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Crime Prediction of Bicycle Theft Based on Online Search Data

  • Ning DingEmail author
  • Yi-ming Zhai
  • Xiao-feng Hu
  • Ming-yuan Ma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

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.

Keywords

Baidu index Bicycle theft K-means Correlation analysis Multiple linear regression 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ning Ding
    • 1
    Email author
  • Yi-ming Zhai
    • 1
  • Xiao-feng Hu
    • 2
  • Ming-yuan Ma
    • 1
  1. 1.School of Criminal Investigation and CounterterrorismPeople’s Public Security University of ChinaBeijingChina
  2. 2.School of Information Technology and Network SecurityPeople’s Public Security University of ChinaBeijingChina

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