Mining candidate causal relationships in movement patterns
Galton, A; Bleisch, S; Duckham, M; et al.Laube, P; Lyon, J
Date: 1 October 2013
Journal
International Journal of Geographical Information Science
Publisher
Taylor & Francis
Publisher DOI
Abstract
In many applications, the environmental context for, and drivers of movement patterns are just as important as the patterns themselves. This paper adapts standard data mining techniques, combined with a foundational ontology of causation, with the objective of helping domain experts identify candidate causal relationships between ...
In many applications, the environmental context for, and drivers of movement patterns are just as important as the patterns themselves. This paper 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 the technique is capable of identifying statistically significant patterns of movement indicative of causal and causal-like relationships. 1365-8816
Computer Science
Faculty of Environment, Science and Economy
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