Spatial-temporal differentiation pattern and influencing factors of land economic density at the township scale in Zhejiang Province

• Published in: PloS one 2024-05, Vol.19 (5), p.e0304327
• Main Authors: Ma, Fangfang, Hu, Yiping, Ding, Zhiwei
• Format: Article
• Language: English
• Subjects: Agglomeration; Analysis; China; Cities; Coefficient of variation; Construction; Correlation analysis; Economic activity; Economic aspects; Economic Development; Economic value added; Equilibrium; GDP; Gross Domestic Product; Humans; Industrial production; Land economics; Modernization; Natural resources; Population density; Regions; Regression models; Research hot spots; Research methodology; Spatio-Temporal Analysis; Urban areas; Urbanization
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0304327

Based on the land economic density of 892 town units, the spatial pattern of the land economic density in Zhejiang Province is analyzed using the coefficient of variation, spatial classification, and spatial correlation methods, and the influencing factors are analyzed using a spatial regression model. The results are as follows: (1) The coefficients of variation were 2.6 and 3.1 in 2014 and 2019, respectively, indicating that the degree of imbalance of the town's industrial economy at the county level increased. (2) The distribution of the high-level agglomeration areas was characterized by one core area and two sub-core areas. The main core area was located at the junction of Hangzhou City, Shaoxing City, and Jiaxing City, and the two sub-core areas were located in Yuyao City and the main urban area of Ningbo City. In addition, several small-scale agglomeration areas composed of medium and high-level units were distributed in Wenzhou City. (3) The high-value agglomeration and low-value agglomeration distribution in the spatial correlation patterns was identified using the spatial auto-correlation method. The hot spots and sub-hot spots were distributed in Northern Zhejiang, and the cold spots formed a large-scale agglomeration in Quzhou City, Lishui City, Taizhou City, and several other cities in Southern Zhejiang. (4) Compared with the county scale, the spatial scope of the high-level areas in Northern Zhejiang shrunk significantly at the township scale, and the high-level agglomeration areas along the southeast coast changed into a cluster of several townships. (5) According to the geographically weighted regression (GWR) model, the importance of influencing factors is as follows: population density > regional area > industrial output value per capita > total population > proportion of secondary and tertiary personnel > total employees.