Open AccessProceedings Article
Exploring Millions of Footprints in Location Sharing Services
TLDR
It is found that LSS users follow the “Levy Flight” mobility pattern and adopt periodic behaviors; while geographic and economic constraints affect mobility patterns, so does individual social status; and Content and sentiment-based analysis of posts associated with checkins can provide a rich source of context for better understanding how users engage with these services.Abstract:
Location sharing services (LSS) like Foursquare, Gowalla, and Facebook Places support hundreds of millions of user-driven footprints (i.e., "checkins"). Those global-scale footprints provide a unique opportunity to study the social and temporal characteristics of how people use these services and to model patterns of human mobility, which are significant factors for the design of future mobile+location-based services, traffic forecasting, urban planning, as well as epidemiological models of disease spread. In this paper, we investigate 22 million checkins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. We find that: (i) LSS users follow the “Levy Flight” mobility pattern and adopt periodic behaviors; (ii) While geographic and economic constraints affect mobility patterns, so does individual social status; and (iii) Content and sentiment-based analysis of posts associated with checkins can provide a rich source of context for better understanding how users engage with these services.read more
Citations
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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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The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City
TL;DR: This work introduces a clustering model and research methodology for studying the structure and composition of a city on a large scale based on the social media its residents generate, and applies this new methodology to data from approximately 18 million check-ins collected from users of a location-based online social network.
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Social Sensing: A New Approach to Understanding Our Socioeconomic Environments
TL;DR: In this article, the authors use the term social sensing for individual-level big geospatial data and the associat- tation of the data to understand the socioeconomic environments.
Journal ArticleDOI
Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs
TL;DR: A STAP model is proposed that first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference, and a context-aware fusion framework is put forward to combine the temporal and spatial activity preference models for preferences inference.
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Where you like to go next: successive point-of-interest recommendation
TL;DR: This paper proposes a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions in the check-in sequence, and utilizes the information of localized regions to boost recommendation.
References
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Understanding individual human mobility patterns
Marta C. González,A R Cesar Hidalgo,A R Cesar Hidalgo,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási +5 more
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