An Integrated Exploratory Geovisualization Environment Based on Self‐Organizing Map

Koua, Etien L.; Kraak, M.J.

doi:10.1002/9780470021699.ch3

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…A wealth of applications are described in a recent book on the topic [AS08]. As the SOM network itself represents an abstract map for data, color‐coding can be used to link the location of data elements in SOM space with their respective geospatial coordinates [KK08, ST08]. VISSTAMP is a system linking views based on geospatial maps, SOM maps, parallel coordinate and table plots [GCML06].…”

Section: Related Workmentioning

confidence: 99%

Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns

Andrienko

Bremm

et al. 2010

Computer Graphics Forum

120

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Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the "Self-Organizing Map" (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post-processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two-dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41-years time series of 7 crime rate attributes in the states of the USA.

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Section: Related Workmentioning

confidence: 99%

Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns

Andrienko

Bremm

et al. 2010

Computer Graphics Forum

120

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show abstract

“…There are also numerous applications of the SOM to geospatial data; many of them are described in . Colour-coding is used to link the locations of data elements in the SOM space with the respective positions on a cartographical map (Koua andKraak 2008, Spielman and and elements of other displays such as lines in a parallel coordinates plot and cells in a reorderable matrix (Guo et al 2006). Besides using simple linearly scaled two-dimensional colour maps, approaches exist for advanced colour mappings adjusting for non-uniform distributions of the SOM distances (Kaski et al 2000) and considering perceptual issues (Guo et al 2005).…”

Section: Related Workmentioning

confidence: 99%

A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage

Andrienko

Bak

Bremm

et al. 2010

Journal of Location Based Services

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On the Use of Three-Dimensional Self-Organizing Maps for Visualizing Clusters in Georeferenced Data

Gorricha

Lobo

2011

Lecture Notes in Geoinformation and Cartography

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An Integrated Exploratory Geovisualization Environment Based on Self‐Organizing Map

Cited by 6 publications

References 19 publications

Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns

Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns

A framework for using self-organising maps to analyse spatio-temporal patterns, exemplified by analysis of mobile phone usage

On the Use of Three-Dimensional Self-Organizing Maps for Visualizing Clusters in Georeferenced Data

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