Big data, big tourism
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This document discusses how big data is impacting tourism. It notes that data comes from tourists, attractions, hotels, and restaurants through sensors and online activities. This data is owned by both the government and private sector, but the private sector data is more integrated with user experiences. It also discusses challenges with scraping data from major travel sites which frequently change their structures. Potential applications of big data in tourism include analyzing user behaviors, automated storytelling for destinations, and tailoring experiences based on user profiles.
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Big data, big tourism
- 1. Big Data, Big Tourism Tourism and Mechanics https://www.slideshare.net/sirmmo/big-data-big-tourism
- 3. What are «Big Data»? • Excel gets stuck working a dataset? => «medium» data • Stata/R suffer working a dataset? => «big» data
- 4. Where do we get the data? • Tourists • Have sensors • Are sensors • Are actors • Attractions • Are sensors • Are actors • Hotels, restaurants • Are sensors • Have sensors
- 5. Can we access the data? • Tourists • Have sensors • Are sensors • Are actors • Attractions • Are sensors • Are actors • Hotels, restaurants • Are sensors • Have sensors
- 6. Can we access the data? • Tourists • Have sensors • Are sensors • Are actors • Attractions • Are sensors • Are actors • Hotels, restaurants • Are sensors • Have sensors
- 7. Can we access the data? • Tourists • Have sensors • Are sensors • Are actors • Attractions • Are sensors • Are actors • Hotels, restaurants • Are sensors • Have sensors
- 8. Government Can we access the data? • Tourists • Have sensors • Are sensors • Are actors • Attractions • Are sensors • Are actors • Hotels, restaurants • Are sensors • Have sensors Private Sector
- 9. Can we access the data? • Tourists • Have sensors • Are sensors • Are actors • Attractions • Are sensors • Are actors • Hotels, restaurants • Are sensors • Have sensors Private SectorGovernment Open(able/ish) Data Almost always
- 10. Ok so who owns that data? • Government • Bureaucracy-driven data • Incoherent • Inconsistent • Irregular production • Private Sector • Deeply integrated with user experience • Very «behavioral», and as such very «real» • Very business-oriented metrics
- 11. Ok so who owns that data? • Government • Bureaucracy-driven data • Incoherent • Inconsistent • Irregular production • Private Sector • Deeply integrated with user experience • Very «behavioral», and as such very «real» • Very business-oriented metrics
- 12. Ok so who owns that data? • Government • Bureaucracy-driven data • Incoherent • Inconsistent • Irregular production • Private Sector • Deeply integrated with user experience • Very «behavioral», and as such very «real» • Very business-oriented metrics
- 13. Scraping • Time consuming • Power consuming • Illegal (up to a certain point) • Unavoidable (up to a certain point)
- 14. Scraping • It relies on the fact that (most) web is based on HTML • And HTML is text • And JavaScript is text • And CSS is text • Everything can be read before the render…
- 15. Scraping • It relies on the fact that (most) web is based on HTML • And HTML is text • And JavaScript is text • And CSS is text • Everything can be read before the render… • Or after the render
- 16. Tools • Not easy for «complex» sites • Some cases come up • Some tools help • Maybe knowledge of Xml Query Language or CSS required • Some tools are very advanced • Selenium browser driver • «headless» browsers • Chrome • https://chrome.google.com/webstore/detai l/scraper/mbigbapnjcgaffohmbkdlecaccepn gjd?hl=en • https://chrome.google.com/webstore/detai l/web- scraper/jnhgnonknehpejjnehehllkliplmbmh n?hl=en • https://chrome.google.com/webstore/detai l/advanced-web- scraper/gpolcofcjjiooogejfbaamdgmgfehgff • Firefox • https://addons.mozilla.org/en- US/firefox/addon/datascraper/ • Web • https://www.import.io/ • https://scrapinghub.com/portia/
- 17. Cases and issues of scraping • Booking.com • Amazing website • Easy navigation for the user • Issues • They know!!! • The website gets a complete structural overhaul every 6-9 months • They tend to hate scrapers • The webpage is empty at the beginning
- 18. Cases and issues of scraping • Booking.com • Amazing website • Easy navigation for the user • Issues • They know!!! • The website gets a complete structural overhaul every 6-9 months • They tend to hate scrapers • The webpage is empty at the beginning
- 19. Cases and issues of scraping • AirBnB • Nice navigation • Full overhaul every 3 months • Issues • The page really tracks what kind of user is accessing • The visible pages are 13 (only) • They are randomly generated every day for the major areas
- 20. Cases and issues of scraping • Weather • Many sources • Many formats • Issues • Normalization of vocabulary • Bad weather == Rain == Rainy == Cloud Icon == ??? • Normalization of ranges • Normalization of numbers • Normalization of periodicity
- 21. Apps Questionnaire to get user to explicitly give data Information driven application to track user data Gamification and/or information platform to elaborate and give data back
- 22. Explicit data • Relies on the user’s knowing actions • Requires real willing acceptance for sharing information • Stops at politically correctness • Implies (almost always) anonimity • Questionnaire • In-place review • In-place comment • Bureaucracy
- 25. Behavioral data • Almost always true • Difficult to get • Easily contextualizable • Interactive • Interconnected • Application • Platform • Social Media integration • Gamification • Social Media involvement
- 26. Cool, so what can be done? Getting Data • Municipalities are setting up open wireless networks. • Users can be tracked. • Services can be offered (and instrumented) • Museums can track users within their premises • Social Media interactions Using Data • Analysis of context of specific behaviours • Automated storytelling for city visits • Pricing methodologies • Destination brand analysis
- 27. Big and Big-ish Data Tools • The problem is computational power • Lots of work on AI • Classification • Generation • Machine Learning • Correlations • DataWarehouses • Mondrian - http://community.pentaho.com/projects/ mondrian/ • Big Data DBs • Cassandra - http://cassandra.apache.org/ • Hadoop - http://hadoop.apache.org/ • Big Data Search • BigQuery - https://cloud.google.com/bigquery/ • GraphQL - http://graphql.org/ • Big Data AI/ML • TensorFlow - https://www.tensorflow.org/ • ScikitPy - https://www.scipy.org/
- 28. A few open questions • Impact of crowdfunding on tourism-bound projects • Impact of meta-search-engines on pricing • Impact (or lack thereof) of destination information websites on user decisions • How can the user be «vetted» in order to tailor the touristic experience around her? • Would such vetting process impact on customer return decisions?
- 29. One more thing: Watch out!!