Crime sensing with Big Data: the affordances and limitations of using open-source communications to estimate crime patterns

This paper critically examines the affordances and limitations of big data for the study of crime and disorder. We hypothesize that disorder-related posts on Twitter are associated with actual police crime rates. Our results provide evidence that naturally occurring social media data may provide an...

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Bibliographic Details
Main Author: Williams, Matthew L. (Author)
Contributors: Burnap, Pete ; Sloan, Luke
Format: Electronic Article
Language:English
Published: 2017
In: The British journal of criminology
Online Access: Presumably Free Access
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Summary:This paper critically examines the affordances and limitations of big data for the study of crime and disorder. We hypothesize that disorder-related posts on Twitter are associated with actual police crime rates. Our results provide evidence that naturally occurring social media data may provide an alternative information source on the crime problem. This paper adds to the emerging field of computational criminology and big data in four ways: (1) it estimates the utility of social media data to explain variance in offline crime patterns; (2) it provides the first evidence of the estimation offline crime patterns using a measure of broken windows found in the textual content of social media communications; (3) it tests if the bias present in offline perceptions of disorder is present in online communications; and (4) it takes the results of experiments to critically engage with debates on big data and crime prediction.
ISSN:1464-3529
DOI:10.1093/bjc/azw031