Geographic Big Data’s Applications in Retailing Business Market

Abstract

Location has been considered as a determinant factor for retail location’s success since the 1970s. Various researches have inspected different aspects in order to understand the market and select the best site for each store, from classifying the location to generating hierarchical trading areas. Previous researches done by human geographers may provide great insights into one specified site. However, their traditional methods are likely to be too expensive and difficult to expand to the whole city and even the whole nation. This chapter explores methods based on a large amount of data, which are currently being used as commercial solutions. By inspecting POI data and other social media data within pre-built grids, different types of business districts could be discovered, and site characteristics could be summarized. Two famous fast-food retailers have been explored in this study. Results indicate that more than 70% of stores can be explained by business district classifications. Also, results indicate that similar POI aggregations can be analyzed, from which 83% and 90% of stores’ locations can be explained for two retailers separately. Besides, the distance relationship between POIs and stores can contribute to explain the location of retail stores.