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

ABSTRACT

The 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.

KEYWORDS:

• Crowdsourced data
• weighted Voronoi diagram
• shopping malls
• potential over-supply

Notes on Contributors

Jiabin Gao is a Ph.D candidate in the Department of Land Management, Zhejiang University, Hangzhou, China.

Wenz Yue is professor of urban geography in the Department of Land Management, Zhejiang University, Hangzhou, China.

Xinyue Ye is professor of spatial econometrics in the Department of Informatics, New Jersey Institute of Technology.

Dong Li is the deputy director of the Technology Innovation Center, Beijing Tsinghua Tongheng Urban Planning and Design Institute, Beijing, China.

ORCID

Wenze Yue http://orcid.org/0000-0002-7533-3294

Xinyue Ye http://orcid.org/0000-0001-8838-9476

Additional information

Funding

This work has been supported by the Key Program of the National Social Science Foundation of China [grant number 14AZD124], National Natural Science Foundation of China [grant numbers 41671533 and 41871169], and the Fundamental Research Funds for the Central Universities.