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Journal of Intelligent Information Systems
Erschienen in: Journal of Intelligent Information Systems 3/2020

21.11.2019

Analysis of street crime predictors in web open data

verfasst von: Yihong Zhang, Panote Siriaraya, Yukiko Kawai, Adam Jatowt

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 3/2020

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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.
Vorheriger Artikel System for monitoring road slippery based on CCTV cameras and convolutional neural networks
Nächster Artikel Compacting frequent star patterns in RDF graphs

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Fußnoten
5
The entire data used for the analysis is available upon request.
 
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Metadaten
Titel
Analysis of street crime predictors in web open data
verfasst von
Yihong Zhang
Panote Siriaraya
Yukiko Kawai
Adam Jatowt
Publikationsdatum
21.11.2019
Verlag
Springer US
Erschienen in
Journal of Intelligent Information Systems / Ausgabe 3/2020
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-019-00587-4

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