S
Stanislav Sobolevsky
Researcher at New York University
Publications - 112
Citations - 5487
Stanislav Sobolevsky is an academic researcher from New York University. The author has contributed to research in topics: Computer science & Population. The author has an hindex of 28, co-authored 98 publications receiving 4578 citations. Previous affiliations of Stanislav Sobolevsky include Saint Petersburg State University of Information Technologies, Mechanics and Optics & Massachusetts Institute of Technology.
Papers
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Journal ArticleDOI
Quantifying the benefits of vehicle pooling with shareability networks
Paolo Santi,Giovanni Resta,Michael Szell,Stanislav Sobolevsky,Steven H. Strogatz,Carlo Ratti +5 more
TL;DR: The notion of shareability network is introduced, which allows to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets, and demonstrates the feasibility of a shareable taxi service in New York City.
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Geo-located Twitter as proxy for global mobility patterns
Bartosz Hawelka,Izabela Sitko,Euro Beinat,Stanislav Sobolevsky,Pavlos Kazakopoulos,Carlo Ratti +5 more
TL;DR: This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility and reveals spatially cohesive regions that follow the regional division of the world.
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Redrawing the map of Great Britain from a network of human interactions
Carlo Ratti,Stanislav Sobolevsky,Francesco Calabrese,Clio Andris,Jonathan Reades,Jonathan Reades,Mauro Martino,Rob Claxton,Steven H. Strogatz +8 more
TL;DR: This paper proposes a novel, fine-grained approach to regional delineation, based on analyzing networks of billions of individual human transactions, and shows how to partition the area into smaller, non-overlapping regions while minimizing the disruption to each person's links.
Journal ArticleDOI
Geo-located Twitter as the proxy for global mobility patterns
Bartosz Hawelka,Izabela Sitko,Euro Beinat,Stanislav Sobolevsky,Pavlos Kazakopoulos,Carlo Ratti +5 more
TL;DR: In this article, the authors analyze geo-located Twitter messages in order to uncover global patterns of human mobility, revealing spatially cohesive regions that follow the regional division of the world.
Journal ArticleDOI
A new insight into land use classification based on aggregated mobile phone data
TL;DR: An analysis of the land-use classification results shows that the detection rate decreases as the heterogeneity of land use increases, and increases as the density of cell phone towers increases.