  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, 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 based on 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.
