基于Ripley’sK函数的南京市ATM网点空间分布模式研究

Point Pattern Analysis of ATMs Distribution Based on Ripley's K-Function Method in Nanjing City

运用Ripley’s K函数的相关理论,以南京市ATM网点为研究对象,分别从平面与网络空间两种视角,在中心城区范围与主城区范围两种空间尺度上,通过单变量K函数法分析ATM网点的分布模式,通过双变量K函数法分析ATM网点与地铁站点的空间关联情况,最后对计算结果进行评价与分析。研究表明,ATM网点在南京主城区与中心城区均呈现出较强的集聚状态;在一定的距离范围内,ATM网点与地铁站点之间也有较强的依赖关系。同时,对于沿着路网分布的地理空间点状对象而言,利用网络K函数法进行空间点模式分析比用平面K函数法更加符合实际情况。

Since distributions of many types of urban objects are not random but in some particular patterns, analyzing and revealing the spatial distribution pattern of these points in urban space are essential to understand social, economic and geographical factors behind the distributions, and analysis results are conductive to wide applications such as facility layout and aided decision support. In point pattern analysis, the results may be biased by merely calculating the nearest neighbor distance. The Ripley＇s K-function was therefore proposed with advantages of considering the distance between any pair of points. Because many urban points associated with human activities are constrained by road networks, a network K-function, as an extension of traditional planar K-function, is then presented by applying a network distance, i.e., the shortest path distance between any pair of two points. In this article, the Ripley＇s K-function was applied to analyze the spatial distribution characteristics of ATMs in Nanjing City. First of all, we used both planar univariate K-function and network univariate K-function to analyze the distribution pattern of ATMs at spatial scales of downtown areas and main urban districts.Then we used planar bivariate K-function and network bivariate K-function to investigate the spatial correlation between ATMs and metro stations in main urban districts. Local bivariate（cross） K-functionwas finally applied to explore the impact of metro stations on the ATMs in local areas. The results show that ATMs are highly clustered in both planar and network space and the cluster characteristic is more significant in downtown areas than in main urban areas. Besides, ATMs and metro stations are highly correlated in the study area. With the increase of the measuring distance, the relationship between ATM and metro station distributions shows more obvious characteristic of spatial aggregation within a certain distance. In the analysis of local cross K-function method,ATMs are clustered with metro stations in downtown areas while there is no significant clustering characteristic between ATMs and metro stations on the outskirts. It implies that the distribution of ATMs is mainly determined by the regional commercial development. In addition, for spatial pattern analysis on point objects distributed along road networks, network K-function method is more practical than planar K-function in terms of revealing appropriate distribution pattern and relationship between two types of point objects.