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

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Table 13: Individual Attractions That Did Not WorkArts & CultureVisual ArtsArts Related Events and FestivalsEnvironment and Built FormPhysical SettingGeographic FeaturesAccommodation and FoodFoodFood Related Events and FestivalsRange of RestaurantsEntertainmentPopular EntertainmentSpectator Sports OpportunitiesPopular Events or FestivalsNight ClubsCultural EntertainmentBallet PASymphony Orchestra PASource: Global Insight, Inc.Step 2: From Step 1, the attractions with positive, statistically significant coefficients andrelatively high adjusted R-squared were sequentially combined into a regression witheach of the other attractions. Global Insight found that this significantly increased thevalues of the adjusted R 2 . Table 14 summarizes the equations with two types ofattractions that yielded relatively high adjusted R 2 12 . Combining two types of attractionssignificantly improved the adjusted R 2 . Please note that at this stage, Global Insightdecided to proceed with only one measure of attraction count–Number of Attractions.When the total count of attractions was used, a wide variety of attractions turned out to besignificant. The other two measures of attraction count confirmed our findings but limitedthe range of attractions. Since Global Insight wanted to ascertain the most complete listof attractions possible, only total count of attractions was selected for further analysis.Table 14: Combining Two Types of Attractions–Significant Results 13Type of AttractionType of Second AttractionAdj.R 2Casinos (TC)Shopping Areas (TC)0.14Specific Structures (TC)Amusement Parks (TC)0.15Casinos (TC)Built Form (TC)0.15Casinos (TC)Specific Structures (TC)0.20Popular Entertainment (Q1)Waterfronts & Beaches (Q1)0.33Popular Entertainment (Q1)Amusement Parks (Q3)Shopping Areas (Q1)Urban Amenities (Q3)0.390.50Amusement Parks (Q3)Amusement Parks (Q3)Visual Art Galleries (Q3)Shopping Areas (Q3)0.520.52Source: Global Insight, Inc.12 The adjusted R 2 statistic indicates the degree to which the variation of the independent variable(visitations) from its mean is explained by the dependent variables used in the regression. An adjusted R 2value of 0.9 indicates that 90% of the variation is explained.13 Q3 = three-star rating, Q2 = two-star rating, Q1 = one-star rating, and TC = total count.19
Step 3: In Step 3, more attraction variables were tried in the equations summarized inTable 14. In addition, non-attraction variables were also examined to see whether byadding them in these regressions the adjusted R 2 was improved.Table 15: Non-Attraction VariablesType of VariableOverall City Score (from Michelin)Population by State or ProvinceHotel Room CountProperty CountPublic Transportation ScoreFilter Variable for International AirportFilter Variable for a Large Hub AirportTotal Count of Missing AttractionsPopulation DensityProximity to Major Metro AreasCity-by-city Marketing BudgetsSignificanceNoYesYesYesYesNoNoNoYesNoYesSource: Global Insight, Inc.Having completed these three steps, the following combinations of attraction and nonattractionvariables emerged. Table 16 presents five equations that proved to be the mostpromising in explaining the variation of leisure visitors among the 50 North Americancities.20
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 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 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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