Abstract:

The trade area hierarchical division has always been a hot issue for business practitioners and scholars. Under the background of urban commercial space expansion, traditional hierarchical division of trade areas from a single perspective can no longer meet commercial market segmentation needs. Trade areas can be affected by many factors, and the scientific identification and division of them need multi perspective cross analysis, which is the basis for analyzing commercial prospects and optimizing commercial space structures. This will provide scientific basis for the healthy development of commercial facilities and efficient utilization of urban commercial space, and help to solve the problems of overlapping and excessive competition in the rapid development of urban commerce. In terms of model definition, we refines and defines two types and four market area methods, which are distance proximity, quotient, ratio, primacy ratio, respectively. By comparing their advantages and disadvantages, it is found that these four perspectives can only reflect a certain feature of trade areas, but can not depict the overall spatial distribution of trade areas, so we systematically construct the interactive model of multi-market areas. In case study, we carry out empirical research by using mobile phone signaling data in Shanghai, taking Xujiahui commercial center, Lujiazui commercial center and East nanjing road commercial center as examples. The results show that: the composite market area model can comprehensively reflect the influence of spatial distance, passenger flow and patronage probability on trade areas, and can make up for the deficiency of the trade areas division method from the perspective of single market areas. Compared with the traditional adjacent trade area, the new model not only has low overlapping degree, but also its spatial distribution is more in line with the reality. In commercial practical application, business practitioners can flexibly combine different market areas according to market demand, which has certain universality.

Key words: retail industry, retail trade area, commercial center, store location selection, Shanghai

摘要：

对商业设施的商圈进行科学识别与划分是分析其商业前景、优化商业空间结构的基础。本研究首先提炼和界定了商圈划分的两种类型、四种市场域方法，分别是基于到访比例的邻近型与份额型市场域、基于出行概率的共享型和首位型市场域，通过比较其优劣发现这四种视角仅能反映商圈的某一特征，无法刻画出商圈空间分布全貌。据此，尝试提出了一种新的复合型市场域模型，并应用手机信令数据，以上海市商业中心为例，对其商圈划分进行了实证检验。研究发现：复合型市场域模型可以综合反映空间距离、客流规模和惠顾概率等要素对商圈的影响，可以弥补单一市场域视角下商圈划分方法的不足。与传统邻近型相比，基于复合市场域模型识别划分的商圈不仅重叠度低，而且其空间分布更加符合实际。

关键词: 零售业, 零售商圈, 商业中心, 店铺选址, 上海