地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (6): 837-843.doi: 10.12082/dqxxkx.2018.170596

• 2017年中国地理信息科学理论与方法学术年会优秀论文专辑 • 上一篇    下一篇

基于网络空间点模式的餐饮店空间格局分析

曾璇(), 崔海山*(), 刘毅华   

  1. 广州大学地理科学学院,广州 510006
  • 收稿日期:2017-12-08 修回日期:2018-02-02 出版日期:2018-06-20 发布日期:2018-06-20
  • 通讯作者: 崔海山 E-mail:gdzengx@163.com;cuihaishan@126.com
  • 作者简介:

    作者简介:曾 璇(1993-),女,硕士生,主要从事GIS空间分析与集成应用研究。E-mail: gdzengx@163.com

  • 基金资助:
    国家自然科学基金项目(41771096);广东省高等学校国际暨港澳台科技合作创新平台项目(2014KGJHZ009)

Analysis on Spatial Distribution Characteristics of Restaurant Based on Network Spatial Point Model

ZENG Xuan(), CUI Haishan*(), LIU Yihua   

  1. School of geographic science, Guangzhou University, Guangzhou 510006, China
  • Received:2017-12-08 Revised:2018-02-02 Online:2018-06-20 Published:2018-06-20
  • Contact: CUI Haishan E-mail:gdzengx@163.com;cuihaishan@126.com
  • Supported by:
    National Natural Science Foundation of China, No.41771096;Project of Internation as well as Hongkong, Macao&Taiwan Science and Technology Cooperation Innovation Platform in Universities in Guangdong Province, No.2014KGJHZ009

摘要:

餐饮业是城市经济发展的重要指标,运用合适的方法来研究城市餐饮业的空间格局特征,对城市规划、商业选址和经济发展等具有重要意义。本文以广州市海珠区为例,基于餐饮店POI(兴趣点)数据,利用核密度估计法分析餐饮店的空间分布特性,采用网络核密度法探究其热点路段的分布情况,并利用网络双变量K函数法,分析餐饮店分布与公交站和居民小区的相关性。结果表明:海珠区餐饮店总体分布呈现“西密东疏”的空间格局,具有多中心的空间分布特征;江南中街道餐饮店分布的热点路段主要集中在江南西路和江南大道中沿线,其密度随着与该沿线的距离增加而衰减;在较小范围内,餐饮店的分布与公交站具有显著的聚集关系,而与居民小区不具有显著的聚集关系。对于沿道路分布的空间地理点对象,利用网络空间点模式分析可得到较好结果。

关键词: 网络空间点模式, K函数, 核密度, 空间格局, 餐饮店

Abstract:

The catering industry has been considered as one of the most important indicators concerning economic development of a city. Using appropriate methods to study catering industry has been playing an important part in research fields such as city planning, business location and economic development. Many restaurants in a city can be abstracted as point objects in the study of geography. It is one of the most commonly used methods to study the spatial layout of facility events by using spatial point patterns. The traditional point pattern analysis methods are basically based on the Euclidean distance and assume that the plane space is a homogeneous and isotropic space. However, many geo-objects are usually distributed on the road network or along the road network, such as restaurants, banks, supermarkets and road traffic accidents. If the traditional method of plane space point analysis is applied to the trend events occurring along the road network, wrong aggregation mode may occur. By using the network spatial point pattern analysis method, the shortest path distance instead of the Euclidean distance can be used to study the distribution characteristics of the event points, and more accurate spatial analysis results can be obtained. Take Haizhu District of Guangzhou city as an example, on the basis of restaurants POI (point of interest) data, Kernel density estimation is adopted to analyze spatial distribution characteristics of restaurants. The network kernel density method is used to investigate the distribution characteristics of the hot roads, and double variable K function method is applied to analyze the relations between distribution of restaurants and bus stations and residential areas. The spatial pattern of Haizhu District restaurants shows much more dense in the West and comparatively sparser in the East. The restaurant hot spots are mainly concentrated along the streets of Jiangnan West and Jiangnan Zhong, and the density of the restaurants decreases with the increase of the distance from the hot spots. The degree of aggregation of restaurants, bus stations and residential areas is also investigated under the road network structure. The results show that restaurants have strong aggregation relations with bus stations, which indicates that the restaurant tends to close to the traffic stations, but have no significant aggregation relationship with the residential areas. As far as the spatial point objects along streets are concerned, better results can be obtained by using network analysis of spatial point pattern.

Key words: network space point pattern, K function, kernel density, spatial pattern, restaurant