Urban magnetism through the lens of geo-tagged photography

Springer Science and Business Media LLC - Tập 4 - Trang 1-17 - 2015
Silvia Paldino1, Iva Bojic2, Stanislav Sobolevsky2, Carlo Ratti2, Marta C González3
1Department of Physics, University of Calabria, Rende CS, Italy
2SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, USA
3Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, USA

Tóm tắt

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.

Tài liệu tham khảo

Ratti C, Pulselli RM, Williams S, Frenchman D (2006) Mobile landscapes: using location data from cell-phones for urban analysis. Environ Plan B, Plan Des 33(5):727-748 Reades J, Calabrese F, Sevtsuk A, Ratti C (2007) Cellular census: explorations in urban data collection. IEEE Pervasive Comput 6(3):30-38 González MC, Hidalgo CA, Barabási AL (2008) Understanding individual human mobility patterns. Nature 453:779-782 Kung KS, Greco K, Sobolevsky S, Ratti C (2014) Exploring universal patterns in human home-work commuting from mobile phone data. PLoS ONE 9(6):e96180. doi:10.1371/journal.pone.0096180 Hoteit S, Secci S, Sobolevsky S, Ratti C, Pujolle G (2014) Estimating human trajectories and hotspots through mobile phone data. Comput Netw 64:296-307 Reades J, Calabrese F, Ratti C (2009) Eigenplaces: analysing cities using the space-time structure of the mobile phone network. Environ Plan B, Plan Des 36(5):824-836 Toole JL, Ulm M, González MC, Bauer D (2012) Inferring land use from mobile phone activity. In: Proceedings of the ACM SIGKDD international workshop on urban computing. ACM, New York, pp 1-8 Grauwin S, Sobolevsky S, Moritz S, Godor I, Ratti C (2014) Towards a comparative science of cities: using mobile traffic records in New York, London and Hong Kong. In: Computational approaches for urban environments. Geotechnologies and the environment, vol 13, pp 363-387 Pei T, Sobolevsky S, Ratti C, Shaw SL, Li T, Zhou C (2014) A new insight into land use classification based on aggregated mobile phone data. Int J Geogr Inf Sci 28(9):1-20 Ratti C, Sobolevsky S, Calabrese F, Andris C, Reades J, Martino M, Claxton R, Strogatz SH (2010) Redrawing the Map of Great Britain from a Network of Human Interaction. PLoS ONE. doi:10.1371/journal.pone.0014248 Sobolevsky S, Szell M, Campari R, Couronné T, Smoreda Z, Ratti C (2013) Delineating geographical regions with networks of human interactions in an extensive set of countries. PLoS ONE 8(12):e81707. doi:10.1371/journal.pone.0081707 Amini A, Kung K, Kang C, Sobolevsky S, Ratti C (2014) The impact of social segregation on human mobility in developing and industrialized regions. EPJ Data Sci 3:6. doi:10.1140/epjds31 Furletti B, Gabrielli L, Renso C, Rinzivillo S (2012) Identifying users profiles from mobile calls habits. ACM SIGKDD International Workshop on Urban Computing Fisher D (2007) Hotmap: looking at geographic attention. IEEE Trans Vis Comput Graph 13(6):1184-1191 Houée M, Barbier C (2008) Estimating foreign visitors flows from motorways toll management system. In: 9th international forum on tourism statistics Sobolevsky S et al (2014) Mining urban performance: scale-independent classification of cities based on individual economic transactions. ASE BigDataScience 2014, Stanford, CA, preprint. arXiv:1405.4301 Sobolevsky S, Sitko I, Tachet des Combes R, Hawelka B, Murillo Arias J, Ratti C (2014) Money on the move: big data of bank card transactions as the new proxy for human mobility patterns and regional delineation. The case of residents and foreign visitors in Spain. In: 2014 IEEE international congress on big data, Anchorage, AK Hawelka B, Sitko I, Beinat E, Sobolevsky S, Kazakopoulos P, Ratti C (2014) Geo-located Twitter as proxy for global mobility patterns. Cartogr Geogr Inf Sci 41(3):260-271 Brockmann D, Hufnagel L, Geisel T (2006) The scaling laws of human travel. Nature 439:462-465 Kang C, Sobolevsky S, Liu Y, Ratti C (2013) Exploring human movements in Singapore: a comparative analysis based on mobile phone and taxicab usages. In: Proceedings of the 2nd ACM SIGKDD international workshop on urban computing. ACM, New York, p 1 Girardin F, Calabrese F, Dal Fiore F, Ratti C, Blat J (2008) Digital footprinting: uncovering tourists with user-generated content. IEEE Pervasive Comput 7(4):36-43 Crandall DJ, Backstrom L, Huttenlocher D, Kleinberg J (2009) Mapping the world’s photos. In: WWW’09: proceedings of the 18th international conference on world wide web. ACM, New York, pp 761-770 Zheng YT, Zha ZJ, Chua TS (2012) Mining travel patterns from geotagged photos. ACM Trans Intell Syst Technol 3(3):1-18 Spirn A (1998) The language of landscape, pp 3-81 Girardin F, Vaccari A, Gerber A, Biderman A, Ratti C (2009) Quantifying urban attractiveness from the distribution and density of digital footprints. Int J Spat Data Infrastruct Res 4:175-200 Sinkiene J, Kromolcas S (2010) Concept, direction and practice of city attractiveness improvement. Public Policy Adm 31:147-154 Van den Berg L, Van der Meer J, Otgaar AHJ (2007) The attractive city: catalyst of sustainable urban development. In: Ache P, Lehtovuori P (eds) European urban and metropolitan planning. Proceedings of the first openings. Seminar 12th October 2007 YTK-espoo. Centre for urban and regional studies publication C67, pp 48-63 Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, Jones SJ, Marra MA (2009) Circos: an information aesthetic for comparative genomics. Genome Res 19(9):1639-1645. doi:10.1101/gr.092759.109 Krings G, Calabrese F, Ratti C, Blondel VD (2009) Urban gravity: a model for inter-city telecommunication flows. J Stat Mech Theory Exp 2009(07):L07003