# The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area and Data

#### 2.1. Study Area

#### 2.2. Data Collection

## 3. Methods

#### 3.1. Correction of the NPP/VIIRS Nighttime Light Data

#### 3.2. Simulation Model

^{2}denotes the percentage of GDP estimation explained by TNL. The correlation coefficients R and the R

^{2}can be used to measure the correlation between TNL and GDP (which is the city’s statistical data) for each administrative region.

## 4. Results

^{2}, the RE, and the RRMSE (Table 1).

#### 4.1. Nighttime Light Images

#### 4.2. Suitability of Nighttime Light Data

^{2}based on NPP/VIIRS nighttime light data are higher, and the residual RE and RRMSE values are lower. The lower the RRMSE, the better the regression function’s fitting accuracy.

^{2}, RE, RRMSE) are approximately the same at different scales, but are all superior to the same indices based on DMSP/OLS data.

#### 4.3. Suitability of Spatial Scale

^{2}and lower values of RE and RRMSE when using the same nighttime light data and the same model (all the R values have a level of significance p < 0.001).

#### 4.4. Suitability of Fitting Function Model

#### 4.5. Suitability of Different City Regions

## 5. Discussion

#### 5.1. The Influence of Nighttime Light Image Resolution and the Spatial Scales of Analysis

^{5}. Its high radiation resolution means that the NPP/VIIRS product has a good ability to capture the ground information using different brightness levels (high, medium, or low). Especially at the high brightness level, the light value may not reach saturation, which represents a big improvement over DMSP/OLS data. For example, even in relatively developed cities, such as Beijing, Shanghai, Tianjin, Nanjing, and Hangzhou, the corresponding RE values based on NPP/VIIRS data are −26.3%, 0.90%, −8.6%, −0.14%, and 5.3%, but the values based on DMSP/OLS data are higher with −42.3%, −54.6%, −24.1%, −31.6%, and −23.1%, respectively. Therefore, GDP estimation based on NPP/VIIRS data usually will not be underestimated due to lighting saturation, and these GDP estimates have a higher accuracy.

