GDP Spatialization in China based on DMSP/OLS Data and Land Use Data
GDP is a key indicator of socioeconomic development,urban planning,and environmental protection,accurate estimates of the magnitude and spatial distribution of economic activity have many useful applications in resources and environmental sciences.Developing alternative methods may prove to be useful for making estimates of gross domestic product when other measures are of suspect accuracy or unavailable.Based on the summary and analysis of existing economic activity spatialization approaches,this paper explored the potential for spatializing GDP through China using night-time satellite imagery（DMSP/OLS） and land-use data.In creating the GDP linear regression model of secondary industry and tertiary industry,night-time light intensity and lit areas,under different types of land use,were employed as predictor variables,and the GDP statistical data was as dependent variable,meanwhile,model of primary industry based on the landuse data.To improve model performance,31 zones were created according to provincial administrative boundary.The model of primary industry is observed to have a correlation（R2） ranging from 0.7 to 0.95 in majority zones and R2 of secondary industry and tertiary industry modle is ranging from 0.8 to 0.98 in majority zones.A comparison of the results of this research with other researches shows that spatialized GDP density map,prepared on night-time imagery and land-use data,which reflects the GDP distribution characteristics more explicitly and greater detail.Meantime,the density map is significant sustainable economic development policies and basically explores the relationship between socioeconomic and regional ecological environment interaction.
DMSP/OLSLand use dataGDPRegression analysisSpatialization simulation
NETL
NSTL
万方数据
维普
