武汉市公共交通网络中心性及其与银行网点的空间耦合性研究  

Centrality of public transportation network and its coupling with bank branches distribution in Wuhan City

作　　者：詹璇[1,2] 林爱文[1,2] 孙铖[1,2] 乔卫[1] 

机构地区：[1]武汉大学资源与环境科学学院,武汉430079 [2]教育部地理信息系统重点实验室,武汉430079

出　　处：《地理科学进展》2016年第9期1155-1166,共12页Progress in Geography

基　　金：国家基础科学人才培养基金项目(J1103409)~~

摘　　要：本文以构建公共交通网络为切入点,运用改进的多中心性评价模型测度了武汉都市发展区公共交通网络中心性,并结合GIS核密度分析法与双变量空间自相关分析法,探讨了公共交通网络中心性和银行网点空间分布规律,以及两者之间的耦合性与空间结构。主要研究结论为:(1)武汉都市发展区公共交通网络中心性具有城市多中心指向性特征,且由中心向外围圈层递减;(2)银行网点布局呈现"核心—中心—过渡区—外围"多层次结构,且具有空间不均衡性;(3)公共交通网络各中心性指标与银行网点具有不同程度的空间正相关。银行网点受接近中心性的影响最大,直达性次之,介数中心性最小;(4)局域上,公共交通网络中心性指标与银行网点的耦合关系存在空间不平稳性与空间异质性。高—高聚集与低—低聚集是主要的空间关联模式;高—高聚集主要位于银行网点布局的核心圈层,低—低聚集点缀于外围圈层,高—低聚集与低—高聚集介于核心圈层与外围圈层之间。Urban public transportation is an indispensable part of urban life and an important topic in today＇s urban geography research. Existing studies on public transportation mainly focused on the characteristics of network structure and topology. Centrality is one of the important properties of public transportation network,and is widely examined both in theoretical and empirical studies. Centrality can be effectively calibrated by Multiple Centrality Assessment Model（MCA）, which is composed of multiple measures such as closeness,betweenness, and straightness. In recent years, researchers began to focus increasingly more on the study of the relationship between public transportation network and social and economic activities. As the most important financial intermediary in the city, banks and the distribution of their branches are closely related to the efficiency of people＇s financial activities. Thus, we investigate the spatial pattern of public transportation network centrality and its coupling with bank branches in Wuhan City. First, this study builds a dataset consisting of public transportation and bank branches in Wuhan urban development zone of 2015 in Arc GIS. Based on the characteristics of the data, this study improves the Multiple Centrality Assessment Model for better accuracy. It then examines the geography of three centrality indices by improved Multiple Centrality Assessment Model, and analyzes the centrality of Wuhan public transportation network and its spatial correlation with bank network layout by using kernel density estimation and bivariate spatial autocorrelation model based on Geo Da. The results show that：（1） Kernel density indicates a clear city multicenter directivity of the public transportation network centralities in Wuhan, and the concentration decreases progressively from the central areas to the periphery.（2） The spatial distribution pattern of bank branches presents a core—center—transitional area—periphery multi- level structure, and the regional variations b

关 键 词：公共交通网络中心性 改进多中心性评价模型 银行网点 耦合 武汉都市发展区 

分 类 号：F572.88[经济管理—产业经济] F832.7