2013, Kisilevich, Slava, Keim, Daniel A., Andrienko, Natalia, Andrienko, Gennady L.

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.

2009, Kisilevich, Slava, Mansmann, Florian, Nanni, Mirco, Rinzivillo, Salvatore

Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering - trajectory clustering, provide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.