Designing a visual environment for exploration of time series of remote sensing data: In search for convective clouds

Turdukulov, U.; Kraak, M.J.; Blok, C.A.

doi:10.1016/j.cag.2007.01.028

Cited by 34 publications

(20 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, these techniques are used for the reconstruction of events, movers, and trajectories from images (e.g. Turdukulov et al 2007, Höferlin et al 2009). …”

Section: Extraction Of Specific Relationsmentioning

confidence: 99%

A conceptual framework and taxonomy of techniques for analyzing movement

Andrienko

Bak

et al. 2011

Journal of Visual Languages & Computing

126

View full text Add to dashboard Cite

This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractMovement data link together space, time, and objects positioned in space and time. They hold valuable and multifaceted information about moving objects, properties of space and time as well as events and processes occurring in space and time. We present a conceptual framework that describes in a systematic and comprehensive way the possible types of information that can be extracted from movement data and on this basis defines the respective types of analytical tasks. Tasks are distinguished according to the type of information they target and according to the level of analysis, which may be elementary (i.e. addressing specific elements of a set) or synoptic (i.e. addressing a set or subsets). We also present a taxonomy of generic analytic techniques, in which the types of tasks are linked to the corresponding classes of techniques that can support fulfilling them. We include techniques from several research fields: visualization and visual analytics, geographic information science, database technology, and data mining.We expect the taxonomy to be valuable for analysts and researchers. Analysts will receive guidance in choosing suitable analytic techniques for their data and tasks.Researchers will learn what approaches exist in different fields and compare or relate them to the approaches they are going to undertake.3

show abstract

“…In particular, these techniques are used for the reconstruction of events, movers, and trajectories from images (e.g. Turdukulov et al 2007, Höferlin et al 2009). …”

Section: Extraction Of Specific Relationsmentioning

confidence: 99%

A conceptual framework and taxonomy of techniques for analyzing movement

Andrienko

Bak

et al. 2011

Journal of Visual Languages & Computing

126

View full text Add to dashboard Cite

show abstract

“…A similar work which focuses on precipitating clouds can be found in [45]. The initial data in [45] is a set of satellite images (2D) whereas the initial data in our work are lightning point coordinates in 3D.…”

Section: Explorative Visualization Of Lightning Datamentioning

confidence: 99%

“…The initial data in [45] is a set of satellite images (2D) whereas the initial data in our work are lightning point coordinates in 3D. In [15], it is stated that one of the challenges for researchers within geovisualization and geovisual analytics is to deal with different spatio-temporal data and find appropriate solutions for the respective data.…”

Section: Explorative Visualization Of Lightning Datamentioning

confidence: 99%

“…In [15], it is stated that one of the challenges for researchers within geovisualization and geovisual analytics is to deal with different spatio-temporal data and find appropriate solutions for the respective data. In [45], a Space-Time-Cube (STC) with icons is presented, whose radius is proportional to the size of the precipitating cloud region. With our work we want to provide not only a STC, but also a 3D view within an interactive exploring tool, which intends to offer different lightning cluster/track presentations to the user (see Section 4.4.1).…”

Section: Explorative Visualization Of Lightning Datamentioning

confidence: 99%

See 1 more Smart Citation

Visual Analysis for Nowcasting of Multidimensional Lightning Data

Peters

Meng

2013

IJGI

View full text Add to dashboard Cite

Globally, most weather-related damages are caused by thunderstorms. Besides floods, strong wind, and hail, one of the major thunderstorm ground effects is lightning. Therefore, lightning investigations, including detection, cluster identification, tracking, and nowcasting are essential. To enable reliable decisions, current and predicted lightning cluster-and track features as well as analysis results have to be represented in the most appropriate way. Our paper introduces a framework which includes identification, tracking, nowcasting, and in particular visualization and statistical analysis of dynamic lightning data in three-dimensional space. The paper is specifically focused on enabling users to conduct the visual analysis of lightning data for the purpose of identification and interpretation of spatial-temporal patterns embedded in lightning data, and their dynamics. A graphic user interface (GUI) is developed, wherein lightning tracks and predicted lightning clusters, including their prediction certainty, can be investigated within a 3D view or within a Space-Time-Cube. In contrast to previous work, our approach provides insight into the dynamics of past and predicted 3D lightning clusters and cluster features over time. We conclude that an interactive visual exploration in combination with a statistical analysis can provide new knowledge within lightning investigations and, thus, support decision-making in weather forecast or lightning damage prevention.

show abstract

“…Galatsky et.al [19], for example, use the time cube for visualizing unconnected earthquake events while Turdukulov et.al [20] use a time cube-like representation, the event graph, for showing events in the paths of clouds in time and space. These representations tend to become overwhelming with a large data size due to clutter.…”

Section: Related Workmentioning

confidence: 99%

2D and 3D Representations for Feature Recognition in Time Geographical Diary Data

Vrotsou

Forsell

Cooper

2009

Information Visualization

View full text Add to dashboard Cite

Time geographical representations are becoming a common approach to analysing spatio-temporal data. Such representations appear intuitive in the process of identifying patterns and features since paths of populations form tracks through the 3D space which can be seen converging and diverging over time. In this paper we compare 2D and 3D representations within a time geographical visual analysis tool for activity diary data. We define a set of representative tasks and evaluate task performance between the two representations. The results show that the 3D representation has benefits over the 2D representation for feature identification but also indicate that these benefits can be lost if the 3D representation is not carefully constructed to permit the user to see them.

show abstract

Designing a visual environment for exploration of time series of remote sensing data: In search for convective clouds

Cited by 34 publications

References 12 publications

A conceptual framework and taxonomy of techniques for analyzing movement

A conceptual framework and taxonomy of techniques for analyzing movement

Visual Analysis for Nowcasting of Multidimensional Lightning Data

2D and 3D Representations for Feature Recognition in Time Geographical Diary Data

Contact Info

Product

Resources

About