An interactive visualization approach to the overview of geoscience data

Li, Jie; Meng, Zhao; Huang, Mao Lin; Zhang, Kang

doi:10.1007/s12650-016-0352-z

Cited by 2 publications

(2 citation statements)

References 36 publications

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“…Equatorial Guinea is a special case, which is far from China but has a high co-occurrence strength. According to the literature [32], we know that the discoveries of large oil fields at the beginning of the 90's made the economic development of Equatorial Guinea rapid, which proves our finding. The expert thinks these findings are potentially very interesting, possibly leading to the discovery of implicit relationship among countries, such as teleconnection.…”

Section: Economic Statistical Datasupporting

confidence: 75%

“…The possibilities for a combined visualization of the spatial and temporal aspects of data in a single display are very limited, and the existing scarce approaches are not scalable to large amounts of data [7]. A common approach is to use coordinated views [44] [32], where map displays show the spatial aspect and various kinds of time-oriented displays [1] [3] show the temporal aspect. Waldner et al [53] used curves to link multiple aspects of objects in different projection space.…”

Section: Spatiotemporal Visualizationmentioning

confidence: 99%

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COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series

Chen

Zhang³

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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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.

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Section: Economic Statistical Datasupporting

confidence: 75%