The visual display of quantitative information
The visual display of quantitative information
LifeLines: visualizing personal histories
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Interactive visualization of serial periodic data
Proceedings of the 11th annual ACM symposium on User interface software and technology
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Maintaining knowledge about temporal intervals
Communications of the ACM
Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visualizing Data
Statistical Models in S
Analyzing Relative Motion within Groups of Trackable Moving Point Objects
GIScience '02 Proceedings of the Second International Conference on Geographic Information Science
Managing Time in GIS: An Event-Oriented Approach
Proceedings of the International Workshop on Temporal Databases: Recent Advances in Temporal Databases
Time Series Abstraction Methods - A Survey
Informatik bewegt: Informatik 2002 - 32. Jahrestagung der Gesellschaft für Informatik e.v. (GI)
Interactive Visualization of Spatiotemporal Patterns Using Spirals on a Geographical Map
VL '99 Proceedings of the IEEE Symposium on Visual Languages
Cluster and Calendar Based Visualization of Time Series Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Visualizing Time-Series on Spirals
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
Modeling and comparing change using spatiotemporal helixes
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
Dynamic query tools for time series data sets: timebox widgets for interactive exploration
Information Visualization
Interactive Analysis of Event Data Using Space-Time Cube
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Event Detection by Eigenvector Decomposition Using Object and Frame Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
BinX: Dynamic Exploration of Time Series Datasets Across Aggregation Levels
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Semiology of graphics
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An ontology based personal exposure history
Proceedings of the 1st ACM International Health Informatics Symposium
Tag clouds for displaying semantics: the case of filmscripts
Information Visualization
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The expanding deployment of sensor systems that capture location, time, and multiple thematic variables is increasing the need for exploratory spatio-temporal data analysis tools. Geographic information systems (GIS) and time series analysis tools support exploration of spatial and temporal patterns respectively and independently, but tools for the exploration of both dimensions within a single system are relatively rare. The contribution of this research is a framework for the visualization and exploration of spatial, temporal, and thematic dimensions of sensor-based data. The unit of analysis is an event, a spatio-temporal data type extracted from sensor data. The conceptual framework suggests an approach for design layout that can be flexibly modified to explore spatial and temporal trends, temporal relationships among events, periodic temporal patterns, the timing of irregularly repeating events, event-event relationships in terms of thematic attributes, and event patterns at different spatial and temporal granularities. Flexible assignment of spatial, temporal, and thematic categories to a set of graphical interface elements that can be easily rearranged provides exploratory power as well as a generalizable design layout structure. The framework is illustrated with events extracted from Gulf of Maine Ocean Observing System data but the approach has broad application to other domains and applications in which time, space, and attributes need to be considered in conjunction.