Mining candidate causal relationships in movement patterns
In many applications, the environmental context for and drivers of movement patterns are just as important as the patterns themselves. This article adapts standard data mining techniques, combined with a foundational ontology of causation, with the objective of helping domain experts
identify candidate causal relationships between movement patterns and their environmental context. In addition to data about movement and its dynamic environmental context, our approach requires as input definitions of the states and events of interest. The technique outputs causal and causal-like
relationships of potential interest, along with associated measures of support and confidence. As a validation of our approach, the analysis is applied to real data about fish movement in the Murray River in Australia. The results demonstrate that the technique is capable of identifying statistically
significant patterns of movement indicative of causal and causal-like relationships.
Keywords: causation; context-aware movement analysis; environmental monitoring; geosensor networks; movement patterns; sequence mining
Document Type: Research Article
Affiliations: 1: Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia 2: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK 3: Department of Geography, University of Zürich, Zürich, Switzerland 4: Arthur Rylah Research Institute, Heidelberg, VIC, Australia
Publication date: 01 February 2014
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