Latest revision as of 12:09, 16 February 2021

Abstract

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term ‘event’ refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: discover temporal relationship patterns between event locations, i.e., repeated cases when there is a specific temporal relationship (same time, before, or after) between events occurring at two locations. This can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.1109/tvcg.2018.2851227

• http://publica.fraunhofer.de/documents/N-524556.html

• https://openaccess.city.ac.uk/id/eprint/20131/1/V1.63%20minor%20review-final.pdf

• http://xplorestaging.ieee.org/ielx7/2945/8750990/08400404.pdf?arnumber=8400404,

http://dx.doi.org/10.1109/tvcg.2018.2851227

• https://dblp.uni-trier.de/db/journals/tvcg/tvcg25.html#LiCZAA19,

https://ieeexplore.ieee.org/document/8400404,

https://openaccess.city.ac.uk/id/eprint/20131,

https://www.ncbi.nlm.nih.gov/pubmed/29994614,

https://doi.org/10.1109/TVCG.2018.2851227,

https://academic.microsoft.com/#/detail/2810861266