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Conference Papers Year : 2016

SoMap: Dynamic Clustering and Ranking of Geotagged Posts

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Abstract

This demo presents SoMap, a web-based platform that provides new scalable methods to aggregate, analyze and valorize large collections of heterogeneous social data in urban contexts. The platform relies on geotagged data extracted from social networks and microblogging applications such as Instagram, Flickr, and Twitter and on Points Of Interest gathered from OpenStreetMap. It could be very insightful and interesting for data scientists and decision-makers. SoMap enables dynamic clustering of filtered social data in order to display it on a map in a combined form. The key components of this platform are the clustering module, which relies on a scalable algorithm described in this paper, and the ranking algorithm that combines the popularity of the posts, their location and their link to the points of interest found in the neighborhood. The system further detects mobility patterns by identifying and aggregating trajectories for all the users. SoMap will be demonstrated through several examples that highlight all of its functionalities and reveal its effectiveness and usefulness.

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Dates and versions

hal-01700153 , version 1 (03-02-2018)

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  • HAL Id : hal-01700153 , version 1

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Marie Al-Ghossein, Talel Abdessalem. SoMap: Dynamic Clustering and Ranking of Geotagged Posts. The 25th international World Wide Web Conference, Apr 2016, Montréal, Canada. ⟨hal-01700153⟩
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