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Title: Incorporating seasonal visitor demand in retail location modelling
Author: Newing, Andrew
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2013
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Retail location planning within the grocery sector employs sophisticated modelling to evaluate the trading potential of proposed new stores and investments. Demand side expenditure estimates are commonly used in conjunction with spatial interaction modelling to analyse consumer flows, determine store catchment areas and predict revenue in advance of store construction. Retailers note that these revenue predictions often underestimate demand in tourist areas, where non-residential demand, originating from visitors, can generate considerable seasonal sales uplift at the individual store level. Modelling visitor demand of this nature is an under-researched area and is addressed within this thesis in order to improve the modelling and revenue estimation capabilities of location planning teams, and to enhance understanding of tourism’s local economic impact. This research is carried out with the support of Sainsbury’s (as an ESRC CASE award partner) and specifically considers location-based modelling for application in the grocery sector. The thesis draws considerably on stores within Cornwall and Kent, especially those in popular (and highly seasonal) coastal resorts. With rare access to store and consumer loyalty card data, this thesis identifies the impact of visitor expenditure on store-level grocery demand. Subsequently, a methodology is developed in order to estimate small-area grocery demand in highly seasonal (coastal) tourist resorts, accounting for the spatial and seasonal variations driven by visitor expenditure. These demand estimates are used in conjunction with a Spatial Interaction Model (SIM) (developed and calibrated specifically for this thesis) to estimate store revenue and market shares in tourist areas. This thesis demonstrates that demand side estimates and a spatial modelling approach are able to generate robust revenue predictions for stores in highly seasonal tourist resorts. The discussion clearly highlights the versatility of the model in addressing demand and supply side interventions, and outlines the impact of this form of analysis on store location based decision making in tourist resorts.
Supervisor: Clarke, Graham ; Clarke, Martin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available