Crime Forecasting Using Spatio-temporal Pattern with Ensemble Learning View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Chung-Hsien Yu , Wei Ding , Ping Chen , Melissa Morabito

ABSTRACT

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. More... »

PAGES

174-185

References to SciGraph publications

  • 2002-03. Aoristic Signatures and the Spatio-Temporal Analysis of High Volume Crime Patterns in JOURNAL OF QUANTITATIVE CRIMINOLOGY
  • 1999-12. Improved Boosting Algorithms Using Confidence-rated Predictions in MACHINE LEARNING
  • 2002-09-20. Multiclass Alternating Decision Trees in MACHINE LEARNING: ECML 2002
  • Book

    TITLE

    Advances in Knowledge Discovery and Data Mining

    ISBN

    978-3-319-06604-2
    978-3-319-06605-9

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-06605-9_15

    DOI

    http://dx.doi.org/10.1007/978-3-319-06605-9_15

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1030451658


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