摘要
以广州的76家星巴克门店为研究对象,借助空间分析和空间统计方法探讨分析了广州星巴克的空间分布特征、不同观测尺度的空间分布模式、空间分布方向、空间分布热点区域以及区位选择的影响因素。研究发现:(1)星巴克的空间分布总体呈现凝聚的带状分布模式;(2)星巴克门店在空间上呈现东北—西南方向分布特征;(3)星巴克门店的空间分布密度呈现出由圈内向圈外密度逐级递减的圈层结构,其热点区域聚集分布在城市中心的商圈、交通枢纽以及中高端休闲消费人群聚集区附近;(4)城市的交通枢纽、商圈区位以及地价水平三个因素显著影响了星巴克门店的空间布局。
With the process of consumption of globalizationand the gradual implementation of the policy of opening up China's retail industry, the foreign retails distributed in the country quickly to strengthen its localization operations in China. From the expansion of the scale and rate of expansion, the expansion of KFC, McDonald's, HaagenDazs, Starbucks and other multinational food and beverage companies is the most typical. In this paper, the multinational food brands Starbucks is been selected as the research object, the article explores it's spatial distribution and location selection factors. Under the local scale, 76 Starbucks stores in Guangzhou are selected as the research object, and by means of the nearest neighbor analysis tools, Ripley's K function, standard deviation ellipse analysis method based on the nearest hot spot analysis, hierarchical clustering and buffer analysis, the paper discusses the spatial distribution, spatial distribution patterns in different observation scales, the spatial distribution of direction, spatial distribution hotspots, and the mechanism of the spatial distribution of Starbucks. Theresults show that: 1) the spatial distribution pattern showed aggregated and zonal distribution patterns; 2) Starbucks in Guangzhou exhibit northeast-southwest direction characteristics; 3) Starbucks in Guangzhou show ring structure on the spatial distribution density, and its den- sity progressively decreasing from insiders to outsiders; 4) the factors of the internal transportation hub, Trade Areas, as well as the level of urban land significantly affect the spatial distribution of Starbucks stores.
出处
《人文地理》
CSSCI
北大核心
2017年第6期47-55,共9页
Human Geography
基金
国家自然科学基金(41571129
41301140)
国家社会科学基金(13BGL091)
关键词
星巴克
空间分析
空间分布
区位选择
Starbucks
spatial analysis
spatial distribution
location selection