Visual Explorations in Finance
Visual Explorations in Finance
Self-Organizing Maps
Visual Exploration of the Spatial Distribution of Temporal Behaviors
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)
IEEE Transactions on Visualization and Computer Graphics
WIAMIS '08 Proceedings of the 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services
IEEE Transactions on Visualization and Computer Graphics
Using treemaps for variable selection in spatio-temporal visualisation
Information Visualization
Visual cluster analysis of trajectory data with interactive Kohonen maps
Information Visualization
The self-organizing map, the Geo-SOM, and relevant variants for geosciences
Computers & Geosciences
Generating color palettes using intuitive parameters
EuroVis'08 Proceedings of the 10th Joint Eurographics / IEEE - VGTC conference on Visualization
An alternative map of the United States based on an n-dimensional model of geographic space
Journal of Visual Languages and Computing
Interactive visual analysis of temporal cluster structures
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Opening up the "black box" of medical image segmentation with statistical shape models
The Visual Computer: International Journal of Computer Graphics
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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.