Regression results.

This article compares the population agglomeration characteristics of the Xi’an metropolitan area in western China with those of metropolitan areas in other regions officially approved by the Chinese government. The kernel density estimation method and Markov chain model were used to conduct the study. The results revealed that from 2010 to 2020, the population agglomeration level of the Xi’an metropolitan area showed a trend of first increasing and then decreasing. The absolute gap in the population agglomeration level between cities within the metropolitan area gradually narrowed, and the polarization phenomenon of population agglomeration was not obvious. Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. Moreover, there was an obvious “club convergence” phenomenon in the population agglomeration levels of different urban agglomerations. The probability of the population agglomeration level remaining stable was at least 53.85%, indicating that there was a “Matthew effect” in which the rich become richer and the poor become poorer. Through the convergence models of α and β, the analysis suggested that there was no significant α convergence between the population agglomeration level of the Xi’an metropolitan agglomeration and that of other metropolitan agglomerations. Instead, there was a significant β divergence, indicating that the gap between the Xi’an metropolitan agglomeration’s population agglomeration level and that of other metropolitan agglomerations is gradually widening. An integrated theoretical framework of population agglomeration was constructed from three dimensions: producers, consumers, and social people. An empirical analysis was conducted on the causes of population agglomeration in the Xi’an metropolitan area and other metropolitan areas. The multiple regression results showed that the income level, public consumption expenditure level, education level, comfortable living environment, and educational level were important factors leading to differences in population agglomeration. The geographic detector results showed that factors in the consumer dimension were the main reasons for population agglomeration in metropolitan areas.