A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growth Rate

County-level economic statistics estimation using remotely sensed data, such as nighttime light data, has various advantages over traditional methods. However, uncertainties in remotely sensed data, such as the saturation problem of the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NSL (nighttime stable lights) data, may influence the accuracy of this remote sensing-based method, and thus hinder its use. This study proposes a simple method to address the saturation phenomenon of nighttime light data using the GDP growth rate. Compared with other methods, the NSL data statistics obtained using the new method reflect the development of economics more accurately. We use this method to calibrate the DMSP-OLS NSL data from 1992 to 2013 to obtain the NSL density data for each county and linearly regress them with economic statistics from 2004 to 2013. Regression results show that lighting data is highly correlated with economic data. We then use the light data to further estimate the county-level GDP, and find that the estimated GDP is consistent with the authoritative GDP statistics. Our approach provides a reliable way to capture county-level economic development in different regions.

[1]  Zhaoxin Dai,et al.  The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels , 2017 .

[2]  Yi Lu,et al.  Does flattening government improve economic performance? Evidence from China , 2016 .

[3]  Zhifeng Liu,et al.  Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .

[4]  Cao Ziyan,et al.  Correction of DMSP/OLS Night-time Light Images and Its Application in China , 2015 .

[5]  W. Nordhaus,et al.  Using luminosity data as a proxy for economic statistics , 2011, Proceedings of the National Academy of Sciences.

[6]  G. Duranton,et al.  Micro-Foundations of Urban Agglomeration Economies , 2003 .

[7]  J. Henderson,et al.  Measuring Economic Growth from Outer Space , 2009, The American economic review.

[8]  L. Zhuo,et al.  An EVI-based method to reduce saturation of DMSP/OLS nighttime light data , 2015 .

[9]  Jianping Wu,et al.  Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data , 2014, Remote. Sens..

[10]  Haizheng Li,et al.  Human Capital, Economic Growth, and Regional Inequality in China , 2007, SSRN Electronic Journal.

[11]  C. Elvidge,et al.  Mapping City Lights With Nighttime Data from the DMSP Operational Linescan System , 1997 .

[12]  J. Henderson,et al.  Roads, Railroads, and Decentralization of Chinese Cities , 2017, Review of Economics and Statistics.

[13]  Xi Li,et al.  Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China , 2013, Remote. Sens..

[14]  Hongbin Li,et al.  Political turnover and economic performance: the incentive role of personnel control in China , 2005 .

[15]  L. Rui.  GDP Spatialization in China Based on Nighttime Imagery , 2012 .

[16]  Mikhail Zhizhin,et al.  A Fifteen Year Record of Global Natural Gas Flaring Derived from Satellite Data , 2009 .

[17]  Chen-Han Yang,et al.  Adaptive Non-Negative Geographically Weighted Regression for Population Density Estimation Based on Nighttime Light , 2019, ISPRS Int. J. Geo Inf..

[18]  Chao-yun Li,et al.  The Erroneous Use of China's Population and Per Capita Data: A Structured Review and Critical Test , 2017 .

[19]  Christopher Doll,et al.  Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery , 2010 .

[20]  Jian Peng,et al.  Intercalibration of DMSP-OLS night-time light data by the invariant region method , 2013 .

[21]  GDP Spatialization in China Based on Nighttime Imagery: GDP Spatialization in China Based on Nighttime Imagery , 2012 .

[22]  R. Bluhm,et al.  Top Lights - Bright Cities and Their Contribution to Economic Development , 2015 .

[23]  A. Storeygard.  Farther on Down the Road: Transport Costs, Trade and Urban Growth in Sub-Saharan Africa , 2013, The Review of economic studies.

[24]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[25]  Budhendra L. Bhaduri,et al.  A global poverty map derived from satellite data , 2009, Comput. Geosci..

[26]  Masanao Hara,et al.  A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery , 2012, IEEE Transactions on Geoscience and Remote Sensing.