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Author (up) Song, J.; Tong, X.; Wang, L.; Zhao, C.; Prishchepov, A.V. url  doi
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  Title Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach Type Journal Article
  Year 2019 Publication Landscape and Urban Planning Abbreviated Journal Landscape and Urban Planning  
  Volume 190 Issue Pages 103580  
  Keywords Remote Sensing; nighttime light; numerical methods  
  Abstract Spatial distribution information on population density is essential for understanding urban dynamics. In recent decades, remote sensing techniques have often been applied to assess population density, particularly night-time light data (NTL). However, such attempts have resulted in mapped population density at coarse/medium resolution, which often limits the applicability of such data for fine-scale territorial planning. The improved quality and availability of multi-source remote sensing imagery and location-based service data (LBS) (from mobile networks or social media) offers new potential for providing more accurate population information at the micro-scale level. In this paper, we developed a fine-scale population distribution mapping approach by combining the functional zones (FZ) mapped with high-resolution satellite images, NTL data, and LBS data. Considering the possible variations in the relationship between population distribution and nightlight brightness in functional zones, we tested and found spatial heterogeneity of the relationship between NTL and the population density of LBS samples. Geographically weighted regression (GWR) was thus implemented to test potential improvements to the mapping accuracy. The performance of the following four models was evaluated: only ordinary least squares regression (OLS), only GWR, OLS with functional zones (OLS&FZ) and GWR with functional zones (GWR&FZ). The results showed that NTL-based GWR&FZ was the most accurate and robust approach, with an accuracy of 0.71, while the mapped population density was at a unit of 30 m spatial resolution. The detailed population density maps developed in our approach can contribute to fine-scale urban planning, healthcare and emergency responses in many parts of the world.  
  Address Department of Geosciences and Natural Resource Management, University of Copenhagen, 1350 Copenhagen, Denmark; songjinchao08(at)163.com  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0169-2046 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2516  
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