Back to Search Start Over

Analysis of street crime predictors in web open data.

Authors :
Zhang, Yihong
Siriaraya, Panote
Kawai, Yukiko
Jatowt, Adam
Source :
Journal of Intelligent Information Systems; Dec2020, Vol. 55 Issue 3, p535-559, 25p
Publication Year :
2020

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09259902
Volume :
55
Issue :
3
Database :
Complementary Index
Journal :
Journal of Intelligent Information Systems
Publication Type :
Academic Journal
Accession number :
146605483
Full Text :
https://doi.org/10.1007/s10844-019-00587-4