Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1099-1104, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1099-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
14 Sep 2017
SPATIOTEMPORAL EVOLUTION OF THE IMBALANCED REGIONAL DEVELOPMENT IN MAINLAND CHINA USING DMSP-OLS DATA
K. Chen and T. Jia School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Keywords: DMSP-OLS, Correlation Analysis, Regional Development Center, Imbalanced Regional Development, Regional Development Gini Abstract. The Defense Meteorological Satellite Programs Operational Linescan System (DMSP-OLS) nighttime lights imagery has been widely used to monitor economic activities and regional development in recent decades. In this paper, we firstly processed the nighttime light imageries of the Mainland China from 1992 to 2013 due to the radiation or geometric errors. Secondly, by dividing the Mainland China into seven regions, we found high correlation between the sum light values and GDP of each region. Thirdly, we extracted the economic centers of each region based on their nighttime light images. Through the analysis, we found the distribution of these economic centers was relatively concentrated and the migration of these economic centers showed certain directional trend or circuitous changes, which suggested the imbalanced socio-economic development of each region. Then, we calculated the Regional Development Gini of each region using the nighttime light data, which indicated that social-economic development in South China presents great imbalance while it is relatively balanced in Southwest China. This study would benefit the macroeconomic control to regional economic development and the introduction of appropriate economic policies from the national level.
Conference paper (PDF, 2183 KB)


Citation: Chen, K. and Jia, T.: SPATIOTEMPORAL EVOLUTION OF THE IMBALANCED REGIONAL DEVELOPMENT IN MAINLAND CHINA USING DMSP-OLS DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1099-1104, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1099-2017, 2017.

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