Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model

Fuliang Deng, Luwei Cao, Fangzhou Li (), Lanhui Li, Wang Man, Yijian Chen, Wenfeng Liu and Chaofeng Peng
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Fuliang Deng: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Luwei Cao: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Fangzhou Li: Development Research Center for Surveying and Mapping, Ministry of Natural Resources of the People’s Republic of China, Beijing 100830, China
Lanhui Li: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Wang Man: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Yijian Chen: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Wenfeng Liu: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Chaofeng Peng: School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China

Sustainability, 2023, vol. 15, issue 10, 1-18

Abstract: Accurate knowledge of the spatiotemporal distribution of gross domestic product (GDP) is critical for achieving sustainable development goals (SDGs). However, there are rarely continuous multitemporal gridded GDP datasets for China in small geographies, and less is known about the variable importance of GDP mapping. Based on remotely sensed and point-of-interest (POI) data, a geographical random forest model was employed to map China’s multitemporal GDP distribution from 2010 to 2020 and to explore the regional differences in the importance of auxiliary variables to GDP modeling. Our new GDP density maps showed that the areas with a GDP density higher than 0.1 million CNY/km 2 account for half of China, mainly distributed on the southeast side of the Hu-line. The proportion of the areas with GDP density lower than 0.05 million CNY/km 2 has decreased by 11.38% over the past decade and the areas with an increase of 0.01 million CNY/km 2 account for 70.73% of China. Our maps also showed that the GDP density of most nonurban areas in northeast China declined, especially during 2015–2020, and the barycenter of China’s GDP moved 128.80 km to the southwest. These results indicate China’s achievements in alleviating poverty and the widening gaps between the South and the North. Meanwhile, the number of counties with the highest importance score for POI density, population density, and nighttime lights in GDP mapping accounts for 52.76%, 23.66%, and 23.56%, respectively, which suggests that they play a crucial role in GDP mapping. Moreover, the relationship between GDP and auxiliary variables displayed obvious regional differences. Our results provide a reference for the formulation of a sustainable development strategy.

Keywords: GDP; urban areas; land cover; geospatial big data; geographical random forest model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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