  If you were to open your own cafe, would you not want to effortlessly
identify the most suitable location to set up your shop? Choosing an optimal
physical location is a critical decision for numerous businesses, as many
factors contribute to the final choice of the location. In this paper, we seek
to address the issue by investigating the use of publicly available Facebook
Pages data---which include user check-ins, types of business, and business
locations---to evaluate a user-selected physical location with respect to a
type of business. Using a dataset of 20,877 food businesses in Singapore, we
conduct analysis of several key factors including business categories,
locations, and neighboring businesses. From these factors, we extract a set of
relevant features and develop a robust predictive model to estimate the
popularity of a business location. Our experiments have shown that the
popularity of neighboring business contributes the key features to perform
accurate prediction. We finally illustrate the practical usage of our proposed
approach via an interactive web application system.
