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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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A multi-dimensional crime spatial pattern analysis and prediction model based on classification

  • Hajela, Gaurav (Department of Computer Science and Engineering, Maulana Azad National Institute of Technology) ;
  • Chawla, Meenu (Department of Computer Science and Engineering, Maulana Azad National Institute of Technology) ;
  • Rasool, Akhtar (Department of Computer Science and Engineering, Maulana Azad National Institute of Technology)
  • Received : 2019.12.05
  • Accepted : 2020.05.06
  • Published : 2021.04.15
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Abstract

This article presents a multi-dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification-based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime-prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real-world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

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References

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