在中國隨著O2O電子商務的流行和普及，產生了像“美團”、“餓了麼”等O2O互聯網企業，使得人們獲取信息的能力空前加強，並對城市實體商業經營產生了重大影響。同時，物流業的即時配送發展迅猛，讓步行距離不再是限制人們消費的主要因素，線上餐飲成為人們飲食消費的常態。研究新技術下城市餐飲業的空間分佈，有利於瞭解餐飲業的發展現狀，對引導產業發展有重要意義。但變遷的趨勢，目前並沒有成型的研究機制，並且變化機制也有待進一步的研究。本次研究以深圳市為例，通過互聯網開放數據獲取“美團”、“安居客”等大數據資料。相較於傳統走訪式的空間統計方法，互聯網大數據存在數據體量大、資料類別豐富、定位精准等特點。本次研究以城市餐飲業空間佈局規律為切入點，在城市中心地理論、區位論、地租理論的指導下，採用核密度分析、空間自相關以及多變量分析等方法，從總體和局部對比實體餐飲店和線上餐飲店的分佈特徵。进而探討兩種餐飲店形態和人口密度、交通因素和平均房價的關係。本次研究結果如下：
（1）深圳市實體和線上餐飲店的密度都呈現“西密東疏、南密北疏、沿海密，內陸疏”的空間格局，以深圳市地鐵分佈形成線上餐飲活躍區域。平均消费和网络口碑随着等级的提升逐漸向高密度核心區轉移。
（2）人口密度和道路密度與两种餐飲店相关性較大，人口和道路密集的街道两种餐飲店也聚集分布；人口密度与中档消费餐饮店相关性较大，道路密度與中低檔餐飲店相关性较大；餐飲店距離公交車站和地鐵車站越遠的街道，餐飲店越稀疏；平均房價因素與餐飲店密度分布相关性較低，但平均房價與高檔消費層級和高網絡口碑的实体店相关性较高，說明高品質服務的實體店选址在高房价地區的可能性较大；平均房價對两种餐飲店網絡口碑的相關性較低，说明网络口碑的好坏不依赖房价的高低。
（3）多變量分析下，对深圳市餐饮业的影响表现为，道路密度因素>人口密度因素>平均房价因素，人口密度對實體餐飲店密度的相关系数更高，而道路密度對線上餐飲店密度的相关系数更高。实体店作为影响因素后，回归方程拟合度非常高，两者相似度极高。這背後的原因可能是，雖然電子商務發展迅速，同時人們獲取餐飲資訊的能力也不斷加強，但是整體的空間結構、規劃方式和人们的生活方式沒有發生改變，線上餐飲店還是當前城市結構下对實體餐飲店的重要補充。

With the popularization of the O2O e-commerce, China appeared many O2O Internet companies such as "Mei Tuan" and "E Le Me". Which make people access to information strengthens and has significant influenced on the offline restaurants in the city. At the same time, instant delivery of the logistics industry grows rapidly, and it makes walking distance is not the main factor to restrict customers to make eating choice. Selecting food online has become the normalcy for Chinese customers. Studying the spatial distribution of urban catering industry under new technology is helpful to understand the development status of catering industry and is of great significance to guide the development of the industry. Shenzhen city is taken as an example in this study，and the big data comes from the open data of "Mei Tuan" and "An Ju Ke" APP. Compared with the traditional visiting-type spatial statistical method, the big data of the Internet is characterized by large data volume, abundant data categories and accurate positioning. In this study, the spatial distribution rules of urban catering industry are taken as the starting point. Under the guidance of Urban Central Location Theory and Land Rent Theory, Kernel Density Analysis, Spatial Autocorrelation and Multivariate Analysis are adopted to compare the distribution characteristics of offline and online restaurants from the overall and part perspectives. Then the relationship between the two types of restaurants and local population density, traffic factors and average housing price was discussed. The results are as follows：
(1) The spatial distribution: look from the density, both of entities and online restaurants being assemble trend, Which appears "Less in the east and more in the west"，“The south is dense and the north is sparse”，“The coast is dense and the interior is sparse” spatial pattern; From the point of view of the average consumption level and the level of Internet word-of-mouth, the restaurants of each consumption level and word-of-mouth level all present in Futian district and Luohu district, forming the core and diverging to the periphery. In terms of correlation, offline restaurants are more dependent on low- medium consumption, while online restaurants are more dependent on medium consumption and more concerned about word-of-mouth.
(2) The population density has an impact on the pattern of two types of restaurants, especially on medium consumption restaurants. Most of densely populated street district are also packed with offline and online restaurants. However, under different population density levels, the aggregation degree of physical and online restaurants is different. The road density factor has a larger influence on the medium and low-grade restaurants. Under the different road density grades, the aggregation degree of offline and online restaurants is different. The distance from restaurants to buses and subway stations has an impact on the distribution of two types of restaurants. The closer the distance, the more concentrated the restaurants are. The average house prices factors on the pattern of establishments were less affected, but prices for high-end consumption level and the impact of high Internet word of mouth is significant, shows that the high-quality service offline restaurants more tend to house prices higher areas. However, the correlation between house price and consumption level and word of mouth of online is relatively low, indicating that high-end consumption and restaurant service with high word of mouth are more inclined to brick-and-mortar stores.
(3) Under multiple linear regression analysis, population density has a greater positive impact on the density of offline restaurants, while road density has a greater positive impact on the density of online restaurants. Pattern of online and offline restaurants are similar, where offline restaurants gather, online restaurants will also gather. The reason may be that even although the ability of people to obtain restaurants information has been constantly strengthened by O2O e-commerce, but the overall spatial structure、 planning method and people's lifestyle have not changed in Shenzhen city, so online restaurants are still an important supplement to the distribution of offline restaurants under the current urban structure.

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