Theft prediction with individual risk factor of visitors

Rumi, S, Deng, K and Salim, F 2018, 'Theft prediction with individual risk factor of visitors', in Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, United States, 06-09 November 2018, pp. 552-555.


Document type: Conference Paper
Collection: Conference Papers

Title Theft prediction with individual risk factor of visitors
Author(s) Rumi, S
Deng, K
Salim, F
Year 2018
Conference name ACM SIGSPATIAL
Conference location Seattle, United States
Conference dates 06-09 November 2018
Proceedings title Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Publisher ACM
Place of publication United States
Start page 552
End page 555
Total pages 4
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.
Subjects Pattern Recognition and Data Mining
DOI - identifier 10.1145/3274895.3274994
Copyright notice © 2018 Copyright held by the owner/author(s).
ISBN 9781450358897
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