A Statistical Approach to Inferring Business Locations Based on Purchase Behavior

Transaction data obtained by Personal Financial Management (PFM) services from financial institutes such as banks and credit card companies contain a description string from which the merchant identity and an encoded store identifier may be parsed. However, the physical location of the purchase is absent from this description. In this paper we present a method designed to recover this valuable spatial information and map merchant and identifier tuples to physical map locations. We begin by constructing a graph of customer sharing between businesses, and based on a small set of known "seed" locations we formulate this task as a maximum likelihood problem using a model of customer sharing between nearby businesses. We test our method extensively on real world data and provide statistics on the displacement error in many cities.