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A visual analytics framework for spatiotemporal analysis and modelling

: Andrienko, Natalia; Andrienko, Gennady

Preprint urn:nbn:de:0011-n-2342250 (2.4 MByte PDF)
MD5 Fingerprint: 8b5459244b484aca98d4f4d97a1ccabd
The original publication is available at
Erstellt am: 22.3.2013

Data mining and knowledge discovery 27 (2013), Nr.1, S.55-83
ISSN: 1384-5810
ISSN: 1573-756X
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IAIS ()
spatio-temporal data; interactive visual techniques; clustering; time series analysis; visual analytic

To support analysis and modelling of large amounts of spatio-temporal data having the form of spatially referenced time series (TS) of numeric values, we combine interactive visual techniques with computational methods from machine learning and statistics. Clustering methods and interactive techniques are used to group TS by similarity. Statistical methods for TS modelling are then applied to representative TS derived from the groups of similar TS. The framework includes interactive visual interfaces to a library of modelling methods supporting the selection of a suitable method, adjustment of model parameters, and evaluation of the models obtained. The models can be externally stored, communicated, and used for prediction and in further computational analyses. From the visual analytics pe rspective, the framework suggests a way to externalize spatio-temporal patterns emerging in the mind of the analyst as a result of interactive visual analysis: the patterns are represented in the form of computer-processable and reusable models. From the statistical analysis perspective, the framework demonstrates how TS analysis and modelling can be supported by interactive visual interfaces, particularly, in a case of numerous TS that are hard to analyse individually. From the application perspective, the framework suggests a way to analyse large numbers of spatial TS with the use of well-established statistical methods for TS analysis.