A likelihood-based spatial statistical transformation model (LBSSTM) of regional economic development using DMSP/OLS time-series nighttime light imagery
Li, Chang1; Li, Guie1; Zhu, Yujia1; Ge, Yong2; Kung, Hsiang-te3; Wu, Yijin1
刊名SPATIAL STATISTICS
2017-08-01
卷号21页码:421-439
关键词DMSP/OLS nighttime light data Likelihood-based spatial statistical transformation model (LBSSTM) Time series analysis Prediction ESDA Spatial cross correlation
ISSN号2211-6753
DOI10.1016/j.spasta.2017.03.004
通讯作者Wu, Yijin(shaka_li@qq.com)
英文摘要In a regional economy, the central city of a metropolitan area has a radiative effect and an accumulative effect on its surrounding cities. Considering the limitations of traditional data sources (e.g., its subjectivity) and the advantages of nighttime light data, including its objectivity, availability and cyclicity, this paper proposes a likelihood spatial statistical transformation model (LBSSTM) to invert for the gross domestic product (GDP) of the surrounding cities, using time series of Sum of Lights (SOL) data covering the central city and taking advantage of the economic and spatial association between the central city and the surrounding cities within a metropolitan area and the correlation between SOL and GDP. The Wuhan Metropolitan Area is chosen to verify the model using time series analysis and exploratory spatial data analysis (ESDA). The experimental results show the feasibility of the proposed LBSSTM. The prediction accuracy of our model is verified by cross-validation using data from 1998, 2004 and 2011, based on the 3 sigma rule. This model can quantitatively express the agglomeration and diffusion effect of the central city and reveal the spatial pattern of this effect. The results of this work are potentially useful in making spatiotemporal economic projections and filling in missing data from some regions, as well as gaining a deeper quantitative and spatio-temporal understanding of the laws underlying regional economic development. (C) 2017 Elsevier B.V. All rights reserved.
资助项目National Natural Science Foundation of China (NSFC)[41101407] ; Morning Plan of WuHan city[2016070204010137] ; Natural Science Foundation of Hubei Province, China[2014CFB377] ; Natural Science Foundation of Hubei Province, China[2010CDZ005] ; CCNU from the colleges' basic research and operation of MOE[CCNU15A02001] ; National undergraduate training programs for innovation and entrepreneurship[201410511037]
WOS关键词ELECTRIC-POWER CONSUMPTION ; MAP URBAN AREA ; URBANIZATION DYNAMICS ; SATELLITE DATA ; OLS DATA ; CHINA ; SCALES ; POPULATION ; GROWTH
WOS研究方向Geology ; Mathematics ; Remote Sensing
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:000410649600007
资助机构National Natural Science Foundation of China (NSFC) ; Morning Plan of WuHan city ; Natural Science Foundation of Hubei Province, China ; CCNU from the colleges' basic research and operation of MOE ; National undergraduate training programs for innovation and entrepreneurship
内容类型期刊论文
源URL[http://ir.igsnrr.ac.cn/handle/311030/62002]  
专题中国科学院地理科学与资源研究所
通讯作者Wu, Yijin
作者单位1.Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Coll Urban & Environm Sci, Wuhan, Hubei, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Univ Memphis, Dept Earth Sci, Memphis, TN 38152 USA
推荐引用方式
GB/T 7714
Li, Chang,Li, Guie,Zhu, Yujia,et al. A likelihood-based spatial statistical transformation model (LBSSTM) of regional economic development using DMSP/OLS time-series nighttime light imagery[J]. SPATIAL STATISTICS,2017,21:421-439.
APA Li, Chang,Li, Guie,Zhu, Yujia,Ge, Yong,Kung, Hsiang-te,&Wu, Yijin.(2017).A likelihood-based spatial statistical transformation model (LBSSTM) of regional economic development using DMSP/OLS time-series nighttime light imagery.SPATIAL STATISTICS,21,421-439.
MLA Li, Chang,et al."A likelihood-based spatial statistical transformation model (LBSSTM) of regional economic development using DMSP/OLS time-series nighttime light imagery".SPATIAL STATISTICS 21(2017):421-439.
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