#### 5.2. The Influence of Land Cover Patterns

#### 5.3. The Influence of Regional Industrial Structures

## 6. Conclusions

- (1)
- DMSP/OLS nighttime light data can be used in GDP estimation at the provincial scale but may be not suitable at the city level scale. NPP/VIIRS nighttime light data are usually suitable for GDP estimation at both the provincial scale and the city-level scale.
- (2)
- For GDP estimation at the provincial scale, the results based on different models display no apparent differences. However, at the city level scale, the accuracy of GDP estimation using the exponential model and the polynomial model is better than the accuracy of GDP estimation using the linear regression model.
- (3)
- The RE values of GDP estimation in each city, based on the DMSP/OLS data, display no obvious spatial distribution pattern, but the spatial distribution of RE displays a regular pattern in each city based on the NPP/VIIRS data. Specifically, the absolute value of RE for GDP estimation gradually declines from the west to the east, which implies that it is more suitable for GDP estimation to use the NPP/VIIRS nighttime light data in the eastern part of Mainland China. However, the unified national model for GDP prediction based on the NPP/VIIRS data is usually not suitable for most of western China.
- (4)
- The GDP estimation accuracy is influenced by the spatial and radiation resolution of the nighttime light data, as well as the spatial scale, the characteristics of the terrain and landforms, the landscape, and the industrial structure of the study area. Generally, higher spatial and radiation resolutions of the nighttime light data result in better accuracy in GDP estimation. The cities with moderate to high elevations, low vegetation coverage, and large amounts of exposure surfaces often have their GDP overestimated; the cities with extremely high elevation, high vegetation coverage, or large amounts of water are prone to GDP underestimation. The cities with low economic development or with large amounts of secondary industry (especially the energy industry) are also characterized by overestimation, but in the regions where the economy mainly relies on primary industries, the GDP is usually underestimated.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Xu, K.N.; Chen, F.L.; Liu, X.Y. The truth of China Economic Growth: Evidence from Global Night-time Light Data. Econ. Res. J.
**2015**, 9, 17–29. (In Chinese) [Google Scholar] - Yue, W.Z.; Gao, J.B.; Yang, X.C. Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A case study in Zhejiang Province, East China. Remote Sens.
**2014**, 6, 7260–7275. [Google Scholar] - Hu, Y.F.; Wang, Q.Q.; Liu, Y.; Li, J.; Ren, W.B. Index System and Transferring Methods to build the National Society and Economy Grid Database. Geo-Inf. Sci.
**2011**, 13, 573–578. (In Chinese) [Google Scholar] [CrossRef] - Mao, W.H.; Hu, D.Y.; Cao, R.; Deng, L. Monitoring urban expansion of Zhejiang Province using MODIS/EVI data products and DMSP/OLS nighttime light data. Geogr. Res.
**2013**, 32, 1325–1335. (In Chinese) [Google Scholar] - Chen, X.; Nordhaus, W.D. Using Luminosity Data as a Proxy for Economic Statistics. Proc. Natl. Acad. Sci. USA
**2011**, 108, 8589–8594. [Google Scholar] [CrossRef] [PubMed] - He, C.Y.; Li, J.G.; Chen, J.; Shi, P.J.; Pan, Y.Z.; Li, J.; Ichinose, T. The urbanization model and process in Bohai Sea surrounding area in the 1990s by using DMSP/OLS data. Acta Geogr. Sin.
**2005**, 60, 409–417. [Google Scholar] - Li, D.R.; Li, X. An overview on data mining of nighttime light remote sensing. Acta Geod. Cartogr. Sin.
**2015**, 44, 591–601. (In Chinese) [Google Scholar] - Wu, J.S.; Liu, H.; Peng, J.; Ma, L. Hierarchical structure and spatial pattern of China’s urban system: Evidence from DMSP/OLS nightlight data. Acta Geogr. Sin.
