摘要:
为了分析像素级社会经济活动的空间分布状况,以Landsat8和NPP-VIIRS夜间灯光影像为数据源,分别对北京市第一产业和第二、三产业GDP进行空间化操作。利用分类回归树( classification and regression tree ,CART)算法,通过Landsat8影像生成北京市的土地利用图,在分析第一产业GDP与土地利用类型面积相关性的基础上,构建了第一产业GDP与耕地面积的线性回归模型。建立了5种灯光指标与第二、三产业GDP的数学关系,通过相关性和回归分析确定第二、三产业GDP与综合灯光指数呈明显的幂函数关系。根据以上2种模型分别生成对应2类产业的像素级GDP密度图,再分别对其进行线性纠正并求和后制作出北京市500 m格网尺寸的GDP密度图。误差分析发现,第一产业GDP、第二、三产业GDP和GDP总量与实际统计值的平均相对误差分别为0.86%,0.61%和1.37%。结果表明,结合土地利用数据的NPP-VIIRS夜间灯光GDP空间化方法可以精确估算北京市GDP产值,反映北京市经济空间分布特征。
Abstract:
In order to analyze spatial distributions of socioeconomic activities at pixel scale , the authors used Landsat8 and NPP-VIIRS night -time light images as data sources and produced spatialization maps of primary industry GDP and the secondary , tertiary industry GDP in Beijing . The land use map of Beijing for the spatialization was produced from Landsat 8 image with CART decision-tree algorithm .According to the correlation results between the primary industry GDP and areas of land use , a linear regression model was built based on the primary industry GDP and areas of plough .By analyzing the correlation relationships between five light indexes and the secondary, tertiary industry GDP, compounded night light index (CNLI) and the secondary, tertiary industry GDP presented apparent power function ’ s correlation relationship .Using linear corrections and summation of two types of pixel level ’ s GDP density maps produced both modes listed above , and a total GDP density map was generated with the resolution of five hundred meters in Beijing .The results of GDP relative errors show that the primary industry GDP and the secondary , tertiary industry GDP were 0.86%, 0.61% and 1.37% respectively. This suggests that this approach of pixel level ’ s GDP spatialization can be applied to estimate Beijing ’ s GDP and reflect characteristics of its economic distribution .
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