Data Analytics for Urban Dynamics Understanding, Monitoring and Simulation
Tourism is one of the most important and progressive industries in many countries of the world, affecting several societal aspects such as hotel industry, public transportation, and restaurant business. It hence goes without saying that understanding touristic patterns, i.e., how tourists move on the territory and explore the available points of interest, is of fundamental importance to a country's economy since it could provide decision makers with quantitative tools to evaluate the impact of tourism on society and improve touristic services to visitors. Unveiling and quantifying touristic patterns, however, requires the availability of data that describe the times, the movements and possibly the preferences of every tourist's journey.
Photography and tourism are inseparable; Photographs play the role of tourists' footprints during their visit of a touristic city. Nowadays, geotagged photos from social media platforms are useful data for tourist mobility analysis. We analyze geotagged photos of European most touristic cities downloaded from Flickr to investigate to what extent movements by tourists are predictable.
Farzad Vaziri received his Bachelor Degree in Information Technology Engineering from UCNA of Tabriz, Iran and his Master Degree in Computer Science from the University of Ca'Foscari of Venice, Italy. Currently, he is a Ph.D. student in Computer Science at the University of Pisa, Italy. He works voluntarily with CNR research center of Pisa as a researcher and he was a member of KDD Lab group in a European project called PETRA. His research interests include machine learning and data mining in big data analytics. He is currently working on his Ph.D. thesis, which is on tourism management and human mobility analysis and simulation.
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