近年来中国的城市人口发生着快速变化，研究其时空变化规律及其影响 因素具有重要意义。本文分析了 2012-2022 年中国各城市的人口变化总体特 征，并对 2012 年和 2022 年各城市进行聚类，分析十年间城市人口类型发生 转变的原因及人口出生率的影响因素。数据采用 2012 年、2022 年中国直辖市 与 333 个地级行政单元的常住人口数，利用 ArcGIS 进行空间可视化；采用人口自然增长率和总抚养比，利用 SPSS 进行聚类分析；采用人口出生率（指标 选取出生人口数、年平均人口数）作为因变量，以社会经济、社会保障、公共 卫生、公共管理（指标选取第三产业增加值占 GDP 比重、人均地区生产总值、 失业率、城镇基本医疗保险参保比例、万人均医院卫生院床位数、人均教育支 出）作为自变量，利用多元线性回归分析进行影响因素探究。研究发现，十年 间多数城市的常住人口趋向于减少，人口减少成为整体趋势。聚类结果为“快 速增长中负担”型、“缓慢减少高负担”型、“缓慢减少中负担”型与“快速减 少低负担”型城市。回归结果中医保参保比例对数、人均教育支出对数、万人 均医院卫生院床位数对数统计上显著。得出结论：人均 GDP 越高、第三产业 占比越高，人口出生率越高；城镇基本医疗保险参保比例、失业率与人口出生 率成反比；人均医疗资源对人口出生率呈显著负向影响；教育水平高的城市， 人口出生率也普遍相对更高。通过人口变化特征及影响因素，以期对改善人口出生率提出针对性措施。

In recent years, China’s urban population has undergone rapid changes, and it is of great significance to study its spatiotemporal variation and its influencing factors. This paper analyzes the overall characteristics of population change in Chinese cities from 2012 to 2022. Then, cluster analysis is applied to the cities based on the data 2012 and 2022, and this study also analyzes the reasons for the change of urban population type and the influencing factors of population birth rate in the past ten years. The data were based on the number of permanent residents of municipalities and 333 prefecture-level administrative units in China in 2012 and 2022, and the spatial visualization was carried out by ArcGIS. The natural growth rate of population and the total dependency ratio were used and SPSS was used for cluster analysis. The birth rate was used as the dependent variable, and the indicators were selected as independent variables from the four aspects of social economy, social security, public health and public management, and the influencing factors were explored by multiple linear regression analysis. The study found that the permanent population of most cities tended to decline in the past decade, and the population decline became an overall trend. The clustering results were “rapid growth and medium burden” type, “slow reduction and high burden” type, “slow reduction and medium burden” type and “rapid reduction and low burden” type of cities. The logarithm of the proportion of medical insurance participation, the logarithm of average education expenditure, and the logarithm of hospital and health center beds per 10,000 people were significantly significant. The regression results show that the higher the per capita GDP and the higher the proportion of the tertiary industry, the higher the birth rate. The proportion of urban basic medical insurance and the unemployment rate are inversely proportional to the birth rate. The level of medical resources per capita has a significant negative effect on the birth rate, and the birth rate of cities with high education level is generally higher. Through the characteristics and influencing factors of population change, suggestions are proposed to improve the birth rate of the population.