Towards Acquisition of Semantics of Places and Events by Multi-perspective Analysis of Geotagged Photo Collections View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012-10-02

AUTHORS

Slava Kisilevich , Daniel Keim , Natalia Andrienko , Gennady Andrienko

ABSTRACT

Due to the pervasiveness of positioning technology combined with the proliferation of socially-oriented web sites, community-contributed spatio-temporal data of people’s historical positions are available today in large amounts. The analysis of these data is valuable to scientists and can provide important information about people’s behavior, their movement, geographical places, and events. In this paper, we develop a conceptual framework and outline a methodology that allows us to analyze events and places using geotagged photo collections shared by people from many countries. These data are often semantically annotated by titles and tags that are useful for learning facts about the geographical places and for detecting events occurring in these places. The knowledge obtained through our analysis carries an additional benefit. For example, it may also be utilized by local authorities, service providers, tourist agencies, in sociological and anthropological studies or for building user centric applications like tour recommender systems. We provide a conceptual foundation for the analysis of spatio-temporal data of places visited by people worldwide using community contributed geotagged photo collections. First, we define several types of spatio-temporal clusters of people’s visits. Second, we discuss methods that can be used for analysis of these clusters. Third, we offer an analysis of tourist activities in Switzerland based on a case study. More... »

PAGES

211-233

Book

TITLE

Geospatial Visualisation

ISBN

978-3-642-12288-0
978-3-642-12289-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-12289-7_10

DOI

http://dx.doi.org/10.1007/978-3-642-12289-7_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049023036


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