广州市中心城区餐饮店空间分异与机制差异研究——基于传统店与外卖店的对比

O2O模式（Online to Offline）使餐饮店经营模式与空间分布发生巨大变化，餐饮店空间分布现象与机制成为研究热点。本文将餐饮店分为仅提供堂食服务的传统店和同时提供堂食与餐饮配送服务的外卖店，利用核密度、双变量空间自相关、半变异函数、标准差椭圆、空间计量模型等方法，分析广州市中心城区传统店与外卖店的空间分异与机制。研究发现：① 2020年传统店主要集聚于自荔湾区多宝街道东至天河区石牌街道；外卖店呈多中心分布，以天河区天河南街道、冼村街道、海珠区江南中街道和赤岗街道为核心；二者在越秀区中部、天河区西南部、车陂街道、海珠区西北部等有多处高高集聚区。② 两种餐饮店空间分异较明显，易受随机性因子影响；外卖店较少分布在越秀区和荔湾区，整体分布范围较小，仅有很弱的空间自相关性，更多呈零散分布特征。③ 餐饮店空间分布受人口密度和餐饮店集聚的正作用；但受教育程度、路网密度、邻近高等院校、公交站、商圈等因素能提高外卖店比例，外卖店有更高店铺付租能力，更邻近地铁站和低经济密度地区，传统店则与其相反。

Spatial heterogenicity and mechanism difference of restaurant in the central urban area of Guangzhou: A comparison between traditional restaurant and take-out restaurant

O2O (Online to Offline) has greatly changed the business and spatial distribution of catering industry. Spatial distribution phenomenon and mechanism of restaurant have become a research hot topic. We divide restaurants into traditional restaurant and take-out restaurant. The former one only provides in-room service while the latter one provides both in-room and food delivery services. Guangzhou, one of the top ten gourmet cities in China, has a large catering industry scale. But few studies focus on spatial distribution change of restaurant and rapid development of take-out restaurant in Guangzhou. Using methods of kernel density, bivariate spatial autocorrelation, semi-variance function, standard deviation ellipse and spatial econometric model, we analyze the spatial differentiation and mechanism of traditional restaurant and take-out restaurant in the central urban area of Guangzhou. The results show that: (1) In 2020, traditional restaurants mainly gather from Duobao Street in Liwan District to Shipai Street in Tianhe District. Take-out restaurants are distributed in multiple centers, with Tianhenan Street, Xiancun Street, Jiangnanzhong Street and Chigang Street as cores. There are many bivariate high-high clustering areas in the middle of Yuexiu District, the southwest of Tianhe District, Chebei Street and the northwest of Haizhu District. (2) The spatial differentiation of two types of restaurant is obvious and easy to be affected by random factors. Traditional restaurant and take-out restaurant have obvious spatial heterogenicity characteristics. Compared with traditional restaurant, the kernel density of take-out restaurant is lower in Yuexiu District and Liwan District. Its overall spatial distribution range is smaller and more southeasterly. It has more obvious scattered spatial distribution characteristics, smaller spatial heterogeneity, weaker spatial autocorrelation and smaller spatial autocorrelation range. (3) The spatial distribution of restaurants is positively affected by population density and restaurant agglomeration. Higher education level, higher road density, higher proximity to universities, bus stops and business districts can increase the proportion of take-out restaurants. Take-out restaurants have higher rent paying capacity and are closer to subway stations as well as low economic density areas. However, traditional restaurants are the opposite. O2O promotes different responses of restaurant to its surrounding influencing factors and leads to the spatial heterogenicity of traditional restaurants and take-out restaurants.