Identification of Potential Over-Supply Zones of Urban Shopping Malls: Integration of Crowdsourced Data and Weighted Voronoi Diagram

Abstract

ABSTRACTThe market saturation issue of urban shopping malls has attracted considerable attention in China in recent years. In order to rapidly identify potential over-supply zones and inform policy-makers, this study developed a new model by integrating a weighted Voronoi diagram and crowdsourced data. The model was then tested in the city of Hangzhou, China. First, crowdsourced data such as user reviews of shopping were collected to measure the weights of malls. Second, by using population and floor space as parameters, an over-supply index was established for over-supply zone delimitation. This study offers a fast and low-cost approach for measuring consumption activities at a fine scale, and shows the merits of integrating classical analysis models and big data. Moreover, long-term user reviews and recommendation datasets with timestamps could be used to monitor the status of market health. From a bottom-up perspective, the market boundary map and over-supply index could constitute an important database for policy formulation through crowdsourced data.