›› 2012, Vol. 14 ›› Issue (1): 128-136.doi: 10.3724/SP.J.1047.2012.00128

• ARTICLES • Previous Articles     Next Articles

GDP Spatialization in China Based on Nighttime Imagery

HAN Xiangdi1,2, ZHOU Yi1*, WANG Shixin1, LIU Rui1,2, YAO Yao1,2   

  1. 1. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, CAS, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2011-05-30 Revised:2011-12-09 Online:2012-02-25 Published:2012-02-24

Abstract: 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.

Key words: GDP, spatialization simulation, regression analysis, nighttime lights