Using Crowdsourced Data to Study Crime and Place

David Buil-Gil and Reka Solymosi
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David Buil-Gil: University of Manchester

No 9ntk6, SocArXiv from Center for Open Science

Abstract: Crowdsourcing refers to the practise of enlisting the knowledge, experience or skills of a large number of people (the crowd) through some digital platform to collect data towards a collaborative project. Crowdsourcing can generate large volumes of data in relatively little time at a very small cost, and can be useful for research, strategic police management and many other purposes. To make effective use of crowdsourced data, it is important to understand its key strengths to emphasize, and limitations to mitigate. In this chapter we highlight the main strengths and weaknesses of crowdsourcing, and illustrate how to acquire, make sense of, and critically evaluate crowdsourced data to study crime and place. We present a step-by-step exemplar study using crowdsourced data from a platform called Place Pulse, where people rate their feelings of safety between different areas. Taking the case study of Atlanta, Georgia, we work through analyzing and interpreting these data while highlighting how to emphasize and evaluate the strengths and limitations of crowdsourcing. Exercises are presented using R software.

Date: 2020-08-29
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:9ntk6

DOI: 10.31219/osf.io/9ntk6

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