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Big data Analytics for Tourism Destination management

Big data Analytics for Tourism Destination management. G. Michael McGrath Professor of Information Systems Victoria University Melbourne. Motivation.

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Big data Analytics for Tourism Destination management

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  1. Big data Analytics for Tourism Destination management G. Michael McGrath Professor of Information Systems Victoria University Melbourne

  2. Motivation • Travel behavior refer to the actual travelactivity of people during their trips such as spatial and temporal movement patterns of tourists. • For examples: • Where do tourists like to visit? • When do tourists visit? • What do tourists like or dislike at each of the visited locations? • How do tourists travel between places? • What routesdo they usually take? • What activities and events do tourists like to participate in?

  3. Motivation • Such knowledge is valuable for: • Policy Marker, Government Departments, Business Managers: • Destination management • Product development • Attraction Development and Marketing  • Tourism Impact management • Transportation Planners: • Traffic management • Transportation Development.

  4. Motivation • Popular methods for capturing travel behavior: Surveyandopinion polls • Disadvantages: • Time consuming • Limited in terms of the number of responses • Limited in scale of the information captured. Unable to provide comprehensive understanding about the locations, time, interests, movement, etc.

  5. Proposed Technologies • Many photo-capturing devices now have built-in global positioning systems(GPS) technology • Geotagged photos, with embedded timeand geographicalinformation, are shared on social networking websites such as (but not limited to):

  6. Proposed Technologies • The geotagged photos have: • GPS tag (latitude, longitude) • Taken Time Stamp (Date, Month, Year, Hours, Minutes, Second) • Textual Metadata (tags, description, title, comments), reflecting whatpeople are interested in. • The Actual Photos, provide insight into tourist’s own experience about the entities of interest. • People’s Profile (Where they come from?) Allow for comprehensive understanding about tourist behavior without the need of actual engagement.

  7. DEMONSTRATION – Using Flickr DataLarge Scale Study of Hong Kong • Photo GPS information viewed on Google Earth. (approximately 29,443 photos from 2,100 user)

  8. DEMONSTRATION • Area Of Interest Identifications using Clustering

  9. DEMONSTRATION • Movement Trajectory generated from geotagged photos.

  10. Data Driven Approach • Data Collection: • Big data sets from Social Network such as: • Data Analysis: • Develop processing techniques for textual data (review comments), visual data (travel photos), temporal data (travel date and time), location data (GPS coordinates), ect…. • Discover Patterns using quantitative data analysis (statistics, data mining)

  11. DEMONSTRATION • Tourist traffic flow Analysis

  12. DEMONSTRATION Actual Route Taken Analysis: From Center MongKokto Time Square Tower

  13. DEMONSTRATION • Time Analysis of Tourist Activity

  14. Demonstration: Location Preference Using Geotagged photos From Flickr Photo Taken by Tourist in Melbourne CBD in July 2015

  15. Demonstration: Location Preference Using Geotagged photos From Flickr • Preferred Location to Take sunset photos in Melbourne

  16. Demonstration: Location Preference Using Geotagged photos From Flickr • Preferred Location to take Art photos in Melbourne CBD

  17. Demonstration: Outbound Location Preference • Top Visited Cities for Australian Travelers.

  18. QUESTIONS 18

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