GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery

Abstract

Accurate data on gross domestic product (GDP) at pixel level are needed to understand the dynamics of regional economies. GDP spatialization is the basis of quantitative analysis on economic diversities of different administrative divisions and areas with different natural or humanistic attributes. Data from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar-orbiting Partnership (NPP) satellite, are capable of estimating GDP, but few studies have been conducted for mapping GDP at pixel level and further pattern analysis of economic differences in different regions using the VIIRS data. This paper produced a pixel-level (500 m × 500 m) GDP map for South China in 2014 and quantitatively analyzed economic differences among diverse geomorphological types. Based on a regression analysis, the total nighttime light (TNL) of corrected VIIRS data were found to exhibit R 2 values of 0.8935 and 0.9243 for prefecture GDP and county GDP, respectively. This demonstrated that TNL showed a more significant capability in reflecting economic status (R 2 > 0.88) than other nighttime light indices (R 2 < 0.52), and showed quadratic polynomial relationships with GDP rather than simple linear correlations at both prefecture and county levels. The corrected NPP-VIIRS data showed a better fit than the original data, and the estimation at the county level was better than at the prefecture level. The pixel-level GDP map indicated that: (a) economic development in coastal areas was higher than that in inland areas; (b) low altitude plains were the most developed areas, followed by low altitude platforms and low altitude hills; and (c) economic development in middle altitude areas, and low altitude hills and mountains remained to be strengthened.

Developing Non-Negative Spatial Autoregressive Models for Better Exploring Relation Between Nighttime Light Images and Land Use Types

“…Remote Sens. 2020, 12,798 4 of 25 area of 27,340 km 2 and population of 6.9 million in 2018, Massachusetts is the third most densely populated state in the U.S [41]. As one of the original 13 states, Massachusetts has a long history of development.…”

“…For example, the nighttime light intensity (NLI) of an American town with a population of 10,000 is three times that of a German town of the same size [10]. Studying the regional differences and influencing factors in the correlation between night light and economic activities can help researchers correct the errors in estimating GDP and economic activities by using night light, improving the accuracy of GDP estimation, and reflecting the spatial distribution of GDP accurately [11,12]. In addition, only by understanding the causes and distribution pattern of night light light more accurately and (2) to find land use types closely related to human nighttime activities and expand the application field of nighttime light data in the study of human activities, such as light pollution, traffic, GDP components, and so forth.…”

Topographical relief characteristics and its impact on population and economy: A case study of the mountainous area in western Henan, China

“…With the support of geographic information system (GIS) and remote sensing (RS) technologies, especially the high resolution images, the spatialization and refinement of socioeconomic factors have become the hot topic of geographic research. The use of these technologies greatly improved the spatial resolution and precision of social and economic data (Kan, 2007;Zhao et al, 2017;Li et al, 2018). Commonly used spatial socioeconomic models include the spatial interpolation model, land use impact model and remote sensing inversion model.…”

Regional Inequality in China Based on NPP-VIIRS Night-Time Light Imagery

“…Compared with DMSP-OLS data, the NPP-VIIRS data feature a higher spatial resolution (15 arc-second, approximately 500 m) and do not have the issue of over-saturation that exists in the DMSP-OLS data. Many scholars have employed NPP-VIIRS data to estimate economy activity [26], CO 2 emissions [27], built-up urban areas [28], urban land expansion [29], and electric power consumption at both national and subnational scales [30]. The evaluation of the NPP-VIIRS NTL in modeling economy activity showed that the total night-time light (TNL) from NPP-VIIRS had a significantly positive linear relationship to the Gross Regional Product (GRP) at both provincial and county level in 2010, being significantly stronger than the relationship between the TNL from DMSP-OLS and GRP [31].…”