Please use this identifier to cite or link to this item: https://doi.org/10.3390/rs13163142
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dc.titleIdentifying and classifying shrinking cities using long-term continuous night-time light time series
dc.contributor.authorDong, Baiyu
dc.contributor.authorYe, Yang
dc.contributor.authorYou, Shixue
dc.contributor.authorZheng, Qiming
dc.contributor.authorHuang, Lingyan
dc.contributor.authorZhu, Congmou
dc.contributor.authorTong, Cheng
dc.contributor.authorLi, Sinan
dc.contributor.authorLi, Yongjun
dc.contributor.authorWang, Ke
dc.date.accessioned2022-10-11T07:54:59Z
dc.date.available2022-10-11T07:54:59Z
dc.date.issued2021-08-08
dc.identifier.citationDong, Baiyu, Ye, Yang, You, Shixue, Zheng, Qiming, Huang, Lingyan, Zhu, Congmou, Tong, Cheng, Li, Sinan, Li, Yongjun, Wang, Ke (2021-08-08). Identifying and classifying shrinking cities using long-term continuous night-time light time series. Remote Sensing 13 (16) : 3142. ScholarBank@NUS Repository. https://doi.org/10.3390/rs13163142
dc.identifier.issn2072-4292
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232042
dc.description.abstractShrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by longterm continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
dc.publisherMDPI AG
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectCity shrinkage
dc.subjectGeographically weighted regression
dc.subjectNight-time light
dc.subjectRandom forest
dc.typeArticle
dc.contributor.departmentDEPT OF BIOLOGICAL SCIENCES
dc.description.doi10.3390/rs13163142
dc.description.sourcetitleRemote Sensing
dc.description.volume13
dc.description.issue16
dc.description.page3142
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