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

value below one suggests that the share of attractions for this city is below the NorthAmerican average.H. Discussion of ResultsThe analysis proceeded in three steps to arrive to the set of five econometric models thatyielded robust results 10 . The structure of these models allowed Global Insight to identifyand rank the relative return offered by each type of attraction in terms of the number ofvisitations it can generate. Based on these models, Global Insight can identify a subset ofthe most important attraction types for generating city visits. Furthermore, Global Insightcan recommend an effective attractions development and promotional tourism strategyfor Toronto and Ottawa to enhance future visitations. It is important to emphasize that allfive models were robust and contained unique characteristicsKey FindingsStep 1: For this step, Global Insight used all three measures of attraction count. Leisurevisitations were regressed against each of these measures of attraction count (i.e. numberof attractions, normalized attractions, and normalized share of attractions) for each typeof attraction, attraction category, and sub-category according to the structure of theattractions matrix. Global Insight modelled total count of attractions and quality-ratedattractions (i.e. Q3, Q2, and Q1) separately. Table 12 summarizes our findings. In thistable, areas highlighted with dark colour represent statistically significant results with t-statistics above 2, while areas highlighted with light colour show marginally significantresults with t-statistics between 1 and 2 11 . Please note that the quality count of casinos,opera, and theatre performances was not available. Conversely, the following tablesummarizes the types of attractions, attraction categories, and sub-categories that did notwork for all three types of attraction measures.Note that there is a consistent statistical significance among all three attraction countmeasures for physical setting, urban amenities, built form, and popular entertainment. Inthis step, these categories and their related subcategories seemed to be the most highlycorrelated with leisure visitation, regardless of the attraction count measure used.10 These results were robust in terms of individual t-statistics for different types of tourist attractions andadjusted R 2 .11 t-statistics refer to a statistical “goodness-of-fit” measure that indicated the likelihood that the coefficientestimated for the explanatory variable is, in fact, greater than 0. Thus, very low t-statistics suggest that thereis no meaningful causation implied between the explanatory variable (the attractions variable) and theindependent variable (the number of visitations). t-statistics greater than 2.0 are generally regarded ashighly significant.17