摘要
基于国家金融监督管理总局登记的商业银行微观统计数据,综合利用空间热点聚类、最邻近指数(NNI)、Ripley's K函数、复合年增长率(CAGR)和标准差椭圆等研究方法,对京津冀商业银行分布时空格局演化、集聚特征和热点区分布特性进行研究,并进一步研究了其影响因素。研究结果表明:京津冀商业银行空间分布不均匀,京津冀整体和三地局部尺度上均表现出显著的空间集聚特征;空间集聚程度随着观测距离的增加先上升后下降,热点区主要分布在以北京市、天津市、邢台市和廊坊市为核心的集聚区内,呈现“主次型”单一结构;工业化水平和政府行为是京津冀商业银行空间集聚的核心影响因素,城市环境、经济发展水平对京津冀商业银行空间集聚的影响程度逐年增强。
Based on the micro data of commercial bank branches registered by the State Administration for Financial Regulation,this paper comprehensively utilizes the spatial hotspot clustering,Nearest Neighbor Index(NNI),Ripley’s K-function,Compound,Annual Growth Rate(CAGR),and standard deviation ellipse analysis to study the spatial pattern of commercial bank distribution,agglomeration characteristics and hotspot area distribution and the influencing factors in the Beijing-Tianjin-Hebei urban agglomeration.The results show that:The distribution of commercial bank in Beijing-Tianjin-Hebei urban agglomeration is not uniform,showing significant spatial clustering characteristics on the scale of the whole urban agglomeration and each local region.The degree of spatial agglomeration firstly increases and then decreases with increasing geographic distance.Hotspots of commercial banks are mainly concentrated in the agglomeration areas surrounding Beijing,Tianjin,Xingtai and Langfang,showing a primary-secondary structure.Industrialization level and government actions are the core influential factors of the agglomeration of commercial banks in the Beijing-Tianjin-Hebei urban agglomeration.Besides,the influence of urban environment and economic development level on the agglomeration is gradually increasing.
作者
李佳琦
吴俊萍
胡建梅
Li Jiaqi;Wu Junping;Hu Jianmei(School of Economics and Management,Hebei University of Technology,Tianjin 300130,China;Tianjin TEDA Branch,Agriculture Bank of China,Tianjin 300457,China)
出处
《金融理论探索》
2024年第5期47-58,共12页
Exploration of Financial Theory
基金
教育部人文社会科学研究项目“中国对非援助分配空间格局演化及其影响因素研究”(23YJCGJW001)。
关键词
京津冀
商业银行
空间演化
金融地理学
泊松回归
Beijing-Tianjin-Hebei urban agglomeration
commercial banks
space evolution
financial geography
Poisson regression