Application of Social Network Analysis in the Economic Connection of Urban Agglomerations Based on Nighttime Lights Remote Sensing: A Case Study in the New Western Land-Sea Corridor, China

• Published in: ISPRS international journal of geo-information 2022-10, Vol.11 (10), p.522
• Main Authors: Zhang, Bin, Yin, Jian, Jiang, Hongtao, Qiu, Yuanhong
• Format: Article
• Language: English
• Subjects: Agglomeration; Analysis; Big Data; Cities; City centres; Data analysis; Datasets; Economic analysis; economic connection; Economic development; Economic growth; Economic statistics; Economics; GDP; Gross Domestic Product; Network analysis; New Western Land-Sea Corridor; Night; Night-time; Nighttime; nighttime lights; Radiation; Regions; Remote sensing; Social network analysis; Social networks; Social organization; Spatial analysis; Spatial data; Strategic planning; Trends; urban agglomerations; Urban areas; Urbanization
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi11100522

Nighttime lights remote sensing has a significant advantage in exploring the economic development of cities. Based on nighttime lighting data, this study employed spatial direction analysis, exploratory spatial data analysis, and social network analysis to explore the spatial characteristics of economic development and analyzed the economic connection network structures within urban agglomerations in the New Western Land-sea Corridor (NWLSC) in western China. The results show that the spatial pattern of the Tianshan North slope urban agglomeration, Guanzhong Plain urban agglomeration, and Lanzhou–Xining urban agglomeration shrank, while other urban agglomerations expanded. The city economy of the Chengdu–Chongqing urban agglomeration (CCUA) and the Beibu Gulf urban agglomeration varied dramatically according to a LISA space-time transition analysis, which indicates a strong spatial dependence between cities in the local space. Within urban agglomerations, the economic connection between cities increased significantly, and central cities were at the core of the network and significantly influenced other cities. Among the urban agglomerations, economic connections among neighboring urban agglomerations in geographic space increased during the study period. The CCUA gradually developed into the center of the economic network in the NWLSC. Network density positively influenced economic connections. The degree centrality, closeness centrality, and betweenness centrality significantly enhanced the economic connections between city agglomerations. The study’s conclusions and methods can serve as the policy support for the cooperative development of urban agglomerations in NWLSC serve as a guideline for the development of other economically underdeveloped regions in the world.