A likelihood-based spatial statistical transformation model (LBSSTM) of regional economic development using DMSP/OLS time-series nighttime light imagery

被引:19
作者
Li, Chang [1 ]
Li, Guie [1 ]
Zhu, Yujia [1 ]
Ge, Yong [2 ]
Kung, Hsiang-te [3 ]
Wu, Yijin [1 ]
机构
[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
基金
中国国家自然科学基金;
关键词
DMSP/OLS nighttime light data; Likelihood-based spatial statistical transformation model (LBSSTM); Time series analysis; Prediction; ESDA; Spatial cross correlation; ELECTRIC-POWER CONSUMPTION; MAP URBAN AREA; URBANIZATION DYNAMICS; SATELLITE DATA; OLS DATA; CHINA; SCALES; POPULATION; GROWTH;
D O I
10.1016/j.spasta.2017.03.004
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
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.
引用
收藏
页码:421 / 439
页数:19
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