Customer attractiveness evaluation and classification of urban commercial centers by crowd intelligence

Abstract

Evaluation on urban commercial centers' attractiveness not only benefits business strategy making and location choice, but also helps traffic management and urban planning. Traditionally, it is studied using questionnaires and field research, which are labor-intensive and time-consuming. To overcome these problems when evaluating the urban commercial centers' attractiveness, massive data analytics with datasets from taxi traffic, population, area, and road networks are adopted in this paper. Taking fifteen commercial centers at Shenzhen as a case study, a Cyber-Physical-Social System is built up to deal with these massive data for statistical analysis. An “Attractiveness Degree Model” is proposed to describe the degree to which customers desire to visit a commercial center. Then attractiveness thematic maps are drawn. Results show that YiTianJiaRiGuangChang has the highest attractiveness degree even though it has a small size and low commercial value. The attractiveness degree rankings are corroborated by annual customer satisfaction survey from Shenzhen Retail Business Association. Attractiveness thematic maps show that about 50–65% visits by taxis are within 5 km range. These results can be applied to support market analysis, urban planning, traffic management, and related areas.