Abstract
In today’s big data era, the development of the internet provides new ideas for analyzing and forecasting various types of criminal activities. The huge number of bicycle theft crime in China has become a social security problem that has to be solved urgently in our country. Can we use of internet search behavior to predict the trend of bicycle theft? Cluster analysis, correlation analysis and linear regression method are utilized to analyze the daily time series of bicycle theft in Beijing from 2012 to 2016 and the relationship between online search data, Taobao bicycle sales data and theft of bicycle theft. The results show that the crime of bicycle theft is mainly concentrated in the summer and autumn and the crime is the lowest near the New Year. What’s more, in the morning of the day is the high incidence of such cases. The correlations between bicycle theft and the Baidu Index, Taobao bicycle sales data are significantly strong. The established multivariate linear regression model has a R-square of 0.804. The research provides an effective new idea for predicting the crime of bicycle theft and the tendency of other types of crime and provides the basis for the intelligence judgment and police dispatch.
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References
Guo, F., Li, C., Zhao, Q.: Empirical analysis of bicycle theft scenarios — a case study of H in second-tier cities. J. GANSU Police Vocat. Coll. 12(02), 48–53 (2014)
Huaon: 2017-2022 China Bicycle Industry Market Research and Investment Analysis Report. Beijing Aikaidete Consulting Co., Ltd., Beijing, China (2017)
Yu, D.: Current status and governance of bicycle theft activities. Journal Of Jiangxi Public Security College (02), 84–87 (2008)
Levy, J.M., Irvin-Erickson, Y., La Vigne, N.: A case study of bicycle theft on the Washington DC Metrorail system using a Routine Activities and Crime Pattern theory framework. Secur. J., 1–21 (2017)
Chen, P., Lu, Y.: Exploring co-offending networks by considering geographic background: an investigation of electric bicycle thefts in Beijing. Prof. Geogr. 70(1), 73–83 (2017). https://doi.org/10.1080/00330124.2017.1325753
Ji, Y., Fan, Y., Ermagun, A., Cao, X., Wang, W., Das, K.: Public bicycle as a feeder mode to rail transit in China: the role of gender, age, income, trip purpose, and bicycle theft experience. Int. J. Sustain. Transp. 11(4), 308–317 (2017)
Wang, Y.: A study of internet attention of public culture service system–taking baidu index as example. J. Mod. Inf. 37(01), 37–40 (2017)
Li, Y., Wen, R., Yang, L.: The relationship between the online search data and the automobile sales–based on the keywords by text mining. J. Mod. Inf. 36(08), 131–136 (2016)
Johnson, H.A., Wagner, M.M., Hogan, W.R., Chapman, W.W., Olszewski, R.T., Dowling, J.N., Barnas, G.: Analysis of Web access logs for surveillance of influenza., 20041202-1206 (2004)
Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012–1014 (2009)
Xiang, Y.: Could Baidu Index Forecast the Trend of Stock? Master, Southwestern University of Finance and Economics (2016)
Liu, T., Xu, X.: Can internet search behavior help to forecast the macro economy? Econ. Res. J. (12), 68–83 (2015)
Chen, T., Lin, J.: Comparative analysis of temporal - spatial evolution of online public opinion based on search engine attention–cases of google trends and Baidu Index. J. Intell. 32(03), 7–10 (2013)
Cohen, L.E., Felson, M.: Social change and crime rate trends: a routine activity approach. Am. Sociol. Rev. 44(4), 588–608 (1979)
Santos, R.B.: Crime analysis with crime mapping. Sage Publications (2016)
Li, S.: Cluster analysis on the influence evaluation of crowdsourcing network platform in China. J. Intell. 36(08), 144–149 (2017)
Zhang, D., He, Y.: Study on SNS network based on cluster analysis. J. Intell. 31(05), 62–65 (2012)
Cao, G., Zhao, A.: Positive study on evaluation and comparison of government affairs micro - blog influence: based on factor analysis and cluster analysis. J. Intell. 33(03), 107–112 (2014)
Murray, A.T., Estivill-Castro, V.: Cluster discovery techniques for exploratory spatial data analysis. Int. J. Geogr. Inf. Sci. 12(5), 431–443 (1998)
Murray, A.T., McGuffog, I., Western, J.S., Mullins, P.: Exploratory spatial data analysis techniques for examining urban crime: Implications for evaluating treatment. Br. J. Criminol. 41(2), 309–329 (2001)
Nath, S.V.: Crime pattern detection using data mining, pp. 41–44. IEEE (2006)
Cornish, D.B., Clarke, R.V.: Understanding crime displacement: an application of rational choice theory. Criminology 25(4), 933–948 (1987). https://doi.org/10.1111/j.1745-9125.1987.tb00826.x
Peng, Z.: Big data: the magic for intelligence - led policing to come true. J. Intell. 34(05), 1–6 (2015)
Zhang, L.: The paradigm change of public security intelligence studies from the perspective of “Big Data”. J. Intell. 34(07), 9–12 (2015)
Su, Y., Wu, H., Chen, Y., Hu, W.: Using CCLM to promote the accuracy of intelligent sentiment analysis classifier for chinese social media service. J. Netw. Intell. 3(2), 113–125 (2018)
Acknowledgement
This work is supported by basic research program of People’s Public Security University of China (No. 2018XKZTHY16) (No. 2016JKF01307) and National Key R&D Program of China (No. 2017YFC0803300).
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Ding, N., Zhai, Ym., Hu, Xf., Ma, My. (2019). Crime Prediction of Bicycle Theft Based on Online Search Data. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_13
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