**2014**, 69, 759–770. [Google Scholar] - Zhuo, L.; Li, Q.; Shi, P.J.; Chen, J.; Zheng, J.; Li, X. Identification and characteristics analysis of urban land expansion types in China in the 1990s using DMSP/OLS data. Acta Geogr. Sin.
**2016**, 61, 169–178. [Google Scholar] - Zhuo, L.; Chen, J.; Shi, P.J.; Gu, Z.H.; Fan, Y.D.; Ichinose, T. Modeling population density of China in 1998 based on DMSP/OLS nighttime light image. Acta Geogr. Sin.
**2005**, 60, 266–276. [Google Scholar] - Yang, X.C.; Yue, W.Z.; Gao, D.W. Spatial improvement of Human Population Distribution Based on Multi-Sensor Remote-sensing Data: An Input for Exposure Assessment. Int. J. Remote Sens.
**2013**, 34, 5569–5583. [Google Scholar] [CrossRef] - Forbes, D.J. Multi-scale Analysis of the Relationship between Economic Statistics and DMSP/OLS Night Light Images. GISci. Remote Sens.
**2013**, 50, 483–499. [Google Scholar] - Elvidge, C.D.; Baugh, K.E.; Kihn, E.A.; Kroehl, H.W.; Davis, E.R.; Davis, C.W. Relation between satellite observed visible near infrared emissions, population, economic activity and electric power consumption. Int. J. Remote Sens.
**1997**, 18, 1373–1379. [Google Scholar] [CrossRef] - Ghost, T.; Powell, R.; Elvidge, C.D.; Baugh, K.E.; Sutton, P.C.; Anderson, S. Shedding light on the global distribution of economic activity. Open Geogr. J.
**2010**, 3, 148–161. [Google Scholar] - He, C.Y.; Ma, Q.; Liu, Z.F.; Zhang, X.F. Modeling the Spatiotemporal Dynamics of Electric Power Consumption in Mainland China Using Saturation-corrected DMSP/OLS Nighttime Stable Light Data. Int. J. Digit. Earth
**2014**, 7, 993–1014. [Google Scholar] [CrossRef] - Han, X.D.; Zhou, Y.; Wang, S.X.; Liu, R.; Yao, Y. GDP spatialization in China based on nighttime imagery. J. Geo-Inf. Sci.
**2012**, 14, 128–136. (In Chinese) [Google Scholar] [CrossRef] - Doll, C.N.H.; Muller, J.P.; Morley, J.G. Mapping regional economic activity from night-time light satellite imagery. Ecol. Econ.
**2006**, 57, 75–92. [Google Scholar] [CrossRef] - Han, X.D.; Zhou, Y.; Wang, S.X.; Liu, R.; Yao, Y. GDP spatialization in China based on DMSP/OLS data and land use data. Remote Sens. Technol. Appl.
**2012**, 27, 396–405. (In Chinese) [Google Scholar] - Zhang, Q.L.; Steo, K.C. Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sens. Environ.
**2011**, 115, 2320–2329. [Google Scholar] [CrossRef] - Ma, T.; Zhou, C.H.; Pei, T. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China’s cities. Remote Sens. Environ.
**2012**, 124, 99–107. [Google Scholar] - Li, X.; Xu, H.M.; Chen, X.L.; Li, C. Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China. Remote Sens.
**2013**, 5, 3057–3081. [Google Scholar] [CrossRef] - Liang, Y.J.; Xu, Z.M. Modeling the Spatial distribution of GDP based on night light radiation: A case study in Ganzhou district, Zhangye Municipality. J. Glaciol. Geocrol.
**2013**, 35, 249–254. [Google Scholar] - Tian, Y.Z.; Yue, T.X.; Zhu, L.F.; Clinton, N. Modeling population density using land cover data. Ecol. Model.
**2005**, 189, 72–88. [Google Scholar] [CrossRef] - Zandbergen, P.A.; Ignizio, D.A. Comparison of dasymetric mapping techniques for small-area population estimates. Cartogr. Geogr. Inf. Sci.
**2010**, 37, 199–214. [Google Scholar] [CrossRef] - Wu, J.S.; Wang, Z.; Li, W.F. Exploring factors affecting the relationship between light consumption and GDP based on DMSP/OLS nighttime satellite imagery. Remote Sens. Environ.
**2013**, 134, 111–119. [Google Scholar] [CrossRef] - Lo, C.P. Modeling the population of China using DMSP operational linescan system nighttime data. Remote Sens.
