Self-Organizing Maps
Visual Exploration of the Spatial Distribution of Temporal Behaviors
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Visual Analytics: Definition, Process, and Challenges
Information Visualization
Visually driven analysis of movement data by progressive clustering
Information Visualization
Visual cluster analysis of trajectory data with interactive Kohonen maps
Information Visualization
Space-time modeling of traffic flow
Computers & Geosciences
A Visual Analytics Approach to Understanding Spatiotemporal Hotspots
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Spatial Generalization and Aggregation of Massive Movement Data
IEEE Transactions on Visualization and Computer Graphics
Journal of Location Based Services - GeoVA(t) - Geospatial visual analytics: focus on time. Special issue of the ICA Commission on GeoVisualisation
A pandemic influenza modeling and visualization tool
Journal of Visual Languages and Computing
Visual sensitivity analysis for artificial neural networks
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Tracing the German centennial flood in the stream of tweets: first lessons learned
Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
Hi-index | 0.00 |
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 perspective, 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.