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

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Hotel Room Count and Hotel Property Count: These are supply-side variables, whichmeasure the number of hotel rooms and hotel properties available in each city. Thenumber of rooms or properties is generally positively correlated with visitations.Although the data were available from 1998 to 2002, Global Insight used a five-yearaverage.Public Transportation Score: Many of the articles examined by Global Insight as partof the literature review noted the importance of an efficient transportation system ininfluencing the urban tourism experience. Using data published in the Places RatedAlmanac for the year 2000, Global Insight was able to incorporate the influence thisvariable has on urban visitations. Places Rated Almanac provides a public transportationscore for 354 North American metro areas. The score is based on three weighted factors:commute time (30%), connectivity (60%), and centrality (10%).Commute Time(30%)Table 7: Public Transportation ScoreLeaders:Connectivity(60%)Leaders:Centrality (10%)Leaders:Round-trip commuting time + local publictransit mileage.Small and mid-sized Canadian metro areas(Edmonton, Halifax).Combines national highways, passenger raildepartures, and nonstop airline destinations.New York, Chicago, Toronto, and LosAngeles.Metropolitan proximity to other metro areas.Memphis, Cincinnati, and Indianapolis.Source: Places Rated Almanac, 2000.Filter Variable for International Airports: This variable was obtained from the PlacesRated Almanac publication. If the city had an international airport where non-stopinternational flights were available, the value of “1” was assigned. If the opposite wastrue, the value of “0” was given. Our hypothesis is that cities with international airportsattract more tourists than cities without these types of airports.Filter Variable for a Large Hub Airports: This variable was obtained from the PlacesRated Almanac publication. If the city had a large hub airport, the value of “1” wasassigned. If the opposite was true, the value of “0” was given. Places Rated Almanacdefines large hub airports as those attracting at least 1% of total tourist flows for thecountry. Our hypothesis is that cities with large hub airports should be positivelycorrelated with city visitations.Total Count of Missing Attractions: This variable was constructed using the data fromthe attractions database. An attraction was considered missing if it was not mentioned inany of the travel publications. The value “1” was assigned to missing attractions, and thevalue “0” was assigned to attractions that were present in the database. Our hypothesis isthat a larger number of missing attractions suggests a less diverse attraction portfolio in aparticular city, reducing the number of tourist visitations to a city.13
Population Density: A higher population density should contribute to a highertourist count. The average population density for the United States is 29.4 residents persquare kilometre, while the average density for Canadian cities is only 3.1. Thepopulation density data were obtained from Statistics Canada.Proximity to Population Centres: This variable was constructed to measure theproximity of each city to population centres. For the eastern block of cities, the distancein miles was measured relative to New York City, and for the western block of citiesrelative to Santa Barbara. As the distance from major metro areas increases (in our caserelative to New York City and Santa Barbara), the number of tourist visitations shoulddecline.City-by-city Marketing Budgets: City-by-city budgets were available from theInternational Association of Conventions and Visitor Bureaus (IACVB) survey 8 . Not allof the 50 cities responded to this survey, and this is why the sample of only 33 cities wasconsidered. The data were available for 2003. Our hypothesis is that a larger marketingbudget should increase the number of tourist visits to a city.Table 8: List of 33 North American CitiesU.S. CitiesCanadian CitiesPhoenix, AZ Chicago, IL New York City, NY Austin, TX Montreal, QCSan Diego, CA Indianapolis, IN Columbus, OH Salt Lake City, UT Vancouver, BCSan Francisco, CA Detroit, MI Cleveland, OH Seattle, WA Calgary, ABLos Angeles, CA Minneapolis, MN Oklahoma City, OK Milwaukee, WI Quebec, QCDenver, CO Kansas City, MN Philadelphia, PA Victoria, BCOrlando, FL St. Louis, MO Pittsburgh, PATampa, FL Charlotte, NC Nashville, TNAtlanta, GA Las Vegas, NV San Antonio, TXF. Travel PublicationsTravel publications by Michelin, Frommer's, and Fodor's were used to populate theattractions matrix with relevant data for each city. These publications provided GlobalInsight with the wealth of information about various types of attractions and their qualityratings across the 50 North American cities. Global Insight selected these publicationsbased on their extensive and consistent coverage of major metropolitan areas acrossNorth America and their high on-line sales ranking. Global Insight believes that these areconsistently the most influential publications for the 50 cities under study.MichelinThe Michelin travel publication was our main source of attractions and quality ratings.Michelin provided the overall rating for each city and consistently provided three-star,two-, and one-star quality ratings of attractions. Global Insight assigned a 0-rating to theattractions that were not rated.8 The results of the survey were provided by the Orlando / Orange County Convention & Visitors Bureau.14
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 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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