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Canadian Conference on Artificial Intelligence

Canadian AI 2018: Advances in Artificial Intelligence pp 367–373Cite as

Predicting Crime Using Spatial Features

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10832)

Abstract

Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to retrieve open street map (OSM) spatial data. This study also proposes finding hotpoints extracted from crime hotspots area found by Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A spatial distance feature is then computed based on the position of different hotpoints for various types of crime and this value is used as a feature for classifiers. We test the engineered features in crime data from Royal Canadian Mounted Police of Halifax, NS. We observed a significant performance improvement in crime prediction using the new generated spatial features.

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Acknowledgments

The authors would like to thank NSERC, NS Health Authority and Injury Free Nova Scotia for financial and other supports.

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Authors and Affiliations

  1. Institute for Big Data Analytics, Dalhousie University, Halifax, Canada

    Fateha Khanam Bappee, Amílcar Soares Júnior & Stan Matwin

  2. Institute for Computer Science, Polish Academy of Sciences, Warsaw, Poland

    Stan Matwin

Authors
  1. Fateha Khanam Bappee
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  2. Amílcar Soares Júnior
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  3. Stan Matwin
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Corresponding author

Correspondence to Fateha Khanam Bappee .

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Editors and Affiliations

  1. Ryerson University, Toronto, Ontario, Canada

    Ebrahim Bagheri

  2. McGill University, Montréal, Québec, Canada

    Jackie C.K. Cheung

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Bappee, F.K., Soares Júnior, A., Matwin, S. (2018). Predicting Crime Using Spatial Features. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_42

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  • DOI: https://doi.org/10.1007/978-3-319-89656-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-89655-7

  • Online ISBN: 978-3-319-89656-4

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