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Analysis of street crime predictors in web open data

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Abstract

Crime predictors have been sought after by governments and citizens alike for preventing or avoiding crimes. In this paper, we attempt to thoroughly analyze crime predictors from three Web open data sources: Google Street View (GSV), Twitter, and Foursquare, which provides visual, textual, and human behavioral data respectively. In contrast to existing works that attempt crime prediction at zip-code level or coarser granularity, we focus on street-level crime prediction. We transform data assigned to street-segments, and extract and determine strong predictors correlated with crime. Particularly, we are the first to discover visual clues on street outlooks that are predictive for crime. We focus on the city of San Francisco, and our extensive experiments show the effectiveness of predictors in a range of tests. We show that by analyzing and selecting strong predictors in Web open data, one could achieve significantly better crime prediction accuracy, comparing to traditional demographic data-based prediction.

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Notes

  1. https://en.wikipedia.org/wiki/Google_Street_View

  2. https://www.statista.com/statistics/303684/regional-twitter-user-distribution/

  3. http://www.internetlivestats.com/twitter-statistics/

  4. https://mashable.com/2009/11/19/foursquare-50-more-cities/

  5. The entire data used for the analysis is available upon request.

  6. https://wiki.openstreetmap.org/wiki/Stats

  7. https://wiki.openstreetmap.org/wiki/API

  8. https://www.qgis.org/en/site/

  9. https://catalog.data.gov/dataset/sfpd-incidents-from-1-january-2014

  10. https://datasf.org/opendata/

  11. https://developers.google.com/maps/documentation/streetview/

  12. https://developer.twitter.com/en/docs/tweets/filter-realtime/overview

  13. https://developer.foursquare.com/docs/venues/search

  14. https://www.tensorflow.org/tutorials/image_recognition

  15. https://nlp.stanford.edu/projects/glove/

  16. https://law.justia.com/codes/massachusetts/2010/partiv/titlei/

  17. http://www.cs.otago.ac.nz/staffpriv/ok/victimless.htm

  18. https://cran.r-project.org/web/packages/e1071/index.html

  19. https://www.census.gov/

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Authors and Affiliations

  1. Department of Social Informatics, Graduate School of Informatics, Kyoto University, Kyoto, Japan

    Yihong Zhang & Adam Jatowt

  2. Division of Frontier Informatics, Kyoto Sangyo University, Kyoto, Japan

    Panote Siriaraya & Yukiko Kawai

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  1. Yihong Zhang
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  2. Panote Siriaraya
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  3. Yukiko Kawai
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  4. Adam Jatowt
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Correspondence to Yihong Zhang.

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Zhang, Y., Siriaraya, P., Kawai, Y. et al. Analysis of street crime predictors in web open data. J Intell Inf Syst 55, 535–559 (2020). https://doi.org/10.1007/s10844-019-00587-4

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