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A complex network of tourism has emerged in the globalized world, but there is little research on the dynamics of global tourism networks and the underlying forces that affect those dynamics. Using international tourism data for 124 countries between 2000 and 2013, we integrated cluster analyses and social network models to identify the structures of global tourism networks and uncover factors affecting changes in international tourist flows. Results indicate that global tourism networks have become highly consolidated over time and that reduced transaction costs (e.g., language, distance, and visa policies) are more important in attracting international tourists than natural and cultural attractions. Furthermore, cost of living differences between countries decreased in importance over time. Finally, international tourist flows are resilient to political instability and terrorism risks. Our approach and findings highlight the key strategic factors for decision-making to implement proactive tourism policies.
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US State Tourism Websites
In the United States, every state has a tourism website. These sites highlight the main attractions of the state, travel tips, and blog posts among other relevant information. The funding for these websites often comes from occupancy taxes, a form of taxes that comes from tourists who stay in hotels and visit attractions. Therefore, current and past tourists fund the efforts to draw future tourists into the state. Since state tourism is funded by the success of past tourism efforts, it is important for researchers to spend their time and resources on finding out what efforts were successful and which weren’t. With this comes the importance of seeing trends in past tourism endeavors. By examining past tourism websites, patterns can be drawn about information that changed, from season to season and year to year. These patterns can be used to see what researchers deemed as successful tourism efforts, and help guide future state tourism decisions. Our client, Dr. Florian Zach of the Howard Feiertag Department of Hospitality and Tourism Management, wants to use this historical analysis on state tourism information to help with his research on trends in state tourism website content. Iterations of the California state tourism website, among other sites, are stored as snapshots on the Internet Archive and can be accessed to see changes in websites over time. Our team was given Parquet files of these snapshots dating back to 2008. The goal of the project was to assist Dr. Zach by using the California state tourism website, visitcalifornia.com, and these snapshots as an avenue to explore data extraction and visualization techniques on tourism patterns to later be expanded to other states’ tourism websites. Python’s Pandas library was utilized to examine and extract relevant pieces of data from the given Parquet files. Once the data was extracted, we used Python’s Natural Language Processing Toolkit to remove non-English words, punctuation, and a set of unimportant “stop words”. With this refined data, we were able to make visualizations regarding the frequency of words in the headers and body of the website snapshots. The data was examined in its entirety as well as in groups of seasons and years. Microsoft Excel functions were utilized to examine and visualize the data in these formats. These data extraction and visualization techniques that we became familiar with will be passed down to a future team. The research on state tourism site information can be expanded to different metadata sets and to other states.
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- PAR ID:
- 10210434
- Date Published:
- Journal Name:
- Multimedia, Hypertext, and Information Access
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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