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ABSTRACT
Volunteered geographic information (VGI) contains valuable field observations that represent the spatial distribution of geographic phenomena. As such, it has the potential to provide regularly updated low-cost field samples for predictively mapping the spatial variations of geographic phenomena. The predictive mapping of geographic phenomena often requires representative samples for high mapping accuracy, but samples consisting of VGI observations are often not representative as they concentrate on specific geographic areas (i.e. spatial bias) due to the opportunistic nature of voluntary observation efforts. In this article, we propose a representativeness-directed approach to mitigate spatial bias in VGI for predictive mapping. The proposed approach defines and quantifies sample representativeness by comparing the probability distributions of sample locations and the mapping area in the environmental covariate space. Spatial bias is mitigated by weighting the sample locations to maximize their representativeness. The approach is evaluated using species habit suitability mapping as a case study. The results show that the accuracy of predictive mapping using weighted sample locations is higher than using unweighted sample locations. A positive relationship between sample representativeness and mapping accuracy is also observed, suggesting that sample representativeness is a valid indicator of predictive mapping accuracy. This approach mitigates spatial bias in VGI to improve predictive mapping accuracy.
Acknowledgments
Supports to Guiming Zhang through the Faculty Startup Funds from the University of Denver and through the Whitbeck Graduate Dissertator Award from the Department of Geography, University of Wisconsin-Madison are greatly appreciated.
Disclosure statement
No potential conflict of interest was reported by the authors.
Supplementary material
supplemental data for this article can be accessed here.
Additional information
Notes on contributors
Guiming Zhang
Dr. Guiming Zhang is an assistant professor of GIScience in the Department of Geography and the Environment at the University of Denver. His research is focused on geospatial big data analytics and the related enabling geo-computation technologies. In particular, he is interested in volunteered geographic information (VGI) and its applications in mapping natural resources (e.g., wildlife habitat, soil).
A-Xing Zhu
Dr. A-Xing Zhu is a professor of Geography in the Department of Geography at the University of Wisconsin-Madison. He has extensive experience of applying GIS in environmental modeling and resource management (e.g., digital soil mapping, landslide susceptability mapping, and hydrological modeling).