Data generated on location-based social networks provide rich information on the whereabouts of urban dwellers. Specifically, such data reveal who spends time where, when, and on what type of activity (e.g., shopping at a mall, or dining at a restaurant). That information can, in turn, be used to describe city regions in terms of activity that takes place therein. For example, the data might reveal that citizens visit one region mainly for shopping in the morning, while another for dining in the evening. Furthermore, once such a description is available, one can ask more elaborate questions: What are the features that distinguish one region from another -- is it simply the type of venues they host or is it the visitors they attract? What regions are similar across cities? In this paper, we attempt to answer these questions using publicly shared Foursquare data. In contrast with previous work, our method makes use of a probabilistic model with minimal assumptions about the data and thus relieves us from having to make arbitrary decisions in our analysis (e.g., regarding the granularity of discovered regions or the importance of different features). We perform an empirical comparison with previous work and discuss insights obtained through our findings.
Finding cohesive geographical regions within cities has been attempted using a variety of data sources, such as cellphone activity @cite_34 , geotagged tweets @cite_6 , social interactions @cite_21 , types of buildings @cite_5 , or public transport and taxi trajectories @cite_36 @cite_3 .
@cite_36 @cite_21 @cite_6 @cite_3 @cite_5 @cite_34
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Pervasive presence of location-sharing services made it possible for researchers to gain an unprecedented access to the direct records of human activity in space and time. This article analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012, we estimate the volume of international travelers by country of residence. Mobility profiles of different nations were examined based on such characteristics as mobility rate, radius of gyration, diversity of destinations, and inflow–outflow balance. Temporal patterns disclose the universally valid seasons of increased international mobility and the particular character of international travels of different nations. Our analysis of the community structure of the Twitter mobility network reveals spatially cohesive regions that follow the regional division of the world. We validate our result using global tourism statistics and mobility models provided by other authors and argue that Twitter is exceptionally useful for understanding and quantifying global mobility patterns. This paper studies the problem of discovering and comparing geographical topics from GPS-associated documents. GPS-associated documents become popular with the pervasiveness of location-acquisition technologies. For example, in Flickr, the geo-tagged photos are associated with tags and GPS locations. In Twitter, the locations of the tweets can be identified by the GPS locations from smart phones. Many interesting concepts, including cultures, scenes, and product sales, correspond to specialized geographical distributions. In this paper, we are interested in two questions: (1) how to discover different topics of interests that are coherent in geographical regions? (2) how to compare several topics across different geographical locations? To answer these questions, this paper proposes and compares three ways of modeling geographical topics: location-driven model, text-driven model, and a novel joint model called LGTA (Latent Geographical Topic Analysis) that combines location and text. To make a fair comparison, we collect several representative datasets from Flickr website including Landscape, Activity, Manhattan, National park, Festival, Car, and Food. The results show that the first two methods work in some datasets but fail in others. LGTA works well in all these datasets at not only finding regions of interests but also providing effective comparisons of the topics across different locations. The results confirm our hypothesis that the geographical distributions can help modeling topics, while topics provide important cues to group different geographical regions. Associating geo-coordinates with the content of social media posts can enhance many existing applications and services and enable a host of new ones. Unfortunately, a majority of social media posts are not tagged with geo-coordinates. Even when location data is available, it may be inaccurate, very broad or sometimes fictitious. Contemporary location estimation approaches based on analyzing the content of these posts can identify only broad areas such as a city, which limits their usefulness. To address these shortcomings, this paper proposes a methodology to narrowly estimate the geo-coordinates of social media posts with high accuracy. The methodology relies solely on the content of these posts and prior knowledge of the wide geographical region from where the posts originate. An ensemble of language models, which are smoothed over non-overlapping sub-regions of a wider region, lie at the heart of the methodology. Experimental evaluation using a corpus of over half a million tweets from New York City shows that the approach, on an average, estimates locations of tweets to within just 2.15km of their actual positions. Individuals generate vast amounts of geolocated content through the use of mobile social media applications. In this context, Twitter has become an important sensor of the interactions between individuals and their environment. Building on this idea, this paper proposes the use of geolocated tweets as a complementary source of information for urban planning applications, focusing on the characterization of land use. The proposed technique uses unsupervised learning and automatically determines land uses in urban areas by clustering geographical regions with similar tweeting activity patterns. Three case studies are presented and validated for Manhattan (NYC), London (UK) and Madrid (Spain) using Twitter activity and land use information provided by the city planning departments. Results indicate that geolocated tweets can be used as a powerful data source for urban planning applications.