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Spatial−temporal forecast research of property crime under the driven of urban traffic factors

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Abstract

According to existing researches, robbery crime is remarkably impacted by urban transport development, road accessibility and complexity of land use. However, researches related to spatial−temporal data mining and forecasting on property crime driven by traffic factors are rare. The BP neural network model improved by genetic algorithm (GA−BP neural network), which utilize Machine Learning to mine the factor weights of the property crime, can describe these complicated relationships to forecast more effectively. This study collected spatialtemporal data of property crime that occurred at city A located in South China in the period from 2008 to 2012, filtered the factors of property crime by correlation analysis, and selected the neighbors by spatial autocorrelation. This study also standardized the data, with one year as the time window and 100m×100m as the spatial dimension. Property crime is forecasted based on the GA−BP neural network, and the root mean square error is 0.019. Current researches demonstrate that the correlation between property crime and factors, like urban transport, network density, floating population and economic indicator, is significant. This study confirms that the GA−BP neural network model based on GIS is a reasonable forecast model in the field of property crime forecast research. Given its features, the GA−BP neural network model can forecast other types of rational crime and can set the time window and spatial dimension to obtain the corresponding forecast result based on the accuracy of input data.

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Authors and Affiliations

  1. School of Geography, South China Normal University, Guangzhou, 510631, China

    Li Weihong, Wen Lei & Chen Yebin

Authors
  1. Li Weihong
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  2. Wen Lei
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  3. Chen Yebin
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Corresponding author

Correspondence to Li Weihong.

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Li Weihong, Associate Professor, specializing in criminal geographic research and spatial−temporal data mining.

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Weihong, L., Lei, W. & Yebin, C. Spatial−temporal forecast research of property crime under the driven of urban traffic factors. Multimed Tools Appl 75, 17669–17687 (2016). https://doi.org/10.1007/s11042-016-3467-2

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  • DOI: https://doi.org/10.1007/s11042-016-3467-2

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