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Big data, big tourism

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Marco Montanari
Marco Montanari

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 Tourism and Mechanics /sirmmo/big-data-big-tourism
Big data, big tourism
What are 束Big Data損? Excel gets stuck working a dataset? => 束medium損 data Stata/R suffer working a dataset? => 束big損 data
Where do we get the data? Tourists Have sensors Are sensors Are actors Attractions Are sensors Are actors Hotels, restaurants Are sensors Have sensors
Can we access the data? Tourists Have sensors Are sensors Are actors Attractions Are sensors Are actors Hotels, restaurants Are sensors Have sensors
Can we access the data? Tourists Have sensors Are sensors Are actors Attractions Are sensors Are actors Hotels, restaurants Are sensors Have sensors
Can we access the data? Tourists Have sensors Are sensors Are actors Attractions Are sensors Are actors Hotels, restaurants Are sensors Have sensors
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
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
Ok so who owns that data? Government Bureaucracy-driven data Incoherent Inconsistent Irregular production Private Sector Deeply integrated with user experience Very 束behavioral損, and as such very 束real損 Very business-oriented metrics
Ok so who owns that data? Government Bureaucracy-driven data Incoherent Inconsistent Irregular production Private Sector Deeply integrated with user experience Very 束behavioral損, and as such very 束real損 Very business-oriented metrics
Ok so who owns that data? Government Bureaucracy-driven data Incoherent Inconsistent Irregular production Private Sector Deeply integrated with user experience Very 束behavioral損, and as such very 束real損 Very business-oriented metrics
Scraping Time consuming Power consuming Illegal (up to a certain point) Unavoidable (up to a certain point)
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
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
Tools Not easy for 束complex損 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 束headless損 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/
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
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
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
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
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
Explicit data Relies on the users 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
Big data, big tourism
Big data, big tourism
Behavioral data Almost always true Difficult to get Easily contextualizable Interactive Interconnected Application Platform Social Media integration Gamification Social Media involvement
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
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/
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 束vetted損 in order to tailor the touristic experience around her? Would such vetting process impact on customer return decisions?
One more thing: Watch out!!
Thanks! Questions? @ingmmo marco.montanari@gmail.com http://ingmmo.com, https://medium.com/@ingmmo sirmmo http://it.linkedin.com/in/montanarim/ https://www.facebook.com/marco.montanari marco.montanari /sirmmo/big-data-big-tourism

More Related Content

Big data, big tourism

  • 1. Big Data, Big Tourism Tourism and Mechanics /sirmmo/big-data-big-tourism
  • 3. What are 束Big Data損? Excel gets stuck working a dataset? => 束medium損 data Stata/R suffer working a dataset? => 束big損 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 束behavioral損, and as such very 束real損 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 束behavioral損, and as such very 束real損 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 束behavioral損, and as such very 束real損 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 束complex損 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 束headless損 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 users 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 束vetted損 in order to tailor the touristic experience around her? Would such vetting process impact on customer return decisions?
  • 29. One more thing: Watch out!!