Simulation and spatialization of GDP in poverty areas based on night light imagery

doi:10.6046/gtzyyg.2016.02.26

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Abstract

Gross domestic products (GDP) can represent the economic development conditions of a region, and it is significant for poverty alleviation work to build the GDP spatial databases with high precision. In this paper, a regression model for DMSP/OLS night light data and GDP values in poverty areas was established in groups, and the GDP in continuous poverty areas were retrieved from the night light data. Then the GDP was divided into two parts. One is the GDP of primary industry, and the other is the GDP of second and tertiary industry. Finally, a spatial model for GDP of primary industry was established based on land use data and, in addition, the spatial model for GDP of other two kinds of industries was also built based on night light data. According to the results obtained, the estimation results of GDP are more accurate in most counties; the correlation coefficient between the estimated values and true values is 0.873 8 at the county level; the continuous poverty areas almost consist of GDP low-density areas; nevertheless, there are a few GDP high-density areas concentrated in towns; the GDP of most continuous poverty areas is less than 500 000 yuan per square kilometer except for the center of town poverty areas, and the GDP is even less than 50 000 yuan per square kilometer in the northwest territories. The economic status can be well reflected by the density distributions map of GDP in poverty areas, which can provide data support for poverty alleviation work.

Keywords glacier surface velocities D-InSAR multiple aperture interferometric(MAI) offset tracking ALOS PALSAR image Skyang glacier

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Issue Date: 14 April 2016

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	WANG Sisheng
	JIANG Liming
	SUN Yongling
	LIU Lin
	SUN Yafei
	WANG Hansheng

Cite this article:

WANG Sisheng,JIANG Liming,SUN Yongling, et al. Simulation and spatialization of GDP in poverty areas based on night light imagery[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 168-174.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.02.26 OR https://www.gtzyyg.com/EN/Y2016/V28/I2/168

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