Digital Footprinting: Uncovering Tourists with User-Generated Content

By Fabien Girardin, Francesco Calabrese, F. Fiore, Carlo Ratti, Josep Blat. IEEE pervasive computing, 2008

489 citations recorded by the source corpus.

Novel methods and tools are being developed to explore the significance of the new types of user-related spatiotemporal data. This approach helps uncover the presence and movements of tourists from cell phone network data and the georeferenced photos they generate. A city's visitors have many ways of leaving voluntary or involuntary electronic trails: prior to their visits, tourists generate server log entries when they consult digital maps or travel Web sites; during their visit, they leave traces on wireless networks whenever they use their mobile phones; and after their visit, they might add online reviews and photos. Broadly speaking then, there are two types of footprint: active and passive. Passive tracks are left through interaction with an infrastructure, such as a mobile phone network, that produces entries in locational logs; active prints come from the users themselves when they expose locational data in photos, messages, and sensor measurements. In this article, we consider two types of digital traces from Rome, Italy: georeferenced photos made publicly available on the photo-sharing Web site Flickr and aggregate records of wireless network events generated by mobile phone users making calls and sending text messages on the Telecom Italia Mobile (TIM) system.

Subfields: Computer Vision and Pattern Recognition, Geography, Planning and Development, Transportation

View Paper: https://www.semanticscholar.org/paper/4e14669f459bead06afbeb527836f53babf9465d

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Citations in the corpus

Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2011, methodology): “Different analysis is being done on the acquired data (see, for instance, [15] and [16]) to quantitatively evaluate the usability of the data for urban-planning purposes and tourist management, with the principal aim being to show how modern digital technologies and their applications could give cit”
Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics (2016, background): “It has been shown that based on mobile data, it is possible to identify tourists from residents, identify hotspots within a city, detect preferred activity sequences, see for instance [6, 8, 30].”
Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice (2012, methodology): “For example, geotagged photos posted on Flickr have been used as the primary data source to reconstruct tourists’ movements at various sites (Girardin et al. 2008; Girardin et al. 2009).”
Extracting Diurnal Patterns of Real World Activity from Social Media (2013, background): “…cellphone use to understand spatio-temporal human dynamics (Candia et al. 2008), geo-coded image posting rate to learn about points of interest (Girardin et al. 2008; Pereira et al. 2011) and activity on Foursquare to model city dynamic structure and functional use (Cranshaw et al. 2012; Kling…”
Prediction of urban human mobility using large-scale taxi traces and its applications (2012, background): “From these digital footprints, it is feasible for researchers to extract social and community intelligence [1], ranging from urban environment dynamics [2,3] to social events [4,5].”
Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, methodology): “An example of self-reported data is the one that can be obtained from Flickr 23 that is used for example in (Girardin et al., 2008) to mine tourists patterns in Rome.”
Image sequence geolocation with human travel priors (2009, background): “We anticipate the use of geolocation for obtaining valuable data for the study of human behavior in multiple disciplines [2, 6, 9, 10, 11, 13, 15].”
Automatic construction of travel itineraries using social breadcrumbs (2010, background): “In [12], the authors study digital footprints, explicit (e.”
Exploring place through user-generated content: Using Flickr tags to describe city cores (2010, background): “Girardin et al. [13] explored how tourists and residents of Rome left digital traces in Flickr through georeferenced photos.”
Human mobility: Models and applications (2017, background): “[323] studied the location and mobility patterns of tourists in Rome, and subsequent studies highlighted the specific travel patterns of visitors, when compared to the movements of city residents, notably in Paris [324, 325], Jakarta and Singapore [326].”
Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2017, background): “human footprints in unprecedented scale [17], [18], [19].”