Assuring the quality of volunteered geographic information

Michael F. Goodchild; Linna Li

doi:10.1016/j.spasta.2012.03.002

Assuring the quality of volunteered geographic information

Michael F. Goodchild, Linna Li

Research output: Contribution to journal › Article › peer-review

578 Scopus citations

Abstract

Volunteered geographic information (VGI) is a phenomenon of recent years, offering an alternative mechanism for the acquisition and compilation of geographic information. As such it offers substantial advantages, but suffers from a general lack of quality assurance. We discuss the issues involved in the determination of quality for geospatial data, and trace the history of research on VGI quality. We describe three approaches to quality assurance, which we term the crowd-sourcing, social, and geographic approaches respectively. We discuss the advantages and limitations of each, and the research that will be needed to operationalize the geographic approach.

Original language	English (US)
Pages (from-to)	110-120
Number of pages	11
Journal	Spatial Statistics
Volume	1
DOIs	https://doi.org/10.1016/j.spasta.2012.03.002
State	Published - May 2012
Externally published	Yes

Keywords

Crowdsourcing
Data quality
Geographic information
Volunteered geographic information

ASJC Scopus subject areas

Statistics and Probability
Computers in Earth Sciences
Management, Monitoring, Policy and Law

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10.1016/j.spasta.2012.03.002

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title = "Assuring the quality of volunteered geographic information",

abstract = "Volunteered geographic information (VGI) is a phenomenon of recent years, offering an alternative mechanism for the acquisition and compilation of geographic information. As such it offers substantial advantages, but suffers from a general lack of quality assurance. We discuss the issues involved in the determination of quality for geospatial data, and trace the history of research on VGI quality. We describe three approaches to quality assurance, which we term the crowd-sourcing, social, and geographic approaches respectively. We discuss the advantages and limitations of each, and the research that will be needed to operationalize the geographic approach.",

keywords = "Crowdsourcing, Data quality, Geographic information, Volunteered geographic information",

author = "Goodchild, {Michael F.} and Linna Li",

note = "Funding Information: The research reported in this paper is supported by the US National Science Foundation , award 0849910, and by the US Army Research Office , award W911NF-09-1-0302. Copyright: Copyright 2012 Elsevier B.V., All rights reserved.",

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doi = "10.1016/j.spasta.2012.03.002",

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AU - Goodchild, Michael F.

AU - Li, Linna

N1 - Funding Information: The research reported in this paper is supported by the US National Science Foundation , award 0849910, and by the US Army Research Office , award W911NF-09-1-0302. Copyright: Copyright 2012 Elsevier B.V., All rights reserved.

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N2 - Volunteered geographic information (VGI) is a phenomenon of recent years, offering an alternative mechanism for the acquisition and compilation of geographic information. As such it offers substantial advantages, but suffers from a general lack of quality assurance. We discuss the issues involved in the determination of quality for geospatial data, and trace the history of research on VGI quality. We describe three approaches to quality assurance, which we term the crowd-sourcing, social, and geographic approaches respectively. We discuss the advantages and limitations of each, and the research that will be needed to operationalize the geographic approach.

AB - Volunteered geographic information (VGI) is a phenomenon of recent years, offering an alternative mechanism for the acquisition and compilation of geographic information. As such it offers substantial advantages, but suffers from a general lack of quality assurance. We discuss the issues involved in the determination of quality for geospatial data, and trace the history of research on VGI quality. We describe three approaches to quality assurance, which we term the crowd-sourcing, social, and geographic approaches respectively. We discuss the advantages and limitations of each, and the research that will be needed to operationalize the geographic approach.

KW - Crowdsourcing

KW - Data quality

KW - Geographic information

KW - Volunteered geographic information

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JO - Spatial Statistics

JF - Spatial Statistics

ER -

Assuring the quality of volunteered geographic information

Abstract

Keywords

ASJC Scopus subject areas

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