Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.landurbplan.2019.05.011 |
Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach | |
Song, Jinchao1; Tong, Xiaoye1,2; Wang, Lizhe3; Zhao, Chunli4,5; Prishchepov, Alexander V.1 | |
2019-10-01 | |
发表期刊 | LANDSCAPE AND URBAN PLANNING |
ISSN | 0169-2046 |
EISSN | 1872-6062 |
出版年 | 2019 |
卷号 | 190 |
文章类型 | Article |
语种 | 英语 |
国家 | Denmark; Peoples R China; Sweden |
英文摘要 | 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. |
英文关键词 | LBS Geographically weighted regression Land use Spatial heterogeneity Urban functional zone |
领域 | 资源环境 |
收录类别 | SCI-E ; SSCI |
WOS记录号 | WOS:000484871000033 |
WOS关键词 | GEOGRAPHICALLY WEIGHTED REGRESSION ; NIGHTTIME LIGHT DATA ; LAND-USE ; URBANIZATION ; CHINA ; ACCESSIBILITY ; CLASSIFICATION ; INEQUALITY ; EMISSIONS ; POLLUTION |
WOS类目 | Ecology ; Environmental Studies ; Geography ; Geography, Physical ; Regional & Urban Planning ; Urban Studies |
WOS研究方向 | Environmental Sciences & Ecology ; Geography ; Physical Geography ; Public Administration ; Urban Studies |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/187504 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Copenhagen, Dept Geosci & Nat Resource Management, DK-1350 Copenhagen, Denmark; 2.DHI GRAS, Agern Alle 5, DK-2970 Horsholm, Denmark; 3.China Univ Geosci, Wuhan 430074, Hubei, Peoples R China; 4.Lund Univ, Dept Technol & Soc, Fac Engn, LTH,Transport & Rd, S-22100 Lund, Sweden; 5.K2 Swedish Knowledge Ctr Publ Transport, Lund, Sweden |
推荐引用方式 GB/T 7714 | Song, Jinchao,Tong, Xiaoye,Wang, Lizhe,et al. Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach[J]. LANDSCAPE AND URBAN PLANNING,2019,190. |
APA | Song, Jinchao,Tong, Xiaoye,Wang, Lizhe,Zhao, Chunli,&Prishchepov, Alexander V..(2019).Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach.LANDSCAPE AND URBAN PLANNING,190. |
MLA | Song, Jinchao,et al."Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach".LANDSCAPE AND URBAN PLANNING 190(2019). |
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