Abstract
Advancement of technology in every aspect of our daily life has shaped an expanded analytical approach to crime. Crime is a foremost problem where the top priority has been concerned by the individual, the community and government. Increasing possibilities to track crime events give public organizations and police departments the opportunity to collect and store detailed data, including spatial and temporal information. Thus, exploratory analysis and data mining become an important part of the current methodology for the detection and forecasting of crime development. Spatiotemporal crime hotspots analysis is an approach to analyze and identify different crime patterns, relations, and trends in crime with identification of highly concentrated crime areas. In this paper spatiotemporal crime hotspots analysis using the dataset of the city of Chicago was done. First, we explored the spatiotemporal characteristics of crime in the city, secondary we explored the time series trend of top five crime types, Thirdly, the seasonal autoregressive integrated moving average model (SARIMA) based crime prediction model is presented and its result is compared to the one of the recently developed models based on deep learning algorithms for forecasting time series data, Long Short-Term Memory (LSTM). The results show that LSTM outperforms SARIMA model.
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References
Dubey, N., et al.: Int. J. Eng. Res. Appl. 4(3), 396–400 (2014). ISSN: 2248-9622 (Version 1)
Sathyadevan, S., Devan, M.S., Surya, G.S.: Crime analysis and prediction using data mining. In: International Conference on Networks & Soft Computing, pp. 406–412 (2014)
Thongtae, P., Srisuk, S.: An analysis of data mining applications in crime domain. In: International Conference on Computer and Information Technology Workshops, pp. 122–126 (2008)
Grover, V., Adderley, R., Bramer, M.: Review of current crime prediction techniques. In: Ellis, R., Allen, T., Tuson, A. (eds.) Applications and Innovations in Intelligent Systems. SGAI 2006, vol. 14, pp. 233–237. Springer, London (2007). https://doi.org/10.1007/978-1-84628-666-7_19
Liu, H., Brown, D.E.: Criminal incident prediction using a point-pattern- based density model. Int. J. Forecast. 19, 603–622 (2003)
Vold, G.B.: Prediction methods applied to problems of classification within institutions. J. Crim. Law Criminol. 26, 202–209 (1951)
Babakura, A., Sulaiman, M.N., Yusuf, M.A.: Improved method of classification algorithms for crime prediction. In: International Symposium on Biometrics and Security Technologies (ISBAST), pp. 250–255 (2014)
https://catalog.data.gov/dataset/crimes-2001-to-present-398a4
Brantingham, P.L., Brantingham, P.J., Vajihollahi, M., Wuschke, K.: Crime analysis at multiple scales of aggregation: a topological approach. In: Weisburd, D., Bernasco, W., Bruinsma, G.J. (eds.) Putting Crime in its Place, pp. 87–107. Springer, New York (2009). https://doi.org/10.1007/978-0-387-09688-9_4
Eck, J., Chainey, S., Cameron, J., Wilson, R.: Mapping crime: Understanding hotspots. National Institute of Justice, Washington DC (2005)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Egenhofer, M.J., Franzosa, R.D.: Point-set topological spatial relations. Int. J. Geogr. Inf. Syst. 5(2), 161–174 (1991)
Tseng, F.-M., Tzeng, G.-H.: A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Syst. 126, 367–376 (2002)
Acknowledgement
This work was supported by National Key Research and Development Program of China (2016YFC0803000), Beijing Municipal Science and Technology Projects under Grant (No. Z171100005117002).
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Ibrahim, N., Wang, S., Zhao, B. (2019). Spatiotemporal Crime Hotspots Analysis and Crime Occurrence Prediction. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_42
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DOI: https://doi.org/10.1007/978-3-030-35231-8_42
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