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Analysis of user generated spatio-temporal data : Learning from collections of geotagged photos

Analysis of user generated spatio-temporal data : Learning from collections of geotagged photos

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KISILEVICH, Slava, 2012. Analysis of user generated spatio-temporal data : Learning from collections of geotagged photos [Dissertation]. Konstanz: University of Konstanz

@phdthesis{Kisilevich2012Analy-20278, title={Analysis of user generated spatio-temporal data : Learning from collections of geotagged photos}, year={2012}, author={Kisilevich, Slava}, address={Konstanz}, school={Universität Konstanz} }

Large collections of geotagged photos publicly accessible through photo-sharing web sites such as Flickr or Panoramio, present an opportunity for the entire Internet community to access the wealth of visual and textual data stored on photos. At the same time, it presents the challenge of how to efficiently and effectively turn this data into information about locations of interest and people's movement preferences.<br />Geotagged photos can be viewed in a dual way: as independent spatio-temporal events and as trajectories of people in the geographical space. These two views imply, however, two different approaches to an analysis that will yield different kinds of valuable knowledge about places as well as people. In this thesis, geotagged photos are therefore primarily regarded in the context of (event-based) movement rather than multimedia content and present several exploratory and analytical techniques corresponding to event-based and trajectory-based movement analysis.<br /><br />Moreover, geotagged photos are contextually rich data that combine geographical information about the places where photos were taken with textual information. This information includes for the most part titles and tags attached to photos by owners of these photos as well as comments that can be written by other users. This unique combination is what makes the geotagged photos special for analysis of people's movement and behavior in contrast to the general purpose spatio-temporal data like GPS-based records. Analysis of spatio-temporal data is challenging since it always requires combining different analytical methods like geocomputation and geographical analytics, algorithms like aggregation and clustering, technologies in which the data is appropriately visualized on the map and scalable with the amount of the data.<br /><br />Taking into consideration the above mentioned aspects, we propose in this thesis a systematic approach to an analysis of people's movement and events using geotagged photos. We make contributions in four main research areas: (1) geovisual analytics - interactive exploration of patterns of people's trajectories and interesting routes that they take during photographing; (2) data mining - development of techniques and algorithms for discovery of attractive areas and finding frequent sequential patterns of people's movement; (3) text mining and computational linguistics - development of approaches for extraction and analysis of comments that people write for photos, and (4) systems engineering - development of a GIS-based tool to facilitate handling of geotagged photos. Moreover, while some of the approaches were targeted towards the imaginary domain expert, some approaches proposed in this thesis were deliberately usercentered in order to demonstrate how the proposed approaches can be used in user-centered scenarios if implemented and delivered by a service provider. Finally, the research demonstrated in this thesis will be useful for practical reasons, such as future research targeting people's preferences for urban locations, landmarks and corresponding travel itineraries. It may also benefit (research) areas like city promotion and advertising, public safety, tourism, and civic minded activities. Kisilevich, Slava 2012-09-04T10:26:30Z Analysis of user generated spatio-temporal data : Learning from collections of geotagged photos Kisilevich, Slava eng deposit-license 2012 2012-09-04T10:26:30Z

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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