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

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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
easonable attraction types for Toronto and Ottawa to pursue in expanding its attractionportfolio to enhance future visitations.E. Data CollectionThe data required for this study can be classified into three categories: visitations data,attraction data, and non-attraction data.Visitations DataThe visitations data Global Insight analyzed focused on tourists travelling for leisure(excluding trips to visit friends and relatives) and travelling at least 50 miles from theirorigin. These visits were totalled regardless of the visitor source—whether domestic orinternational. The data sources included D.K. Shifflet, the Office of Travel and TourismIndustries, and Statistics Canada. Global Insight focused only on the most recent year ofdata, which was 2002. (Please see the Technical Appendix for more information aboutthe visitations data.)Attractions DataFor the purpose of this study, the attractions database was created for each selected NorthAmerican city. The attractions data were the independent (or explanatory) variables inour regression analysis. The database contained (1) the total count of attractions (i.e. thenumber of amusement parks); and (2) the quality-rated attractions for each category, subcategory,and type of attraction.Since some travel publications 7 provided consistent quality ratings of attractions acrossthe selected North American cities, Global Insight utilized these ratings to rank touristattractions. The quality rating of attractions ranged from a no-star to a three-star rating,with no star indicating a “worth seeing” attraction (value of zero was assigned to this typeof attraction) and a three-star indicating a “highly recommended” attraction (value ofthree was assigned to this type of attraction). To capture the quality ratings of touristattractions in the database, the value of three was assigned to a three-star rating, value oftwo to a two-star rating, value of one to a one-star rating and value of zero to the type ofattraction that was not rated by either travel guide, but is considered worth seeing. Theattractions were totalled for each category, sub-category, and type of attraction. The totalswere calculated for the total count of attractions, three-star, two-star-, and one-star-ratedattractions. For each city, a summary sheet was also included to show the total count, thetotal count of three-star, two-star, and one-star-rated attractions across all categories.Tourist visitations data was separately regressed on the attraction categories, theattraction sub-categories and the specific types of attractions. For example, the number oftourist visitations was regressed on the number of attractions in the arts and culturecategory. Secondly, the visitations data was regressed on the number of museum subcategories.Finally, tourist arrivals were regressed on the number of history museums.7 Please see “Publications” section of the report for more information about these travel publication sandhow they were selected.10
Page 2 and 3: Table of ContentsI. EXECUTIVE SUMMA
Page 4 and 5: I. Executive SummaryHighlightsThe r
Page 6 and 7: Next StepsThis project has surfaced
Page 8 and 9: IV.coefficients from these models p
Page 10 and 11: series, simple autoregressive and
Page 14 and 15: The tourist visitations data was al
Page 16 and 17: Hotel Room Count and Hotel Property
Page 18 and 19: Table 9: Michelin’s RatingsRating
Page 20 and 21: value below one suggests that the s
Page 22 and 23: Table 13: Individual Attractions Th
Page 24 and 25: Table 16: Significant Results 14Equ
Page 26 and 27: The adjusted R 2 for this model is
Page 28 and 29: • On average across all cities, b
Page 30 and 31: Model 5C 17 : In Model 5C, the numb
Page 32 and 33: The model overpredicts the number o
Page 34 and 35: Attraction Elasticities for Ottawa
Page 36 and 37: Table 22: Marketing Budget Elastici
Page 38 and 39: VII.Technical AppendixA. Attraction
Page 40 and 41: • Metropolitan Opera House in New
Page 42 and 43: Model 3: Three-Star Quality-Rated A
Page 44 and 45: Model 5B: Three-Star Quality-Rated
Page 46 and 47: 3. The cognitive perspective involv
Page 48 and 49: also found that almost half of the
Page 50 and 51: Also, the model seems to completely
Page 52 and 53: strengths and its weaknesses, devel
Page 54 and 55: Items from QuestionnaireTable 23: M
Page 56 and 57: Tourism Demand/Econometric Modellin
Page 58 and 59: Table 25: Types of Explanatory Vari
Page 60 and 61: Results: Lim and McAleer found that
Page 62 and 63:
Richards, Greg. (2002). “Tourism
Page 64 and 65:
Figure 4: Overseas Visitors to U.S.
Page 66 and 67:
Figure 6: Cities Person-Trips Volum
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