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Crime Forecasting Using Spatio-temporal Pattern with Ensemble Learning

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 8444)


Crime forecasting is notoriously difficult. A crime incident is a multi-dimensional complex phenomenon that is closely associated with temporal, spatial, societal, and ecological factors. In an attempt to utilize all these factors in crime pattern formulation, we propose a new feature construction and feature selection framework for crime forecasting. A new concept of multi-dimensional feature denoted as spatio-temporal pattern, is constructed from local crime cluster distributions in different time periods at different granularity levels. We design and develop the Cluster-Confidence-Rate-Boosting (CCRBoost) algorithm to efficiently select relevant local spatio-temporal patterns to construct a global crime pattern from a training set. This global crime pattern is then used for future crime prediction. Using data from January 2006 to December 2009 from a police department in a northeastern city in the US, we evaluate the proposed framework on residential burglary prediction. The results show that the proposed CCRBoost algorithm has achieved about 80% on accuracy in predicting residential burglary using the grid cell of 800-meter by 800-meter in size as one single location.


  • Spatio-temporal Pattern
  • Crime Forecasting
  • Ensemble Learning
  • Boosting

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© 2014 Springer International Publishing Switzerland

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Yu, CH., Ding, W., Chen, P., Morabito, M. (2014). Crime Forecasting Using Spatio-temporal Pattern with Ensemble Learning. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8444. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06604-2

  • Online ISBN: 978-3-319-06605-9

  • eBook Packages: Computer ScienceComputer Science (R0)