Abstract
There is an increasing trend of people leaving digital traces through social media. This reality opens new horizons for urban studies. With this kind of data, researchers and urban planners can detect many aspects of how people live in cities and can also suggest how to transform cities into more efficient and smarter places to live in. In particular, their digital trails can be used to investigate tastes of individuals, and what attracts them to live in a particular city or to spend their vacation there. In this paper we propose an unconventional way to study how people experience the city, using information from geotagged photographs that people take at different locations. We compare the spatial behavior of residents and tourists in 10 most photographed cities all around the world. The study was conducted on both a global and local level. On the global scale we analyze the 10 most photographed cities and measure how attractive each city is for people visiting it from other cities within the same country or from abroad. For the purpose of our analysis we construct the users’ mobility network and measure the strength of the links between each pair of cities as a level of attraction of people living in one city (i.e., origin) to the other city (i.e., destination). On the local level we study the spatial distribution of user activity and identify the photographed hotspots inside each city. The proposed methodology and the results of our study are a low cost mean to characterize touristic activity within a certain location and can help cities strengthening their touristic potential.
Highlights
The traces of communication and information technologies are currently considered to be an efficient and consolidated way of collecting useful and large data sources for urban studies
It was shown how to identify some of user characteristics from mobile call patterns, for example how to determinate if a user is a tourist or a resident [ ]. When it comes to studying human mobility patterns, exploring detailed call records is not the only possibility - other sources of big data collected from digital maps [ ], electronic toll systems [ ], credit cards payments [, ], Twit-Paldino et al EPJ Data Science (2015) 4:5 ter [ ], circulation of bank notes [ ], vehicle GPS traces [ ] and using geotagged photographs [ – ] can be successfully applied
Girardin et al showed that it was possible to define a measure of city attractiveness by exploring big data from photograph sharing websites [ ]
Summary
Silvia Paldino[1], Iva Bojic[2], Stanislav Sobolevsky2*, Carlo Ratti[2] and Marta C González[3]
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