Big spatial data for urban and environmental sustainability. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Huang, Bo; Wang, Jionghua
• Is Part Of: Geo-spatial information science. Volume 23:Issue 2(2020)
• Page Start: 125
• Page End: 140
• Publication Date: 2020-04-02
• Subjects: Big spatial data -- analytics -- review -- spatial modeling -- data fusion; Geographic information systems -- Periodicals; Cartography -- Data processing -- Periodicals; Surveying -- Data processing -- Periodicals; Remote sensing -- Periodicals; 526.0285
• DOI: 10.1080/10095020.2020.1754138
• Languages: English
• ISSNs: 1009-5020

Abstract

ABSTRACT: 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.