A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatio-Temporal Data Mining for Typhoon Image Collection
Journal of Intelligent Information Systems
Time-Expanded Graphs for Flow-Dependent Transit Times
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Discovering and Summarising Regions of Correlated Spatio-Temporal Change in Evolving Graphs
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
IEEE Transactions on Knowledge and Data Engineering
Change analysis in spatial datasets by interestingness comparison
SIGSPATIAL Special
Change detection in rainfall and temperature patterns over India
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Time-Aggregated graphs for modeling spatio-temporal networks
CoMoGIS'06 Proceedings of the 2006 international conference on Advances in Conceptual Modeling: theory and practice
Characterizing sensor datasets with multi-granular spatio-temporal intervals
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Mining trajectories of moving dynamic spatio-temporal regions in sensor datasets
Data Mining and Knowledge Discovery
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Spatio-temporal data from earth observation systems and models are increasing at astronomical rates in the climate domain. This results in a massive dataset that is increasingly difficult to navigate to find interesting time periods where the spatial pattern of a process changes. The ability to navigate to such areas can lead to new knowledge about the factors that contribute to a spatio-temporal process. This paper proposes a method to automatically characterize multi-variate spatio-temporal datasets using basic image processing techniques and an efficient distance measure. The approach uses a measure of local image entropy combined with edge detection to find naturally occurring boundaries in the dataset. Then a distance measure is used to track the change in these boundaries over time. The resulting measure of spatio-temporal change can be used to explore spatio-temporal datasets to find new relationships between the spatial pattern of variables over time. Experiments were performed on a real-world climate dataset and the results were promising in that new patterns emerged and interesting relationships between variables were found.