Resources and environmental sciences require greatly the spatialized socio-economic data sets which are always obtained from administrative regions at national or provincial level, and accurate estimates of the magnitude and spatial distribution of economic activity have many useful applications. Developing alternative methods for making estimates of gross domestic product (GDP) may prove to be useful when other measures are of suspect accuracy or unavailable. Based on the summary and analysis of existing economic activity spatialization approaches and nighttime imagery applications in economic activity, this research explores the potential for estimating the GDP using relationship between the spatial patterns of nighttime satellite imagery and GDP in China by correlation analysis and regression analysis using concerned data processing software. With the regional differences of China's economic development, logarithmic regression models have been established between different night light indexes and GDP, primary industry, secondary industry, tertiary industry and the sum of secondary industry and tertiary industry at the provincial level. A clear logarithmic linear relationship between nightlight imagery and GDP, especially the correlation coefficient of night light index and the sum of secondary industry and tertiary industry is 0.824 and R2 of them is 0.679 at national level, suggests that this method is available and feasible to estimate the spatial distribution of economic activity such as GDP. The result, 1-km grid GDP map of China based on nighttime light data, by comparing with the other GDP spatialization approaches, shows the obvious advantage to reflect complete details and characteristics of the national secondary industry and tertiary industry distribution, which is extending the field of nighttime light data research and applications for the socio-economic data in resources and environmental sciences.
HAN Xiangdi, ZHOU Yi, WANG Shixin, LIU Rui, YAO Yao
. GDP Spatialization in China Based on Nighttime Imagery[J]. Journal of Geo-information Science, 2012
, 14(1)
: 128
-136
.
DOI: 10.3724/SP.J.1047.2012.00128
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