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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Online Access: | http://dx.doi.org/10.1080/10095020.2020.1754138 |
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doaj-ce571802777245619e2760ecf22effb42021-01-26T11:50:10ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532020-04-0123212514010.1080/10095020.2020.17541381754138Big spatial data for urban and environmental sustainabilityBo Huang0Jionghua Wang1The Chinese University of Hong KongThe Chinese University of Hong KongEighty 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.http://dx.doi.org/10.1080/10095020.2020.1754138big spatial dataanalyticsreviewspatial modelingdata fusion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bo Huang Jionghua Wang |
spellingShingle |
Bo Huang Jionghua Wang Big spatial data for urban and environmental sustainability Geo-spatial Information Science big spatial data analytics review spatial modeling data fusion |
author_facet |
Bo Huang Jionghua Wang |
author_sort |
Bo Huang |
title |
Big spatial data for urban and environmental sustainability |
title_short |
Big spatial data for urban and environmental sustainability |
title_full |
Big spatial data for urban and environmental sustainability |
title_fullStr |
Big spatial data for urban and environmental sustainability |
title_full_unstemmed |
Big spatial data for urban and environmental sustainability |
title_sort |
big spatial data for urban and environmental sustainability |
publisher |
Taylor & Francis Group |
series |
Geo-spatial Information Science |
issn |
1009-5020 1993-5153 |
publishDate |
2020-04-01 |
description |
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. |
topic |
big spatial data analytics review spatial modeling data fusion |
url |
http://dx.doi.org/10.1080/10095020.2020.1754138 |
work_keys_str_mv |
AT bohuang bigspatialdataforurbanandenvironmentalsustainability AT jionghuawang bigspatialdataforurbanandenvironmentalsustainability |
_version_ |
1724322819726114816 |