Published August 28, 2018 | Version v1
Conference paper Open

Stay-Move Tree for Summarizing Spatiotemporal Trajectories

Creators

  • 1. University Research Priority Program (URPP) "Dynamics of Healthy Aging" & Department of Geography, University of Zurich, Zürich, Switzerland

Description

Abstract. Summarizing spatiotemporal trajectories of a large number of individual objects or events provides insight into collective patterns of phenomena. A well-defined data model can serve as a vehicle for classifying and analyzing data sets efficiently. This paper proposes the Stay-Move tree (SM tree) to represent frequency distributions for types of trajectories by introducing concepts of stay and move. The proposed tree model was applied to analyzing the Korean Household Travel Survey data. The preliminary results show that the proposed SM trees can potentially be employed to compare/classify spatiotemporal trajectories of different groups (e.g., demographic groups or species of animals). The methodology can potentially be useful to summarize big trajectory data observed from both human and natural phenomena.

Cite as:

Eun-Kyeong Kim. (2018). Stay-Move Tree for Summarizing Spatiotemporal Trajectories. In Martin Raubal, Shaowen Wang, Mengyu Guo, David Jonietz, & Peter Kiefer. (editors). Spatial Big Data and Machine Learning in GIScience, Workshop at GIScience 2018, Melbourne, Australia, 2018. http://doi.org/10.5281/zenodo.3402236

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References

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