Big spatial data for urban and environmental sustainability

Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with c...

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Bibliographic Details
Published inGeo-spatial information science Vol. 23; no. 2; pp. 125 - 140
Main Authors Huang, Bo, Wang, Jionghua
Format Journal Article
LanguageEnglish
Published Wuhan Taylor & Francis 02.04.2020
Taylor & Francis Ltd
Taylor & Francis Group
Subjects
analytics
Big Data
Big spatial data
data fusion
Data integration
Land use
Machine learning
Population statistics
Remote sensing
review
Spatial data
spatial modeling
Sustainability
Sustainable development
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Summary:Eighty percent of big data are associated with spatial information, and thus are Big Spatial Data (BSD). BSD provides new and great opportunities to rework problems in urban and environmental sustainability with advanced BSD analytics. To fully leverage the advantages of BSD, it is integrated with conventional data (e.g. remote sensing images) and improved methods are developed. This paper introduces four case studies: (1) Detection of polycentric urban structures; (2) Evaluation of urban vibrancy; (3) Estimation of population exposure to PM2.5; and (4) Urban land-use classification via deep learning. The results provide evidence that integrated methods can harness the advantages of both traditional data and BSD. Meanwhile, they can also improve the effectiveness of big data itself. Finally, this study makes three key recommendations for the development of BSD with regards to data fusion, data and predicting analytics, and theoretical modeling.
ISSN:1009-5020
1993-5153
DOI:10.1080/10095020.2020.1754138