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

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IV.coefficients from these models provided an assessment <strong>of</strong> the relative importance<strong>of</strong> various attractions <strong>in</strong> expla<strong>in</strong><strong>in</strong>g the number <strong>of</strong> tourist visitations.MethodologyGlobal Insight's approach to this project was to <strong>in</strong>itially conduct a literature review tobecome familiar with the most recent research regard<strong>in</strong>g the appropriate attractiveness<strong>in</strong>dicators to be utilized, the schemes to quantify them for modell<strong>in</strong>g purposes, and thetype <strong>of</strong> data that were used.Subsequently, Global Insight selected a sample <strong>of</strong> 50 major metropolitan areas <strong>in</strong> <strong>North</strong>America with the population size <strong>of</strong> 500,000 or more to be covered <strong>in</strong> the attractionsdatabase. Ten <strong>of</strong> these cities were <strong>in</strong> Canada, while 40 were <strong>in</strong> the United States. Thesecities were those that <strong>of</strong>fered a broad range <strong>of</strong> attractions by themselves. Global Insightdid not <strong>in</strong>clude cities that developed a large visitation count by virtue <strong>of</strong> one ma<strong>in</strong> type <strong>of</strong>attraction or that relied on attractions that were close to, but not a part <strong>of</strong>, themetropolitan area itself.Then, Global Insight developed an attractions matrix to classify the attractions <strong>in</strong>to fourcategories that are recognized to draw tourists and consistent across cities. Us<strong>in</strong>g thisattraction matrix, the appropriate attractions data were collected for each city. The totalleisure tourist visitations and additional non-attractions data were also assembled.Travel reviews published by Michel<strong>in</strong>, Frommer's, and Fodor's were selected <strong>in</strong> order topopulate the attractions matrix with relevant data for each city. These publicationsprovided Global Insight with the wealth <strong>of</strong> <strong>in</strong>formation about various types <strong>of</strong> attractionsand their quality rat<strong>in</strong>gs across the 50 <strong>North</strong> American cities.Because city attraction portfolios change slowly over time, Global Insight selected across-sectional approach to econometric modell<strong>in</strong>g. This approach allowed Global Insightto pool data across the 50 cities and estimate one equation for each structural relationship.The methodology section <strong>of</strong> the report will review tasks one through seven. Tasks eightand n<strong>in</strong>e will be discussed <strong>in</strong> the third section <strong>of</strong> the report “Discussion <strong>of</strong> Results.”Table 1: Required TasksTask # Description1 Literature Review2 Select <strong>North</strong> American Cities3 Implement an Attraction Classification Scheme4 Choose an Econometric Approach5 For Each City, Collect Travel Visitation Data,Attraction Data, and Non-attraction Data6 Select Travel Publications7 Select Measures <strong>of</strong> Attraction Count8 Construct Economic Model or Models9 Identify High-Return AttractionsSource: Global Insight, Inc.5

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