GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing Data
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Data Sources
3. Methods
3.1. Establishing Consistent Long NTL Time Series
3.1.1. Internal Calibration of DMSP/OLS
3.1.2. Cross-Sensor Calibration of DMSP/OLS and NPP/VIIRS
3.2. GDP Forecasting Model
3.2.1. Linear Regression (LR) Model
3.2.2. ARIMA Model
3.2.3. ARIMAX Model
3.2.4. SARIMA Model
3.3. Accuracy Evaluation
4. Results
4.1. The Calibration of the NTL
4.2. NTL–GDP Relationship and Model Evaluation
4.3. GDP Forecast in 2030
5. Discussion
5.1. Time Change of GDP
5.2. Spatial Variation of GDP
5.3. Limitation Analysis
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sensor | DMSP/OLS | Suomi NPP/VIIRS |
---|---|---|
Archive year | 1992–2013 | April 2012- |
Spatial resolution/m | 2700 | 740 |
Time resolution/h | 12 | 12 |
Country | America | America |
Data accessibility | Free annual video download, monthly average and daily video to order | Monthly average, daily video free download |
Year | F10 | F12 | F14 | F15 | F16 | F18 |
---|---|---|---|---|---|---|
1992 | F101992 | |||||
1993 | F101993 | |||||
1994 | F101994 | F121994 | ||||
1995 | F121995 | |||||
1996 | F121996 | |||||
1997 | F121997 | F141997 | ||||
1998 | F121998 | F141998 | ||||
1999 | F121999 | F141999 | ||||
2000 | F142000 | F152000 | ||||
2001 | F142001 | F152001 | ||||
2002 | F142002 | F152002 | ||||
2003 | F142003 | F152003 | ||||
2004 | F152004 | F162004 | ||||
2005 | F152005 | F162005 | ||||
2006 | F152006 | F162006 | ||||
2007 | F152007 | F162007 | ||||
2008 | F162008 | |||||
2009 | F162009 | |||||
2010 | F182010 | |||||
2011 | F182011 | |||||
2012 | F182012 | |||||
2013 | F182013 |
Year | Satellite 1 | Satellite 2 | Raw | Elvidge | RSR | ||
---|---|---|---|---|---|---|---|
1994 | F10 | F12 | 0.023 | 0.015 | 0.052 | ||
1997 | F12 | F14 | 0.532 | 0.017 | 0.008 | ||
1998 | F12 | F14 | 0.089 | 0.012 | 0.129 | ||
1999 | F12 | F14 | 0.077 | 0.054 | 0.099 | ||
2000 | F14 | F15 | 0.238 | 0.234 | 0.002 | ||
2001 | F14 | F15 | 0.361 | 0.084 | 0.088 | ||
2002 | F14 | F15 | 0.241 | 0.221 | 0.242 | ||
2003 | F14 | F15 | 0.086 | 0.118 | 0.206 | ||
2004 | F15 | F16 | 0.006 | 0.003 | 0.059 | ||
2005 | F15 | F16 | 0.079 | 0.108 | 0.064 | ||
2006 | F15 | F16 | 0.149 | 0.145 | 0.151 | ||
2007 | F15 | F16 | 0.150 | 0.119 | 0.008 | ||
2.033 | 1.132 | 1.108 |
Year | Trillion Yuan | Year | Trillion Yuan | Year | Trillion Yuan | |||
---|---|---|---|---|---|---|---|---|
Province | Province | Province | ||||||
Beijing | 62,832.69 | Tianjin | 36,746.19 | Hebei | 63,771.21 | |||
Shanxi | 19,321.30 | Neimenggu | 22,280.36 | Liaoning | 26,416.12 | |||
Jilin | 24,768.18 | Heilongjiang | 19,619.97 | Shanghai | 71,024.35 | |||
Jiangsu | 194,010.35 | Zhejiang | 108,359.54 | Anhui | 58,035.48 | |||
Fujian | 68,441.22 | Jiangxi | 46,614.7 | Shandong | 13,3058.04 | |||
Henan | 95,018.14 | Hubei | 76,278.04 | Hunan | 68,639.61 | |||
Guangdong | 193,447.95 | Guangxi | 39,520.92 | Hainan | 8959.36 | |||
Chongqing | 43,340.22 | Sichuan | 69,464.10 | Guizhou | 35,046.67 | |||
Yunnan | 31,157.92 | Xizang | 3454.22 | Shaanxi | 38,687.81 | |||
Gansu | 12,941.07 | Qinghai | 4720.20 | Ningxia | 6764.07 | |||
Xinjiang | 14,198.30 |
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Gu, Y.; Shao, Z.; Huang, X.; Cai, B. GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing Data. Remote Sens. 2022, 14, 3671. https://doi.org/10.3390/rs14153671
Gu Y, Shao Z, Huang X, Cai B. GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing Data. Remote Sensing. 2022; 14(15):3671. https://doi.org/10.3390/rs14153671
Chicago/Turabian StyleGu, Yan, Zhenfeng Shao, Xiao Huang, and Bowen Cai. 2022. "GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing Data" Remote Sensing 14, no. 15: 3671. https://doi.org/10.3390/rs14153671