Theft prediction with individual risk factor of visitors

Publication Year: 2018 Publication Type : ConferenceProceeding


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.


    author = {Shakila Khan Rumi and Ke Deng and Flora D. Salim},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    booktitle = {Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2018, Seattle, WA, USA, November 06-09, 2018},
    doi = {10.1145/3274895.3274994},
    editor = {Farnoush Banaei Kashani and Erik G. Hoel and Ralf Hartmut Güting and Roberto Tamassia and Li Xiong},
    pages = {552--555},
    publisher = {ACM},
    timestamp = {Wed, 25 Sep 2019 01:00:00 +0200},
    title = {Theft prediction with individual risk factor of visitors},
    url = {},
    year = {2018}


Related Publications

RUP: Large Room Utilisation Prediction with carbon dioxide sensor
Type : JournalArticle
Show More
A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO 2 Sensor Data
Type : JournalArticle
Show More
Topical Event Detection on Twitter
Type : ConferenceProceeding
Show More

© 2021 Flora Salim - CRUISE Research Group.