**2001**, 67, 1037–1047. [Google Scholar] - Huang, H.Q.; Wang, Y.L.; Hu, B.Q.; Li, L. Study of spatialization of population census data based on Neural Network and GIS-Taking Guangxi Du’an county as an example. Geomat. Spat. Inf. Technol.
**2009**, 32, 46–49. (In Chinese) [Google Scholar] - Chai, Z.W.; Wang, S.L.; Qiao, J.G. Township GDP estimation of the Pearl Delta based on the NPP-VIIRS night-time satellite data. Trop. Geogr.
**2015**, 35, 379–385. (In Chinese) [Google Scholar] - Henderson, J.V.; Storeygard, A.; Weil, D.N. Measuring Economic Growth from Outer Space. Am. Econ. Rev.
**2012**, 102, 994–1028. [Google Scholar] [CrossRef] [PubMed] - Shi, K.F.; Yu, B.L.; Huang, Y.X.; Hu, Y.J.; Yin, B.; Chen, Z.Q.; Chen, L.J.; Wu, J.P. 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. Remote Sens.
**2014**, 6, 1705–1724. [Google Scholar] [CrossRef] - Ou, J.O.; Liu, X.P.; Li, X.; Li, M.F.; Li, W.K. Evaluation of NPP-VIIRS Nighttime Light Data for mapping global fossil fuel combustion CO
_{2}emissions: A comparison with DMSP-OLS Nighttime Light Data. PLoS ONE**2015**. [Google Scholar] [CrossRef] [PubMed] - Kramer, H.J. Observation of the Earth and Its Environment: Survey of Missions and Sensors, 2nd ed.; Springer-Verlag: Berlin, Germany, 1994. [Google Scholar]
- Wu, J.S.; Niu, Y.; Peng, J.; Wang, Z.; Huang, X.L. Research on energy consumption dynamic among prefecture level cities in China based on DMSP/OLS Nighttime Light. Geogr. Res.
**2014**, 33, 625–634. (In Chinese) [Google Scholar] - Su, Y.X.; Chen, X.Z.; Ye, Y.Y.; Wu, Q.; Zhang, H.O.; Huang, N.S.; Kuang, Y.Q. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries. Acta Geogr. Sin.
**2013**, 68, 1513–1526. [Google Scholar] - Elvidge, C.D.; Ziskin, D.; Baugh, K.E.; Tuttle, B.T.; Ghosh, T.; Pack, D.W.; Erwin, E.H.; Zhizhin, M. A fifteen year record of global natural gas flaring derived from satellite data. Energies
**2009**, 2, 595–622. [Google Scholar] [CrossRef] - Baugh, K.; Elvidge, C.D.; Ghosh, T.; Ziskin, D. Development of a 2009 stable lights product using DMSP-OLS data. Proc. Asia Pac. Adv. Net.
**2010**, 30, 114–130. [Google Scholar] [CrossRef] - Gao, Y.; Wang, H.; Wang, P.T.; Sun, X.Y.; Lv, T.T. Population spatial processing for Chinese Coastal Zones based on census and multiple night light data. Resour. Sci.
**2013**, 35, 2517–2523. (In Chinese) [Google Scholar] - Li, T.; He, C.Y.; Yang, Y.; Liu, Z.F. Understanding electricity consumption changes in Chinese mainland from 1995 to 2008 by using DMSP/OLS stable nighttime light time series data. Acta Geogr. Sin.
**2011**, 66, 1403–1412. [Google Scholar] - Baugh, K.; Hsu, F.-C.; Elvidge, C.D.; Zhizhin, M. Nighttime lights compositing using the VIIRS day-night band: Preliminary results. Proc. Asia Pac. Adv. Net.
**2013**, 35, 70–86. [Google Scholar] [CrossRef] - National Bureau of Statistics. China Statistical Yearbook; China Statistical Publishing House: Beijing, China, 2014.
- National Bureau of Statistics. China City Statistical Yearbook; China Statistical Publishing House: Beijing, China, 2014.
- Letu, H.; Hara, M.; Yagi, H.; Naoki, K.; Tana, G.; Nishio, F.; Shuhei, O. Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects. Remote Sens.
**2010**, 31, 4443–4458. [Google Scholar] [CrossRef] - Aldhous, P. Energy: China’s burning ambition. Nature
**2005**, 435, 1152–1154. [Google Scholar] [CrossRef] [PubMed] - Christopher, C.M.K.; Stefanie, G.; Helga, K.; Alejandro, S.M.; Jaime, Z.; Jürgen, F.; Franz, H. High-Resolution Imagery of Earth at Night: New Sources, Opportunities and Challenges. Remote Sens.
**2015**, 7, 1–23. [Google Scholar] - Liu, Y.X.; Wu, W.H.; Wen, X.J.; Zhang, D.H. Urban process and its eco-environmental impact in Shanxi-Shaanxi-Inner Mongolia energy area. Geogr. Res.
**2013**, 32, 2009–2020. (In Chinese) [Google Scholar]

