Locating a shopping centre by considering demand disaggregated by categories

We model a shopping centre. The demand for goods and services in shopping centres is classified in four different categories: food, leisure, household goods and clothing. As some of these sectors do not provide essential goods and services, a Huff customer-choice model is applied that sets a parameter absorbing any lost demand when there is a shortfall in customer attraction. For each category, the parameters for the Huff model are estimated both globally (by means of ordinary least squares, assuming the same effect for the parameters throughout the entire market), and locally (using geographically weighted regression, considering that parameters depend on the customers’ location). The proposed model was applied to a real data case on the island of Gran Canaria (Spain) to determine the best location for a shopping centre selling all four categories of goods. Finally, a study is conducted to determine how robust the solution is with respect to the lost demand parameter, and a comparison is made between the solutions obtained, using both global and local calibration methods.