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To Volunteer or to Contribute Locational Information? Towards Truth in Labeling for Crowdsourced Geographic Information

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Crowdsourcing Geographic Knowledge

Abstract

Geographers, planners, and others increasingly refer to crowdsourced data in geography as volunteered geographic information (VGI). But is volunteered the right adjective to use for all types of crowdsourced geographic information? This chapter examines this question by making the following distinction along an ethical line for crowdsourced data collection: data collected following an “opt-in” agreement is volunteered; data collected under an “opt-out” provision is contributed (CGI). Opt-in agreements provide some clarity and control in the collection and intended reuse of collected data. Opt-out agreements are, in comparison, very open-ended and begin with few, if any, possibilities to control data collection. The chapter suggests that distinguishing contributed crowdsourced data from volunteered crowdsourced data is important to start to understand the nature of sources of crowdsourced data of any provenance and to help begin to identify possible biases. In the concluding discussion, this chapter argues that the simple distinction between CGI and VGI is valuable for assessments of data’s fitness for use. Following the truth-in-labeling principle known for food products, differentiating between CGI and VGI is also helpful to identify cases where lax approaches or even malfeasance leads to inaccurate or biased crowdsourced data.

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Notes

  1. 1.

    1 http://www.apple.com/privacy

  2. 2.

    2 http://images.apple.com/legal/sla/docs/iphone.pdf

  3. 3.

    3 http://www.google.com/mapmaker/mapfiles/s/terms_mapmaker.html

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Acknowledgements

In addition to helpful comments and discussion at the 2011 workshop in Seattle, the comments of two anonymous reviewers and editors have been very helpful in improving this chapter.

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Correspondence to Francis Harvey .

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Harvey, F. (2013). To Volunteer or to Contribute Locational Information? Towards Truth in Labeling for Crowdsourced Geographic Information. In: Sui, D., Elwood, S., Goodchild, M. (eds) Crowdsourcing Geographic Knowledge. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4587-2_3

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