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

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The tourist visitations data was also regressed on the number of quality-rated attractions(three-, two-, or one-star rated) using the attractions categories, the attractions subcategories,and the specific types of attractions. For example, the number of touristvisitations was regressed on the number of three-star-rated attractions in the arts andculture category. Secondly, the visitations data was regressed on the number of three-starratedmuseums. Finally, tourist arrivals were regressed on the number of three-star-ratedhistory museums.Table 4: City Attractiveness Database for New York (Extract)Count Arts&Culture MuseumsGeneral HistoryMuseumsHistoric SitesOtherThemedMuseumsVisualArtsArtGalleries2 1 2 12 1 2 32 0 30 0 31 3 22 0 22 0 11 0110222222Arts relatedevents andfestivalsTotal Countof Attractionsby Category 34 27 2 17 8 7 7 0Total numberof attractionswith a threestarrating 4 1 0 0 1 3 3 0Total numberof attractionswith a twostarrating 15 13 2 9 2 2 2 0Total numberof attractionswith a onestarrating 8 6 0 6 0 2 2 0ExamplesThe NY HistoricalSocietyEllis IslandImmigrationmuseum;the SouthStreetSouth Street SeaportSeaport; Lower Museum;Manhattan; Lower EastDowntown and Sidethe TenementNeighborhoods; Museum;East Village; AmericanSoho; Cathedral Museum ofof St. John the NaturalDivine HistoryMuseumforAfricanArt;MuseumofModernArtSource: Global Insight, Inc.11
Table 5: Summary Sheet for New YorkNEY Overall score - Conde Naste Traveller 82.70%Overall score - Michelin 3Total count of attractions 294Number of attractions with *** rating 61Number of attractions with ** rating 81Number of attractions with * rating 23Public Transportation/Infrastructure Overall score - Places Rated Almanac 99.43%Large hub (yes-1, no-0) 1International airport, nonstopinternational destination (yes-1, no-0) 1Number of Missing Attractions (tcma) 7Source: Global Insight, Inc.Non-Attraction VariablesThe number of visits was not only regressed on different types of attractions, but also ona variety of non-attraction variables. These variables were included to control forvariations in other visitor influences from city to city; and to include measures of tourisminfrastructure that also varied among the 50 cities. The literature review had suggestedthe importance that infrastructure plays in attracting tourists and contributing to theoverall experience. Consequently, Global Insight added measures of hotel property count,hotel room count, and public transportation score to the modelling process.A number of these variables ultimately proved useful in our analysis, while others didnot. The detailed discussion of our modelling results will refocus on the usefulness ofthese non-attraction variables.Table 6: Non-Attraction VariablesType of VariableOverall City ScoreFilter Variable for a Large Hub AirportPopulation by State or Province Total Count of Missing AttractionsHotel Room CountPopulation DensityProperty CountProximity to Major Metro AreasPublic Transportation ScoreCity-by-city Marketing BudgetsFilter Variable for International AirportOverall City Score: The overall city score captures the quality rating for a particularcity. This variable was obtained from Michelin's travel guide, which provided aconsistent ranking for all 50 cities. The overall score ranges from a three-star rating to aone-star rating. Global Insight assumed that a higher overall score would increase thenumber of tourists.Population by State or Province: Population data was obtained from Statistics Canadaand Global Insight databanks. The data were obtained for 2002.12
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 12 and 13: Arts & Culture Museums Historymuseu
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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