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Crime sensing with big data: the affordances and limitations of using open source communications to estimate crime patterns

Williams, Matthew Leighton, Burnap, Peter and Sloan, Luke 2016. Crime sensing with big data: the affordances and limitations of using open source communications to estimate crime patterns. British Journal of Criminology 10.1093/bjc/azw031

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Abstract

This paper critically examines the affordances and limitations of big data for the study of crime and disorder. We hypothesise 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: i) it estimates the utility of social media data to explain variance in offline crime patterns; ii) 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; iii) it tests if the bias present in offline perceptions of disorder is present in online communications; and iv) it takes the results of experiments to critically engage with debates on big data and crime prediction.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Social Sciences (Includes Criminology and Education)
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Computer software
Publisher: Oxford University Press
Funders: Economic and Social Research Council
Last Modified: 03 Feb 2017 05:30
URI: http://orca-mwe.cf.ac.uk/id/eprint/87031

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