**Figure 1.**The 31 provinces and 341 city level regions in Mainland China for analysis in this study. The map also contains the North China Plain, Yangtze River Delta, and Pearl River Delta.

**Figure 2.**The nighttime light data of Mainland China. The map also contains the North China Plain, Yangtze River Delta, and Pearl River Delta. (

**a**) DMSP/OLS data in 2013; (

**b**) NPP/VIIRS data in 2014.

**Figure 3.**The scatter diagram of regression variables in provincial regions and in city regions: (

**a**) DMSP/OLS data in 2013 versus GDP data in 2014 at the provincial scale; (

**b**) NPP/VIIRS data in 2014 versus GDP data in 2014 at the provincial scale; (

**c**) DMSP/OLS data in 2013 versus GDP data in 2014 at the city level scale; (

**d**) NPP/VIIRS data in 2014 versus GDP data in 2014 at the provincial scale. The black color line means the TNL-GDP relationship using linear regression model, the blue color line means the TNL-GDP relationship using polynomial model, the red color line means the TNL-GDP relationship using power function model. The red triangle frame in the picture means the marking of economic developed region.

**Figure 4.**Relative Error (RE) values based on different sources of nighttime light data at the city level scale using the power function model; (

**a**) based on the DMSP/OLS data and (

**b**) based on the NPP/VIIRS data.

**Figure 5.**The spatial distribution of topography and typical land covers in Mainland China: (

**a**) Altitude; (

**b**) Landscape of Bare Land; (

**c**) Landscape of Forestry; (

**d**) Landscape of Water and Wetland.

**Table 1.**GDP fitting precision values based on different nighttime light data sources in the provincial scale and the city level scale.

Data | Simulation Model | Provincial Scale | City Level Scale | ||||
---|---|---|---|---|---|---|---|

R | RE (%) | RRMSE | R | RE (%) | RRMSE | ||

DMSP-OLS | Linear Regression Model | 0.87 | 0.161 | 66.5 | 0.80 | −0.002 | 404.1 |

Power Function Model | 0.87 | −0.068 | 54.9 | 0.82 | −0.211 | 187.2 | |

Polynomial Model | 0.87 | 0.012 | 76.4 | 0.84 | −0.108 | 180.8 | |

NPP-VIIRS | Linear Regression Model | 0.93 | −0.0006 | 50.2 | 0.93 | −0.017 | 67.6 |

Power Function Model | 0.93 | −0.020 | 40.9 | 0.90 | −0.121 | 65.4 | |

Polynomial Model | 0.93 | −0.226 | 52.5 | 0.93 | 0.004 | 66.2 |

**Table 2.**The summary of RE values in 341 cities based on different nighttime light products and models.

Data | Model | Significantly Overvalued (RE ≥ 50%) | Overvalued (30% ≤ RE < 50%) | Estimated Accurately (−30% < RE < 30%) | Undervalued (−50% < RE ≤ −30%) | Significantly Undervalued (RE ≤ −50%) | |||||
---|---|---|---|---|---|---|---|---|---|---|---|

Quantity | Ratio | Quantity | Ratio | Quantity | Ratio | Quantity | Ratio | Quantity | Ratio | ||

DMSP-OLS | Linear Regression Model | 88 | 25.8 | 21 | 6.2 | 107 | 31.4 | 39 | 11.4 | 86 | 25.2 |

Power-Function Model | 85 | 24.9 | 26 | 7.6 | 112 | 32.8 | 32 | 9.4 | 86 | 25.2 | |

Polynomial Model | 87 | 25.5 | 42 | 12.3 | 121 | 35.5 | 50 | 14.7 | 41 | 12.0 | |

NPP-VIIRS | Linear Regression Model | 57 | 16.7 | 44 | 12.9 | 172 | 50.5 | 42 | 12.3 | 26 | 7.6 |

Power-Function Model | 57 | 16.7 | 45 | 13.2 | 171 | 50.1 | 42 | 12.3 | 26 | 7.6 | |

Polynomial Model | 55 | 16.1 | 44 | 12.9 | 165 | 48.4 | 43 | 12.6 | 34 | 10.0 |

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## Share and Cite

**MDPI and ACS Style**

Dai, Z.; Hu, Y.; Zhao, G.
The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels. *Sustainability* **2017**, *9*, 305.
https://doi.org/10.3390/su9020305

**AMA Style**

Dai Z, Hu Y, Zhao G.
The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels. *Sustainability*. 2017; 9(2):305.
https://doi.org/10.3390/su9020305

**Chicago/Turabian Style**

Dai, Zhaoxin, Yunfeng Hu, and Guanhua Zhao.
2017. "The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels" *Sustainability* 9, no. 2: 305.
https://doi.org/10.3390/su9020305