中国城市人才聚集的时空演化特征及 影响因素研究.

Based on the fifth and sixth national census data and the seventh national census bulletin of administrative regions at the prefecture level and above, the Gini coefficient, Theil index, spatial autocorrelation analysis and other methods are used to analyze the temporal and spatial evolution characteristics of talent agglomeration in Chinese cities from 2000 to 2020, and the multiscale geographically weighted regression is used to study the spatial heterogeneity and spatial scale of the influencing factors of talent agglomeration. The results show that: ①There is a large gap in the distribution of talents in eastern, central, and western cities in China, forming a distribution pattern of "strong in the east and weak in the central and west" and regional "one super and many strong", and the gap in average talent density tends to narrow. ②The distribution of talents shows great imbalance, with a slight weakening trend of instability. ③On the whole, the density of talents presents a significant positive spatial autocorrelation, and the spatial ag‐ glomeration effect of talents is becoming more and more significant. Locally, the Yangtze River Delta high-high talent agglomeration area and the Pearl River Delta high-high talent agglomeration area are gradually formed; the low-level talent agglomeration area composed of low-low talent agglomeration cities will have a homogeneous impact on its neighboring cities; some provincial capital cities in the west have significant high-low agglomeration characteristics, forming a "center-periphery" talent agglomeration pattern. ④The proportion of tertiary industry in GDP, the proportion of science and education expenditure in total fiscal expenditure, per capita GDP, the number of general higher education institutions, per capita public library collections, and the number of public transportation vehicles per 10 000 people have a significant positive impact on talent agglomeration, and the intensity of the impact decreases in order. Among them, the eco‐ nomic environment factor represented by the tertiary industry's share of GDP and per capita GDP has a relatively small spatial scale and strong spatial heterogeneity. Other independent variables are close to the global scale, and the spatial heterogeneity is not obvious. [ABSTRACT FROM AUTHOR]