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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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Correspondence to Fateha Khanam Bappee .

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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

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