江西省人口结构格局、空间聚类及其经济影响分析

Population Structure Pattern, Spatial Clustering and Economic Impact Analysis of Jiangxi Province

摘要: 人口结构是影响城市发展进程的关键因素，也在相当程度上反映地区的经济发展水平。本文以江西省为研究对象，综合运用可视化分析与统计方法，对人口结构特征及其演变趋势展开多维度探讨。首先，基于Python编程对江西省各城市的人口数量、性别构成、教育程度及婚姻状况等人口特征进行可视化呈现；进而，结合行业就业分布、人口自然增长率、出生率、死亡率及人口抚养比等指标，分析人口结构的动态变化趋势。结果表明：江西省人口主要集中于赣州和南昌两市，两地性别比例差异较为明显，其中南昌市居民中本科与硕士学历人口占比最高；制造业与批发零售业中就业人口以30~34岁年龄段为主；全省人口自然增长率呈下降态势，死亡率保持稳定，出生率总体走低，老年抚养比持续上升。在可视化分析基础上，运用聚类方法将江西省各城市按人口结构特征划分为三类：南昌市所属类别具有较为成熟的人口结构、较高的经济发展水平与较强的发展活力；赣州市所属类别人均收入适中，劳动力结构相对稳定，在金融、科研等领域具备较大发展潜力；萍乡市所属类别人均收入偏低，产业结构优化与转型压力较大。进一步通过主成分分析发现，居民人均可支配收入与城镇化率对人口年龄结构影响显著，而死亡率对人口老龄化具有重要解释力。本研究从多维度对江西省人口结构进行系统剖析，可为相关人口政策的制定提供科学参考。

Abstract: Population structure is a key factor influencing urban development and also significantly reflects the level of regional economic development. Taking Jiangxi Province as the research subject, this study conducts a multidimensional exploration of the characteristics and evolutionary trends of its population structure by integrating visual analysis and statistical methods. First, Python programming is used to visualize demographic features such as population size, gender composition, education level, and marital status across cities in Jiangxi Province. Then, by incorporating indicators including employment distribution by industry, natural population growth rate, birth rate, mortality rate, and dependency ratio, the dynamic trends of population structure are analyzed. The results show that the population of Jiangxi Province is primarily concentrated in Ganzhou and Nanchang, where the gender ratio disparity is relatively significant. Among these, Nanchang has the highest proportion of residents with bachelor’s and master’s degrees. Within the manufacturing and wholesale/retail sectors, the employed population is predominantly aged 30~34. At the provincial level, the natural population growth rate shows a declining trend, the mortality rate remains stable, the birth rate is generally decreasing, and the elderly dependency ratio continues to rise. Building on the visual analysis, clustering methods are applied to categorize Jiangxi’s cities into three types based on population structure characteristics: the category including Nanchang exhibits a more mature population structure, higher economic development level, and stronger growth vitality; the category containing Ganzhou has moderate per capita income, a relatively stable labor structure, and considerable potential for development in sectors such as finance and scientific research; the category including Pingxiang is characterized by lower per capita income and faces greater pressure in optimizing and transforming its industrial structure. Further principal component analysis reveals that per capita disposable income and urbanization rate significantly influence the age structure of the population, while the mortality rate has considerable explanatory power regarding population aging. This study provides a systematic, multidimensional analysis of the population structure in Jiangxi Province, offering a scientific reference for the formulation of relevant population policies.