TY - GEN
T1 - Modeling temporal-spatial correlations for crime prediction
AU - Zhao, Xiangyu
AU - Tang, Jiliang
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2017/11/6
Y1 - 2017/11/6
N2 - Crime prediction plays a crucial role in improving public security and reducing the financial loss of crimes. The vast majority of traditional algorithms performed the prediction by leveraging demographic data, which could fail to capture the dynamics of crimes in urban. In the era of big data, we have witnessed advanced ways to collect and integrate fine-grained urban, mobile, and public service data that contains various crime-related sources and rich temporal-spatial information. Such information provides better understandings about the dynamics of crimes and has potentials to advance crime prediction. In this paper, we exploit temporal-spatial correlations in urban data for crime prediction. In particular, we validate the existence of temporal-spatial correlations in crime and develop a principled approach to model these correlations into the coherent framework TCP for crime prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of temporal-spatial correlations in crime prediction.
AB - Crime prediction plays a crucial role in improving public security and reducing the financial loss of crimes. The vast majority of traditional algorithms performed the prediction by leveraging demographic data, which could fail to capture the dynamics of crimes in urban. In the era of big data, we have witnessed advanced ways to collect and integrate fine-grained urban, mobile, and public service data that contains various crime-related sources and rich temporal-spatial information. Such information provides better understandings about the dynamics of crimes and has potentials to advance crime prediction. In this paper, we exploit temporal-spatial correlations in urban data for crime prediction. In particular, we validate the existence of temporal-spatial correlations in crime and develop a principled approach to model these correlations into the coherent framework TCP for crime prediction. The experimental results on real-world data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of temporal-spatial correlations in crime prediction.
KW - Crime prediction
KW - Crime prevention
KW - Temporal-spatial correlation
UR - http://www.scopus.com/inward/record.url?scp=85037350982&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85037350982&origin=recordpage
U2 - 10.1145/3132847.3133024
DO - 10.1145/3132847.3133024
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781450349185
VL - Part F131841
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 497
EP - 506
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Y2 - 6 November 2017 through 10 November 2017
ER -