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

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Model 3: Three-Star Quality-Rated Attractions, Total Count of Casinos andProperties CountDependent Variable: Total Number of Visits(Millions of Visitors)Sample: 2002Number of Observations: 50VariableCoefficientStd. ErrorT-StatConstant2.760.684.08Specific Structures (Q3)1.950.543.60Amusement Parks (Q3)6.670.768.76Casinos (TC)0.430.104.31Shopping Areas (Q3)0.810.263.06Properties Count0.0070.0023.50Adj. R-sq0.76S.E. of Reg.2.44Source: Global Insight, Inc.Model 3B: Three-Star Quality-Rated Attractions, Total Count of Casinos,Properties Count, and Population DensityDependent Variable: Total Number of Visits(Millions of Visitors)Sample: 2002Number of Observations: 50VariableCoefficientStd. ErrorT-StatConstant-0.040.79-0.05Specific Structures (Q3)1.880.444.27Amusement Parks (Q3)6.690.6210.84Casinos (TC)0.410.085.00Shopping Areas (Q3)0.760.213.56Properties Count0.0050.0023.54Population Density0.130.034.89Adj. R-sq0.84S.E. of Reg.1.98Source: Global Insight, Inc.39
Model 4: Three-Star Quality-Rated Attractions, Total Count of Casinos, PublicTransportation Score, and Population DensityDependent Variable: Total Number of Visits(Millions of Visitors)Sample: 2002Number of Observations: 50VariableCoefficientStd. ErrorT-StatConstant-5.424.97-1.09Casinos (TC)0.390.104.01Amusement Parks (Q3)7.090.749.56Specific Structures (Q3)2.870.446.47Public Trans. Score7.485.551.35Population Density0.140.034.23Adj. R-sq0.78S.E. of Reg.2.38Source: Global Insight, Inc.Model 5: Three-Star Quality-Rated Attractions and Room CountDependent Variable: Total Number of Visits(Millions of Visitors)Sample: 2002Number of Observations: 50VariableCoefficientStd. ErrorT-StatConstant1.750.434.10Shopping Areas(Q3)0.630.193.40Amusement Parks (Q3)4.710.588.14Specific Structures (Q3)1.050.402.59# of Hotel Rooms0.0001040.0000110.12Adj. R-sq0.88S.E. of Reg.1.74Source: Global Insight, Inc.40
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 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 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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