The Representativeness and Spatial Bias of Volunteered Geographic Information: A Review

Authors

Guiming Zhang, University of Denver, University of Wisconsin-Madison
A-Xing Zhu, University of Wisconsin-Madison, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, State Key Laboratory Cultivation Base of Geographical Environment Evolution, State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences

Publication Date

7-26-2018

Document Type

Article

Organizational Units

Geography and the Environment

Keywords

Volunteered geographic information (VGI), Representativeness, Representative sample, Spatial bias, Sample selection bias

Abstract

Many applications of volunteered geographic information (VGI) involve inferring the properties of the underlying population from a sample consisting of VGI observations, i.e. VGI sample. The representativeness of VGI sample is crucial for deciding the fitness for use of VGI in such applications. Due to the volunteers’ opportunistic observation efforts, spatial distribution of VGI observations is often biased (i.e. spatial bias). This degrades the representativeness of VGI and impedes the quality of inference made from VGI. Extensive research has been conducted on assessing or assuring VGI quality from the perspective of the fundamental dimensions of spatial data quality. Yet, this perspective alone provides limited insights on the representativeness of VGI. Assessing VGI representativeness and developing novel approaches to accounting for spatial bias in VGI is in need for broadening the spectrum of VGI applications. This article offers a comprehensive survey of the scientific literature from various domains (ecology, statistics, machine learning, etc.) to summarize existing endeavors related to sample representativeness assessment and sample selection bias correction for enlightening the treatment of these issues in VGI applications.

Publication Statement

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Recommended Citation

Guiming Zhang & A-Xing Zhu (2018) The representativeness and spatial bias of volunteered geographic information: a review, Annals of GIS, 24:3, 151-162, doi: 10.1080/19475683.2018.1501607.