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A Framework for Using Self-Organizing Maps to Analyze Spatio-Temporal Patterns, Exemplified by Analysis of Mobile Phone Usage

Andrienko, Gennady ; Andrienko, Natalia ; Bak, Peter ; Bremm, Sebastian ; Keim, Daniel A. ; Landesberger von Antburg, Tatiana ; Pölitz, Christian ; Schreck, Tobias (2010)
A Framework for Using Self-Organizing Maps to Analyze Spatio-Temporal Patterns, Exemplified by Analysis of Mobile Phone Usage.
In: Journal of Location Based Services, 4 (3-4)
doi: 10.1080/17489725.2010.532816
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

We suggest a visual analytics framework for the exploration and analysis of spatially and temporally referenced values of numeric attributes. The framework supports two complementary perspectives on spatio-temporal data: as a temporal sequence of spatial distributions of attribute values (called spatial situations) and as a set of spatially referenced time series of attribute values representing local temporal variations. To handle a large amount of data, we use the self-organising map (SOM) method, which groups objects and arranges them according to similarity of relevant data features. We apply the SOM approach to spatial situations and to local temporal variations and obtain two types of SOM outcomes, called space-in-time SOM and time-in-space SOM, respectively. The examination and interpretation of both types of SOM outcomes are supported by appropriate visualisation and interaction techniques. This article describes the use of the framework by an example scenario of data analysis. We also discuss how the framework can be extended from supporting explorative analysis to building predictive models of the spatio-temporal variation of attribute values. We apply our approach to phone call data showing its usefulness in real-world analytic scenarios.

Typ des Eintrags: Artikel
Erschienen: 2010
Autor(en): Andrienko, Gennady ; Andrienko, Natalia ; Bak, Peter ; Bremm, Sebastian ; Keim, Daniel A. ; Landesberger von Antburg, Tatiana ; Pölitz, Christian ; Schreck, Tobias
Art des Eintrags: Bibliographie
Titel: A Framework for Using Self-Organizing Maps to Analyze Spatio-Temporal Patterns, Exemplified by Analysis of Mobile Phone Usage
Sprache: Englisch
Publikationsjahr: 2010
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Journal of Location Based Services
Jahrgang/Volume einer Zeitschrift: 4
(Heft-)Nummer: 3-4
DOI: 10.1080/17489725.2010.532816
Kurzbeschreibung (Abstract):

We suggest a visual analytics framework for the exploration and analysis of spatially and temporally referenced values of numeric attributes. The framework supports two complementary perspectives on spatio-temporal data: as a temporal sequence of spatial distributions of attribute values (called spatial situations) and as a set of spatially referenced time series of attribute values representing local temporal variations. To handle a large amount of data, we use the self-organising map (SOM) method, which groups objects and arranges them according to similarity of relevant data features. We apply the SOM approach to spatial situations and to local temporal variations and obtain two types of SOM outcomes, called space-in-time SOM and time-in-space SOM, respectively. The examination and interpretation of both types of SOM outcomes are supported by appropriate visualisation and interaction techniques. This article describes the use of the framework by an example scenario of data analysis. We also discuss how the framework can be extended from supporting explorative analysis to building predictive models of the spatio-temporal variation of attribute values. We apply our approach to phone call data showing its usefulness in real-world analytic scenarios.

Freie Schlagworte: Forschungsgruppe Visual Search and Analysis (VISA), Spatio-temporal data, Geodata visualization, Self-organizing maps (SOM), Cluster analysis, Visual analysis, Geovisualization
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
Hinterlegungsdatum: 12 Nov 2018 11:16
Letzte Änderung: 22 Jul 2021 18:31
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