Factors Influencing Visitor's Choices of Urban Destinations in North ...

Arts & Culture Museums Historymuseums Historic sites Other Visual Arts Art galleries Art-relatedevents &festivalsTable 3: Attractions Matrix 6Environment &Built Form Physical Setting Waterfronts &beaches Other geographicfeatures Urban Amenities Parks & greenspaces Shopping areas Businessdistricts Built Form General buildingarchitecture Specificstructures ofinterestEntertainment PopularEntertainment Amusements &theme parks Spectator sports Casinos Participationsportsopportunities Events &festivals Night clubs CulturalEntertainment Opera Theater Ballet OrchestraAccommodation& Food Accommodation Luxury hotelrooms Food High-endrestaurants Food-relatedevents & festivals Range ofrestaurantsD. Econometric ApproachBefore turning to the discussion of how the knowledge base represented by the databaseand model will be leveraged to provide a conceptual framework and practical guidancefor the development of a tourism strategy, it will be useful to briefly consider the basicelements of our technical approach.Because attraction portfolios tend to change slowly over time, Global Insight took theapproach to pool data across 50 North American cities and estimated a series of equationsrelating the city attraction portfolios to leisure visitations.In cases where there were missing data for certain types of attractions, this econometricapproach allowed Global Insight to obtain robust results. More substantively, the crosssectionalsample means that the model reflects a much greater range of tourismexperience than possible in a single-city approach. Furthermore, the methodology permitsmore credible measures of potential changes in future attractions or tourism promotionalinitiatives outside the range of the historical experience in the data for a single city.With the guidance from the literature review and collected attraction and non-attractiondata, a series of cross-sectional models were estimated. These models estimated thenumber of leisure tourist visitations as a function of the attractions defined for each of theselected North American cities in 2002. Non-attraction variables were also included inthe model for the same period of time.The structure of the model enabled Global Insight to identify and rank the relative returnoffered by each type of attraction in terms of the number of visitations it can generate.Based on these observations, Global Insight was able to identify a subset of the most6 Please refer to Appendix A for a detailed category listing.9