.3390/app1005169910:5(1699)Online publication date: 2-Mar-2020
https://doi.org/10.3390/app10051699
Michalski RDziubaltowska DMacek P(2020)Revealing the Character of Nodes in a Blockchain With Supervised LearningIEEE Access10.1109/ACCESS.2020.30016768(109639-109647)Online publication date: 2020
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Huang SBan T(2019)A Topic-Based Unsupervised Learning Approach for Online Underground Market Exploration2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)10.1109/TrustCom/BigDataSE.2019.00036(208-215)Online publication date: Aug-2019
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Zhao XTang J(2018)Crime in Urban Areas:ACM SIGKDD Explorations Newsletter10.1145/3229329.322933120:1(1-12)Online publication date: 29-May-2018
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