Name
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: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time. This type of analysis 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TVCG.2018.2851227 | IEEE transactions on visualization and computer graphics |
Keywords | Field | DocType |
Time series analysis,Visual analytics,Data mining,Data visualization,Spatiotemporal phenomena,Economics,Shape | Interdependence,Data science,Economic statistics,Time series,Data visualization,Computer science,Visual analytics,Co-occurrence,Theoretical computer science,Event analysis,Scalability | Journal |
Volume | Issue | ISSN |
25 | 8 | 1941-0506 |
Citations | PageRank | References |
6 | 0.38 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Li | 1 | 41 | 8.35 |
| Siming Chen | 2 | 125 | 14.34 |
| Kang Zhang | 3 | 1054 | 126.26 |
| Gennady Andrienko | 4 | 3106 | 208.19 |
| Natalia Andrienko | 5 | 2922 | 192.14 |