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A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena
Zhang, Guiming1; Zhu, A-Xing2,3,4,5,6
2019-09-02
Source PublicationINTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
ISSN1365-8816
Volume33Issue:9Pages:1873-1893
Corresponding AuthorZhang, Guiming(guiming.zhang@du.edu)
AbstractVolunteered 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.
KeywordVolunteered geographic information (VGI) spatial bias sample representativeness predictive mapping habitat suitability mapping
DOI10.1080/13658816.2019.1615071
WOS KeywordSAMPLE SELECTION BIAS ; POINT PATTERN-ANALYSIS ; SPECIES DISTRIBUTIONS ; DISTRIBUTION MODELS ; CITIZEN DATA ; INFORMATION ; KNOWLEDGE ; IMPROVE ; INFERENCE ; REDUCE
Indexed BySCI
Language英语
Funding ProjectUniversity of Denver ; Department of Geography, University of Wisconsin-Madison
Funding OrganizationUniversity of Denver ; Department of Geography, University of Wisconsin-Madison
WOS Research AreaComputer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS SubjectComputer Science, Information Systems ; Geography ; Geography, Physical ; Information Science & Library Science
WOS IDWOS:000485047400009
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/69768
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhang, Guiming
Affiliation1.Univ Denver, Dept Geog & Environm, Denver, CO 80208 USA
2.Jiangsu Ctr Collaborat Innovat Geog Informat Res, Nanjing, Jiangsu, Peoples R China
3.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing, Jiangsu, Peoples R China
4.State Key Lab Cultivat Base Geog Environm Evolut, Nanjing, Jiangsu, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
6.Univ Wisconsin, Dept Geog, Madison, WI 53706 USA
Recommended Citation
GB/T 7714
Zhang, Guiming,Zhu, A-Xing. A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2019,33(9):1873-1893.
APA Zhang, Guiming,&Zhu, A-Xing.(2019).A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,33(9),1873-1893.
MLA Zhang, Guiming,et al."A representativeness-directed approach to mitigate spatial bias in VGI for the predictive mapping of geographic phenomena".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 33.9(2019):1873-1893.
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