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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (2) : 81-87     DOI: 10.6046/gtzyyg.2020.02.11
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GDP estimation model of county areas based on NPP/VIIRS satellite nighttime light data
Chenyang QU1,2,3, Li ZHANG2,3, Mingquan WANG2,3, Maohua WANG2,3
1. School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
2. Key Laboratory of Low-Coal Conversion Science and Engineering, Chinese Academy of Sciences, Shanghai 201210, China
3. Shanghai Carbon Data Research Center, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
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Abstract  

Based on the NPP/VIIRS nighttime lighting data, the authors constructed a panel regression model to estimate the county GDP of some counties where the new high-speed railway was located in 2013—2018. In this paper, the NPP/VIIRS data was firstly based on the maximum estimation of the correction process, and the night light-GDP panel regression model was established for GDP estimation. The results show that, among the 25 counties, 16 counties have a correlation coefficient R2 of 0.9 or more. The R2 of the six county-level regions is between 0.85 and 0.9, which confirms that the NPP/VIIRS satellite nighttime lighting data changes and the economic growth of the county where the new high-speed railway station is located shows a good and long-term stable positive correlation. At the same time, the authors made a brief analysis of the impact of high-speed rail and county-level economic development, and argued that it is feasible for the panel data model to fit the NPP/VIIRS satellite nighttime lighting data and to estimate the GDP of the counties where the new high-speed railway is located.

Keywords NPP/VIIRS nighttime light data      new high-speed railway station      county economic estimation      panel data analysis     
:  TP79  
Issue Date: 18 June 2020
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Chenyang QU
Li ZHANG
Mingquan WANG
Maohua WANG
Cite this article:   
Chenyang QU,Li ZHANG,Mingquan WANG, et al. GDP estimation model of county areas based on NPP/VIIRS satellite nighttime light data[J]. Remote Sensing for Land & Resources, 2020, 32(2): 81-87.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.11     OR     https://www.gtzyyg.com/EN/Y2020/V32/I2/81
省 县级行政区域
贵州 安龙、册亨、盘州、兴义
云南 呈贡、富源、广南、陆良、罗平、弥勒、师宗、石林、宜良
广西 隆安、平果、田东、田林
四川 广汉、剑阁、江油、青川
陕西 城固、佛坪、宁强、洋县
Tab.1  Selected area name
Fig.1  Comparison of TNL average before and after correction
序列
阶数		 LLC检验 ADF-Fisher检验
零阶 一阶差分 零阶 一阶差分
TNL -2.124 5 -12.580 2 1.758 2 -18.526 7
GDP 1.513 5 -8.785 2 4.192 3 -11.226 7
Tab.2  Unit root test result of panel data
种类 Panel Group
v-统计量 rho-统计量 PP-统计量 ADF-统计量 rho-统计量 PP-统计量 ADF-统计量
GDP -0.691 4 -3.878 1 0.275 5 -3.936 6 0.511 2 -4.377 5 -1.834 8
Tab.3  Pedroni panel co-integration test result
省 县域 α β R2
安龙 -8.687 9 0.451 2 0.934 818

贵州		 册亨 -128.542 8 0.800 6 0.989 821
盘州 -7 475.443 6 1.445 7 0.913 424
兴义 -1 483.823 4 0.335 4 0.995 886
富源 -974.728 8 0.573 6 0.446 155
呈贡 79.291 2 0.145 1 0.898 707
广南 234.877 7 0.360 4 0.907 995
陆良 -404.317 6 0.726 0 0.961 307
云南 罗平 -873.509 1 0.765 8 0.826 104
弥勒 316.027 7 0.388 9 0.928 302
师宗 3 454.222 1 -0.940 6 0.916 207
石林 290.737 4 0.154 3 0.819 312
宜良 443.285 0 0.383 8 0.835 792
城固 100.488 1 0.744 7 0.970 234

陕西		 佛坪 30.623 7 0.185 4 0.658 605
宁强 -119.314 6 0.736 5 0.831 916
洋县 291.047 8 0.416 3 0.924 146
隆安 121.387 9 0.407 4 0.916 377

广西		 平果 -2127.954 9 1.031 8 0.929 706
田东 476.060 4 0.348 3 0.901 103
田林 -485.302 8 1.104 7 0.925 872
广汉 6 942.120 3 -0.739 4 0.539 354

四川		 剑阁 -581.125 3 5.370 6 0.994 888
江油 -4216.557 4 1.470 6 0.979 202
青川 -177.005 6 1.114 4 0.945 177
Tab.4  Panel regression model parameters for each area
Fig.2  Estimated GDP of each area
Fig.3  Comparison of GDP statistical predictions of four regression models
县域 模拟GDP/亿元 统计GDP/亿元 误差/%
册亨 59.54 58.12 2.385 0
盘州 596.52 596.5 0.003 4
兴义 446.34 450.07 -0.835 7
呈贡 248.76 249.9 -0.458 3
弥勒 298.63 300 -0.458 8
石林 85.46 85.6 -0.163 8
城固 252.31 260.4 -3.206 4
佛坪 9.81 10.8 -10.091 7
宁强 86.6 89.05 -2.829 1
洋县 131.33 130.1 0.936 6
广汉 407.43 438 -7.503 1
江油 431.59 428 0.831 8
青川 39.09 39.1 -0.025 6
总计 3 093.41 3 135.64 -1.346 8
Tab.5  Comparison of 2018 GDP forecast and statistical data of some areas
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