Abstract
Crimes of various types are occurring in different areas of each country, almost every day. Hence, observing, predicting and preventing crimes is a crucial issue to ensure a peaceful and safe environment. Although several systems have been designed for analyzing crime related data, to our best knowledge, there is no system designed for travelers. This paper addresses this issue by proposing a decision support system named CPD-DSST (Crime Pattern Discovery Decision Support System for Travelers) that allows users to learn about crime occurrences in specific areas and provide suggestions to ensure travel safety. To discover and locate crimes, the system applies an efficient algorithm named Crime Classifying Discovery and Location (CCDL) based on multinomial logistic regression, and a Crime Rate Evaluation (CRE) algorithm, on spatio-temporal crime data. Experiments show that the proposed system can perform accurate predictions. Moreover, preliminary feedback indicates that the system is appreciated by users.
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Chi, H., Lin, Z., Jin, H., Xu, B., Qi, M.: A decision support system for detecting serial crimes. Knowl.-Based Syst. 123, 88–101 (2017)
Erdoğan, G., Stylianou, N., Vasilakis, C.: An open source decision support system for facility location analysis. Decis. Support Syst. 125, 113116 (2019)
Gerber, M.S.: Predicting crime using twitter and kernel density estimation. Decis. Support Syst. 61, 115–125 (2014)
Ghaseminejad, A.H., Brantingham, P.: An executive decision support system for longitudinal statistical analysis of crime and law enforcement performance crime analysis system pacific region (CASPR). In: 2010 IEEE International Conference on Intelligence and Security Informatics, pp. 1–6. IEEE (2010)
Kadar, C., Maculan, R., Feuerriegel, S.: Public decision support for low population density areas: an imbalance-aware hyper-ensemble for spatio-temporal crime prediction. Decis. Support Syst. 119, 107–117 (2019)
Ku, C.H., Leroy, G.: A decision support system: automated crime report analysis and classification for e-government. Gov. Inf. Q. 31(4), 534–544 (2014)
Li, M., Sui, R., Meng, Y., Yan, H.: A real-time fuzzy decision support system for alfalfa irrigation. Comput. Electron. Agric. 163, 104870 (2019)
of Maryland, U.: Global terrorism database (gtd). http://www.start.umd.edu/gtd
Win, K.N., Chen, J., Chen, Y., Fournier-Viger, P.: PCPD: a parallel crime pattern discovery system for large-scale spatiotemporal data based on fuzzy Clustering. Int. J. Fuzzy Syst. 21(6), 1961–1974 (2019). https://doi.org/10.1007/s40815-019-00673-3
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Win, K.N. et al. (2020). A Decision Support System to Provide Criminal Pattern Based Suggestions to Travelers. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_50
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DOI: https://doi.org/10.1007/978-3-030-55789-8_50
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