基于Ripley’s K函数的南京市ATM网点空间分布模式研究

doi:10.13249/j.cnki.sgs.2016.12.009

地理科学 ›› 2016, Vol. 36 ›› Issue (12): 1843-1849.doi: 10.13249/j.cnki.sgs.2016.12.009

基于Ripley’s K函数的南京市ATM网点空间分布模式研究

王结臣^1,^2,³(), 卢敏¹, 苑振宇¹, 芮一康^1,², 钱天陆¹

1.南京大学地理信息科学系, 江苏南京 210023
2. 江苏省地理信息科学重点实验室, 江苏南京 210023
3. 江苏省地理信息资源开发与利用协同创新中心, 江苏南京 210023

收稿日期:2015-11-09 修回日期:2016-03-25 出版日期:2016-12-20 发布日期:2016-12-20
作者简介:
作者简介：王结臣（1973-）,男,安徽太湖人,教授,博导,主要研究GIS理论与应用、地理空间分析。E-mail：wangjiechen@nju.edu.cn
基金资助:
国家自然科学基金项目（41571377,41401450）资助

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

Jiechen Wang^1,^2,³(), Min Lu¹, Zhenyu Yuan¹, Yikang Rui^1,², Tianlu Qian¹

1. Department of Geographic Information Science, Nanjing University, Nanjing 210023, Jiangsu, China
2. Jiangsu Province Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, Jiangsu, China
3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, Jiangsu, China

Received:2015-11-09 Revised:2016-03-25 Online:2016-12-20 Published:2016-12-20
Supported by:
National Natural Science Foundation of China (41571377,41401450)

摘要/Abstract

摘要：

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。

关键词: Ripley’s K函数, ATM网点分布, 点模式分析

Abstract:

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.

Key words: Ripley’s K-Function, ATMs distribution, point pattern analysis

中图分类号:

P208

王结臣, 卢敏, 苑振宇, 芮一康, 钱天陆. 基于Ripley’s K函数的南京市ATM网点空间分布模式研究[J]. 地理科学, 2016, 36(12): 1843-1849.

Jiechen Wang, Min Lu, Zhenyu Yuan, Yikang Rui, Tianlu Qian. Point Pattern Analysis of ATMs Distribution Based on Ripley’s K-Function Method in Nanjing City[J]. SCIENTIA GEOGRAPHICA SINICA, 2016, 36(12): 1843-1849.

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参考文献 19

[1]	Lotwick H W, Silverman B W.Methods for analysing spatial processes of several types of points[J]. Journal of the Royal Statistical Society. 1982,44(3): 406-413.
[2]	Dixon P M.Ripley’s K function[J]. Encyclopedia of environmetrics, 2002, 3: 1796-1803. doi: 10.1002/9780470057339.var046
[3]	Ripley B D.Spatial statistics[M]. Chichester, U.K.:John Wiley & Sons, 2005.
[4]	高凯, 周志翔, 杨玉萍, 等. 基于Ripley’s K函数的武汉市景观格局特征及其变化[J]. 应用生态学报, 2010, 21(10): 2621-2626.
	[Gao Kai, Zhou Zhixiang, Yang Yuping et al. Characteristics and changes of landscape pattern in Wuhan City based on Ripley’s K function.Chinese Journal of Applied Ecology, 2010, 21(10): 2621-2626.]
[5]	Miller H J.Market area delimitation within networks using geographic information systems[J]. Geographical Systems,1994, 1(2): 157-173.
[6]	Okabe A, Yamada I.The K-function method on a network and its computational implementation[J]. Geographical Analysis, 2001, 33(3): 271-290. doi: 10.1111/j.1538-4632.2001.tb00448.x
[7]	Okabe A, Sugihara K.Spatial Statistics along Networks: Statistical and Computational Methods[M]. Chichester, U.K.: John Wiley & Sons, 2012.
[8]	Lu Y, Chen X.On the false alarm of planar K-function when analyzing urban crime distributed along streets[J]. Social Science Research, 2007, 36(2): 611-632. doi: 10.1016/j.ssresearch.2006.05.003
[9]	Garrocho-Rangel C, Álvarez-Lobato J A, Chávez T. Calculating Intraurban Agglomeration of Economic Units with Planar and Network K-Functions: A Comparative Analysis[J]. Urban Geography, 2013, 34(2): 261-286. doi: 10.1080/02723638.2013.778655
[10]	邬伦, 刘亮, 田原, 等. 基于网络K函数法的地理对象分布模式分析——以香港岛餐饮业空间格局为例[J]. 地理与地理信息科学, 2013, 29(5): 7-11. doi: 10.7702/dlydlxxkx20130502
	[Wu Lun, Liu Liang, Tian Yuan et al. Spatial Pattern Analysis of Geographic Feature Using Network K—Function Methods with a Case Study of Restaurant Distribution in Hong Kong Island. Geography and Geo-Information Science, 2013, 29(5): 7-11. ] doi: 10.7702/dlydlxxkx20130502
[11]	葛莹, 蒲英霞, 赵慧慧, 等. 基于边际K函数的长三角地区城市群经济空间划分[J]. 地理学报, 2015,70(4), 528-538. doi: 10.11821/dlxb201504002
	[Ge Ying, Pu Yingxia, Zhao Huihui et al. Dividing economic space into urban agglomerations usingthe marginal K-function:A case study of the Yangtze River Delta region. Acta Geographica Sinica,2015,70(4), 528-538. ] doi: 10.11821/dlxb201504002
[12]	Li L, Huang Z, Ye W et al. Spatial distributions of tree species in a subtropical forest of China[J]. Oikos, 2009, 118(4): 495-502. doi: 10.1111/j.1600-0706.2009.16753.x
[13]	Wiegand T, Kisslin W D, Cipriotti P A et al. Extending point pattern analysis for objects of finite size and irregular shape[J]. Journal of Ecology, 2006, 94(4): 825-837. doi: 10.1111/j.1365-2745.2006.01113.x
[14]	Wiegand T, A Moloney K. Rings, circles, and null-models for point pattern analysis in ecology[J]. Oikos, 2004, 104(2): 209-229.
[15]	Besag J, Diggle P J.Simple Monte Carlo tests for spatial pattern[J]. Applied Statistics, 1977,26(3):327-333. doi: 10.2307/2346974
[16]	Spooner P G, Lunt I D, Okabe A et al. Spatial analysis of roadside Acacia populations on a road network using the network K-function[J]. Landscape Ecology, 2004, 19(5): 491-499. doi: 10.1023/B:LAND.0000036114.32418.d4
[17]	Okabe A, Kitamura M.A computational method for market area analysis on a network[J]. Geographical Analysis, 1996, 28(4): 330-349. doi: 10.1111/j.1538-4632.1996.tb00939.x
[18]	杨珏婕, 刘世梁, 赵清贺, 等. 基于网络K函数的西双版纳人工林空间格局及动态[J]. 生态学报, 2011, 31(22): 6734-6742.
	[Yang Juejie, Liu Shiliang, Zhao Qinghe et al. Spatial and dynamic analysis of plantations in Xishuangbanna using network K-function. Acta Ecologica Sinica, 2011, 31(22): 6734-6742. ]
[19]	Xie Z, Yan J.Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: An integrated approach[J]. Journal of Transport Geography, 2013, 31, 64-71. doi: 10.1016/j.jtrangeo.2013.05.009

银行名称	ATM网点数量	银行名称	ATM网点数量
中国工商银行	130	中国邮政储蓄银行	96
中国建设银行	117	中国银行	86
中国农业银行	102	中国民生银行	62
交通银行	97	招商银行	59

基于Ripley’s K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

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