Mining heterogeneous urban data for retail store placement.

Retail store placement problem has been extensively studied in both academic and industry as it decides the retail success of a business. Existing methods exploited either consumer studies (e.g., consultant-based solutions) or geographic features (e.g., points of interests) to settle it. However, due to the limitations of data sources (i.e., costly in time and labor), none of these methods could provide an accurate and timely solution. In this paper, we rethink retail store placement problem by mining heterogeneous urban data. In particular, unlike existing works which only used geographic features or consumer studies solely, we extract three categories of features (i.e., human movement features, commercial area features and geographic features) from heterogeneous urban data, and integrate them into various machine learning models to predict the popularity of a prospective retail store in the candidate area. We conduct a case study with real data in Shenzhen to demonstrate the predictive power of our proposal.