Crime Forecasting Using Spatio-temporal Pattern with Ensemble Learning

  • Chung-Hsien Yu
  • Wei Ding
  • Ping Chen
  • Melissa Morabito
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chung-Hsien Yu
    • 1
  • Wei Ding
    • 1
  • Ping Chen
    • 1
  • Melissa Morabito
    • 2
  1. 1.University of Massachusetts BostonBostonUSA
  2. 2.University of Massachusetts LowellLowellUSA

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