《Modeling Shopping Center Location Choice: Shopper Preference-Based Competitive Location Model》

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作者
来源
JOURNAL OF URBAN PLANNING AND DEVELOPMENT,Vol.145,Issue1
语言
英文
关键字
Shopping center; Shopper preference-based competitive location model (SPCLM); Diversity index; Retail agglomeration index; Location selection; RETAIL; AGGLOMERATION; FACILITIES; DESIGN; SITE; ATTRACTIVENESS; HIERARCHY; CONSUMERS; STORES; GIS
作者单位
[Wu, Shan-Shan] Tongji Univ, Dept Architecture, Siping Rd, Shanghai 200082, Peoples R China. [Kuang, Hua] Guangxi Normal Univ, Coll Phys Sci & Technol, Guilin 541004, Peoples R China. [Lo, Siu-Ming] City Univ Hong Kong, Dept Architecture & Civil Engn, Kowloon, Tat Chee Ave, Hong Kong 999077, Peoples R China. Lo, SM (reprint author), City Univ Hong Kong, Dept Architecture & Civil Engn, Kowloon, Tat Chee Ave, Hong Kong 999077, Peoples R China. E-Mail: achiperfection@gmail.com; bcsmli@cityu.edu.hk
摘要
The location of a shopping center is important for its future development. However, specific models for shopping center location selection were rarely examined in previous studies. This paper proposes a shopper preference-based competitive location model (SPCLM) to solve the location problem for shopping centers. Five attractive attributes are identified for model construction: size, diversity of the tenant inside the shopping center, retail agglomeration near the shopping center, distance to metro stations, and distance between consumers and shopping center. The parameters of the five variables are calibrated using four sample areas of Hong Kong. To benchmark the improvement of the model, an existing model from the literature is also calibrated for comparison. Furthermore, an onsite survey is carried out to validate the resulted optimal location of both SPCLM and the model from the literature. The results show that the optimal locations predicted by SPCLM have higher pedestrian flow rates than do the optimal location predicted by the model from the literature and the real locations of the existing shopping center. As a result, the proposed model significantly improves the prediction accuracy. Thus, it is a useful tool for both retail and community development.