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

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The model overpredicts the number of visits to cities that are primarily smaller in termsof population size. As well, in seven out of the nine cases, the cities were Canadian. Inconsidering these results, Global Insight developed an additional hypothesis to explainthis behaviour. Most of these cities are relatively isolated from the major populationbases, and this could be a significant barrier to attracting more tourists. Model 5 does notcapture this factor.The addition of a population density variable to Model 5 results in Model 5B. This newmodel has a higher adjusted R 2 and greatly (although not entirely) improves the residualpattern—only three cities have non-random residuals. (Please see Table 18 and Figure 2)The population density variable measures the number of people per square kilometre inthe United States and Canada. For the U.S. cities this value was 29.4 residents per squarekilometre, and for the Canadian cities this number was only 3.1. In Model 5B, only threeout of ten North American cities suffer from extreme overprediction, and only one ofthese is Canadian.Table 18: Comparison of Models 5 and 5BType of AttractionType of SecondAttraction andAttractionNon-AttractionVariablesAdj.Amusement Parks (Q3)Specific Structures (Q3)Shopping Areas (Q3) &Room Count0.88Amusement Parks (Q3)Specific Structures (Q3)Shopping Areas (Q3)Room Count & Pop.Density0.90Source: Global Insight, Inc.R 2Figure 2: Residuals as a Percentage of Total Visits in Model 5B75%50%25%TorontoOttawa% of Total Visits0%0 10 20 30 40 50-25%-50%-75%-100%VancouverDetroitFort Lauderale-125%-150%City Ranking (in terms of Number of Visits)1-highest and 50-lowest in terms of # of visitsSource: Global Insight, Inc.29
Table 19: Residuals as a Percentage of Total Visits in Model 5BCity # of Visits (In Millions) Residuals, % of # of VisitsVancouver 4.29 -83.42%Detroit 3.26 -83.49%Fort Lauderale 2.51 -105.17%Source: Global Insight, Inc.K. Impact of Attraction Type on VisitationsOne of the key objectives of this study was to use the attractions database to build crosssectionaleconometric models to explain the impact of attractions on visitations. Theestimated coefficients from these models provided an assessment of the relativeimportance of various attractions in explaining the number of tourist visitations.However, these coefficients were estimated based on a sample of 50 cities and providedan average estimate across all cities included in the sample. Therefore, additional analysiswas done to better understand the implied impacts on Toronto and Ottawa.For this analysis, Global Insight has utilized the concept of elasticity. For this study,elasticity is defined as the percentage change in visitors divided by the percentage changein attractions.Elasticity city = % change in visitors city (5)% change in # of attractions cityIf we assume that the new attractions would be typically added only one at a time, thedefinition of elasticity reduces to the following equation.Elasticity city = β*(A 0 / V 0 ) (6)Where:A 0 - the current number of like attractions in a particular city; 18V 0 - the current number of visits in a particular city;β - the estimated coefficient from one of the model equations.A higher existing ratio of visitors per attraction will result in a lower elasticity (in theequation (6) a lower value of attractions per visitor). This means that if a city already hasa good return on its current base of attractions, it will have a lower response from addingan additional attraction of the same type relative to other cities.18 In the case where the current number of like attractions is zero (i.e., no such attraction exists in the city ofinterest), Global Insight uses the average of the current number (zero) and the number after the addition(one) for A 0 . In this case, A 0 = 0.5.30
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 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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