Etienne Pinel

This research paper analyses different trade area techniques based on demographic, economic and geographic variables. It compares the different methods and discuss the appropriateness and the limitations of each one. The research study includes 14 shopping centres in which customer geo-referenced information is available. The techniques used consist of rule-based methods, penetration trade areas and spatial interaction models. The rule-based trade areas ' findings show a simplistic view of trade areas that do not represent the observed spatial distribution of customers. Thiessen polygons take into account the location of competitive centres but result in exaggerated trade areas delimitation. Customer spotting trade areas differ based upon the variable used: customer counts or expenditure. Spatial interaction models used the findings uom the customer spotting technique to define probabilistic trade areas based on distance and gross leaseable area of the shopping centres and show that the two variables have varied impact in predicting consumer flows. Population density, trade area spatial extent and customer average travel are interesting variables to attempt to differentiate shopping centres for investment or analytical purposes. Similarly, settlement patterns and accessibility are several traditional indicators that can be appropriate to classify shopping centres. With the use of a limited number of variables in the spatial interaction models, it is difficult to determine which model's parameters were the most appropriate. However for further research, with more resources, significantly tested variables and parameters there is no doubt that spatial interaction model and potentially location-allocation models are the most useful trade areas techniques to represent spatial distribution of patrons.