Leveraging explicitly disclosed location information to understand tourist dynamics: a case study

  • Authors:
  • Fabien Girardin;Filippo Dal Fiore;Carlo Ratti;Josep Blat

  • Affiliations:
  • Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain;Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, USA;Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, USA;Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain

  • Venue:
  • Journal of Location Based Services - 4th International Conference on LBS and TeleCartography Hong Kong
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

In recent years, the large deployment of mobile devices has led to a massive increase in the volume of records of where people have been and when they were there. The analysis of these spatio-temporal data can supply high-level human behaviour information valuable to urban planners, local authorities, and designer of location-based services. In this article, we describe our approach to collect and analyse the history of physical presence of tourists from the digital footprints they publicly disclose on the web. Our work takes place in the Province of Florence in Italy, where the insights on the visitors' flows and on the nationalities of the tourists who do not sleep in town has been limited to information from survey-based hotel and museums frequentation. In fact, most local authorities in the world must face this dearth of data on tourist dynamics. In this case study, we used a corpus of geographically referenced photos taken in the province by 4280 photographers over a period of two years. Based on the disclosure of the location of the photos, we design geovisualisations to reveal the tourist concentration and spatio-temporal flows. Our initial results provide insights on the density of tourists, the points of interests they visit as well as the most common trajectories they follow.