poster

Theft prediction with individual risk factor of visitors

Authors:

Shakila Khan Rumi,

Flora D. SalimAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 552 - 555

https://doi.org/10.1145/3274895.3274994

Published: 06 November 2018 Publication History

Abstract

Location-Based Social Networks (LBSN) provides unprecedented opportunities to tackle various social problems. In this study, we identify a number of crime-prediction-specific dynamic features which, for the first time, explore crime risk factors implicitly associated with the visitors. The reliable correlations between the proposed dynamic features and crime event occurrences have been observed. The evaluations on large real world data sets verify that the crime prediction performance can be notably improved with the inclusion of proposed crime-prediction-specific dynamic features.

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Cited By

Lee YBradford BPosch K(2024)The Effectiveness of Big Data-Driven Predictive Policing: Systematic ReviewJustice Evaluation Journal10.1080/24751979.2024.2371781(1-34)Online publication date: 5-Jul-2024
https://doi.org/10.1080/24751979.2024.2371781
Liang WCao JChen LWang YWu JBeheshti ATang J(2023)Crime Prediction With Missing Data Via Spatiotemporal Regularized Tensor DecompositionIEEE Transactions on Big Data10.1109/TBDATA.2023.32830989:5(1392-1407)Online publication date: Oct-2023
https://doi.org/10.1109/TBDATA.2023.3283098
Sun YChen TYin H(2023)Spatial-temporal meta-path guided explainable crime predictionWorld Wide Web10.1007/s11280-023-01137-326:4(2237-2263)Online publication date: 3-Feb-2023
https://doi.org/10.1007/s11280-023-01137-3
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Theft prediction with individual risk factor of visitors

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Published In

cover image ACM Conferences

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

Copyright © 2018 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2018

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SIGSPATIAL '18

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SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

Acceptance Rates

SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Citations

Cited By

Lee YBradford BPosch K(2024)The Effectiveness of Big Data-Driven Predictive Policing: Systematic ReviewJustice Evaluation Journal10.1080/24751979.2024.2371781(1-34)Online publication date: 5-Jul-2024
https://doi.org/10.1080/24751979.2024.2371781
Liang WCao JChen LWang YWu JBeheshti ATang J(2023)Crime Prediction With Missing Data Via Spatiotemporal Regularized Tensor DecompositionIEEE Transactions on Big Data10.1109/TBDATA.2023.32830989:5(1392-1407)Online publication date: Oct-2023
https://doi.org/10.1109/TBDATA.2023.3283098
Sun YChen TYin H(2023)Spatial-temporal meta-path guided explainable crime predictionWorld Wide Web10.1007/s11280-023-01137-326:4(2237-2263)Online publication date: 3-Feb-2023
https://doi.org/10.1007/s11280-023-01137-3
Liang WWu ZLi ZGe Y(2022)CrimeTensor: Fine-Scale Crime Prediction via Tensor Learning with Spatiotemporal ConsistencyACM Transactions on Intelligent Systems and Technology10.1145/350180713:2(1-24)Online publication date: 25-Mar-2022
https://dl.acm.org/doi/10.1145/3501807
Zhao L(2021)Event Prediction in the Big Data EraACM Computing Surveys10.1145/345028754:5(1-37)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1145/3450287
Wei XQian YSun CSun JLiu Y(2021)A survey of location-based social networks: problems, methods, and future research directionsGeoInformatica10.1007/s10707-021-00450-1Online publication date: 24-Sep-2021
https://doi.org/10.1007/s10707-021-00450-1
Hamdi AShaban KErradi AMohamed ARumi SSalim F(2021)Spatiotemporal data mining: a survey on challenges and open problemsArtificial Intelligence Review10.1007/s10462-021-09994-yOnline publication date: 15-Apr-2021
https://doi.org/10.1007/s10462-021-09994-y
Rumi SSalim Fd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Modelling Regional Crime Risk using Directed Graph of Check-insProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3412065(2201-2204)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3412065
Henrique de Jesus Prado KSantos Souza LDias de Jesus Junior IColaço Júnior M(2020)Applied Intelligent Data Analysis to Government Data Related to Criminal Incident: A Systematic ReviewJournal of Applied Security Research10.1080/19361610.2020.171651115:3(297-331)Online publication date: 25-Jan-2020
https://doi.org/10.1080/19361610.2020.1716511

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