Spatiotemporal variation and driving factors analysis on the expansion of the main urban agglomerations in China

Article type: Research Article

Authors: Li, Qi^{a; b} | Hong, Liang^{a; b; *}

Affiliations: [a] Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, China | [b] GIS Technology Research Centre of Resource and Environment in Western China of Ministry of Education, Yunnan Normal University, Chenggong Country, Kunming, Yunnan, China

Correspondence: [*] Corresponding author. Liang Hong, Yunnan Normal University, No. 1, Yuhua District, Chenggong District, Kunming, Yunnan, China. E-mail: hongliang20433@hotmail.com.

Abstract: The process of urbanization has brought about prosperity in urban civilizations, causing a series of ecological and social problems. Therefore, in recent years, monitoring the process of urban expansion has become a hot spot in the field of geosciences. The 8 main urban agglomerations built-up areas from 1995 to 2015 were extracted by night light images. Based on the expansion speed and intensity index, center of gravity migration model, spatial correlation analysis and grey correlation analysis, the characteristics of the spatial and temporal variation were described. Based on it, a driving force model was established to explore the factors behind its spatial and temporal expansion. The built-up areas of the Yangtze River Delta, the Pearl River Delta, the Beijing-Tianjin-Hebei Region, the Chengdu-Chongqing Economic Circle, Central Plains, the middle reaches of the Yangtze River, central Yunnan, and the Beibu Gulf have been increasing year by year, and reached 7671 km2, 3926 km2, 3729 km2, 3025 km2, 6649 km2, 3172 km2, 500 km2, 1047 km2 in 2015, which are 5.0, 6.6, 2.6, 5.1, 3.1, 2.8, 3.5, 3.2 times more than that in 1995 respectively. There is an expansion trend of ‘point-block-surface’ from the overall perspective. The development of all eight urban agglomerations belongs to the spatial expansion mode under the guidance of agglomeration, the spatial distribution presents positive spatial autocorrelation, and the agglomeration degree manifests fluctuating changes. Socio-economic factors such as non-agricultural population, regional Gross Domestic Product, and total industrial output have a greater impact on the expansion of urban built-up areas, while the number of colleges and universities and the total investment in fixed assets have less impact with less synchronization.

Keywords: Night light images, urban built-up area, spatiotemporal variation, driving factors

DOI: 10.3233/JIFS-220201

Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 4, pp. 4145-4159, 2022