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Analysis of street crime predictors in web open data

  • Yihong ZhangEmail author
  • Panote Siriaraya
  • Yukiko Kawai
  • Adam Jatowt
Article
  • 25 Downloads

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.

Keywords

Crime prediction Web open data Image and text analysis 

Notes

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Social Informatics, Graduate School of InformaticsKyoto UniversityKyotoJapan
  2. 2.Division of Frontier InformaticsKyoto Sangyo UniversityKyotoJapan

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