title: Uncovering Urban Temporal Patterns from Geo-Tagged Photography.
abstract: We live in a world where digital trails of different forms of human activities compose big urban data, allowing us to detect many aspects of how people experience the city in which they live or come to visit. In this study we propose to enhance urban planning by taking into a consideration individual preferences using information from an unconventional big data source: dataset of geo-tagged photographs that people take in cities which we then use as a measure of urban attractiveness. We discover and compare a temporal behavior of residents and visitors in ten most photographed cities in the world. Looking at the periodicity in urban attractiveness, the results show that the strongest periodic patterns for visitors are usually weekly or monthly. Moreover, by dividing cities into two groups based on which continent they belong to (i.e., North America or Europe), it can be concluded that unlike European cities, behavior of visitors in the US cities in general is similar to the behavior of their residents. Finally, we apply two indices, called "dilatation attractiveness index" and "dilatation index", to our dataset which tell us the spatial and temporal attractiveness pulsations in the city. The proposed methodology is not only important for urban planning, but also does support various business and public stakeholder decision processes, concentrated for example around the question how to attract more visitors to the city or estimate the impact of special events organized there.
authors.affiliation: Department of Physics, University of Calabria, Rende CS, Italy.|SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
authors.forename: Silvia
authors.initials: S
authors.lastname: Paldino
authors.affiliation: SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
authors.forename: Dániel
authors.initials: D
authors.lastname: Kondor
authors.affiliation: SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States of America.|SENSEable City Laboratory, SMART Centre, Singapore, Singapore.
authors.forename: Iva
authors.identifier: http://orcid.org/0000-0002-4664-3349
authors.initials: I
authors.lastname: Bojic
authors.affiliation: SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States of America.|Center for Urban Science + Progress, New York University, Brooklyn, NY, United States of America.
authors.forename: Stanislav
authors.initials: S
authors.lastname: Sobolevsky
authors.affiliation: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
authors.forename: Marta C
authors.initials: MC
authors.lastname: González
authors.affiliation: SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
authors.forename: Carlo
authors.initials: C
authors.lastname: Ratti
mesh_terms: D002947:Cities / Q000706:statistics & numerical data*; D002958:City Planning; D003132:Commerce / Q000191:economics / Q000706:statistics & numerical data; D066264:Datasets as Topic; D005060:Europe; D006801:Humans; D009656:North America; D010781:Photography / Q000706:statistics & numerical data*; D062211:Spatio-Temporal Analysis; D014195:Travel / Q000191:economics / Q000523:psychology / Q000706:statistics & numerical data*; D014505:Urban Population / Q000706:statistics & numerical data*