Selecting Location of Retail Stores Using Artificial Neural Networks and Google Places API

Abstract

Selection of locations of retail stores (similarly, facilities) is a kind of decision problem in which the researcher targets maximum profit or minimum cost by using statistical or judgmental forecasts. In many cases, statistical forecasts are based on survey data or data collected for an other store which are not reliable as the survey may not measure the potential demand or the other store that is used for comparison may not be similar enough. Addition to this, estimating the potential demand for a non-existing store can be expensive. Google Places API is a software library which provides a nearby search utility that is able to report several kinds of institutions, establishments and other objects placed in a circumferential area for a given radius. In this paper, we suggest a method which is based on estimating the relation between environmental properties of existing retail stores and their financial rankings. We remark that feed forward neural networks are capable to reveal such a relation as they permit using both integer, real and binary variables in the model and non-linearity. We apply our method on the location selection problem of a retail stores chain in Turkey (Note 1) which has 144 clients in its organization. Expert opinions in the organization show us applicability of this method is high in similar situations.

An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks

“…It is not difficult to find that, although these annotation methods were different, the categories in each paper were very similar. Like the work of Goodchild and Satman [27,28], we also used Google Places, which is widely used to provide location-related information and represent the ground truth labels. As a public data source, Google Places can provide detailed information about 100 million places across a wide range of categories, from the same database as Google Maps.…”