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Lookup NU author(s): Dr Husnain SheraziORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020 Institute of Electrical and Electronics Engineers Inc. All rights reserved. The primary objective of this study is to accumulate, summarize, and evaluate the state-ofthe-art for spatiooral crime hotspot detection and prediction techniques by conducting a systematic literature review (SLR). The authors were unable to find a comprehensive study on crime hotspot detection and prediction while conducting this SLR. Therefore, to the best of author's knowledge, this study is the premier attempt to critically analyze the existing literature along with presenting potential challenges faced by current crime hotspot detection and prediction systems. The SLR is conducted by thoroughly consulting top five scientific databases (such as IEEE, Science Direct, Springer, Scopus, and ACM), and synthesized 49 different studies on crime hotspot detection and prediction after critical review. This study unfolds the following major aspects: 1) the impact of data mining and machine learning approaches, especially clustering techniques in crime hotspot detection; 2) the utility of time series analysis techniques and deep learning techniques in crime trend prediction; 3) the inclusion of spatial and temporal information in crime datasets making the crime prediction systems more accurate and reliable; 4) the potential challenges faced by the state-of-the-art techniques and the future research directions. Moreover, the SLR aims to provide a core foundation for the research on spatiooral crime prediction applications while highlighting several challenges related to the accuracy of crime hotspot detection and prediction applications.
Author(s): Butt UM, Letchmunan S, Hassan FH, Ali M, Baqir A, Sherazi HHR
Publication type: Review
Publication status: Published
Journal: IEEE Access
Year: 2020
Volume: 8
Pages: 166553-166574
Online publication date: 08/09/2020
Acceptance date: 27/08/2020
ISSN (electronic): 2169-3536
Publisher: IEEE
URL: https://doi.org/10.1109/ACCESS.2020.3022808
DOI: 10.1109/ACCESS.2020.3022808
