A Visualization Environment for the Space-Time-Cube

Kraak, M.J.; Koussoulakou, Alexandra

doi:10.1007/3-540-26772-7_15

Cited by 37 publications

(31 citation statements)

References 21 publications

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“…At the faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, the Netherlands, a software implementation of Torsten Hägerstrand's STC was developed, in order to support research in visualisation and analysis of spatio-temporal and multi-attribute data sets (Kraak and Koussoulakou 2004). The objective was to take full advantage of the capabilities of 3D graphics hardware, that is available on all modern computers, in order to resemble an interactive STC.…”

Section: Embedding the Algorithm Into A Visual Environmentmentioning

confidence: 99%

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

Turdukulov¹,

Romero²,

Huisman³

et al. 2014

International Journal of Geographical Information Science

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The popularity of tracking devices continues to contribute to increasing volumes of spatio-temporal data about moving objects. Current approaches in analysing these data are unable to capture collective behaviour and correlations among moving objects. An example of these types of patterns is moving flocks. This article develops an improved algorithm for mining such patterns following a frequent pattern discovery approach, a well-known task in traditional data mining. It uses transaction-based data representation of trajectories to generate a database that facilitates the application of scalable and efficient frequent pattern mining algorithms. Results were compared with an existing method (Basic Flock Evaluation or BFE) and are demonstrated for both synthetic and real data sets with a large number of trajectories. The results illustrate a significant performance increase. Furthermore, the improved algorithm has been embedded into a visual environment that allows manipulation of input parameters and interactive recomputation of the resulting flocks. To illustrate the visual environment a data set containing 30 years of tropical cyclone tracks with 6 hourly observations is used. The example illustrates how the visual environment facilitates exploration and verification of flocks by changing the input parameters and instantly showing the spatio-temporal distribution of the resulting flocks in the Space-Time Cube and interactively selecting, querying and saving the resulting flocks for further analysis and verification.

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Section: Embedding the Algorithm Into A Visual Environmentmentioning

confidence: 99%

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

Turdukulov¹,

Romero²,

Huisman³

et al. 2014

International Journal of Geographical Information Science

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“…The recent revival in the interest in the STC is due to the development of new technologies which makes data collection as well as creation and use of the cube much easier than before [35,36,37,38,39,40,41,42]. Typical geovisualization characteristics like interaction, dynamic and alternative views are now also applicable to the STC, and made it part of an exploratory environment.…”

Section: State-of-the-art In Spatio-temporal Geovisualizationmentioning

confidence: 99%

Visual Exploration of Eye Movement Data Using the Space-Time-Cube

Çöltekin

Kraak

2010

Lecture Notes in Computer Science

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Abstract. Eye movement recordings produce large quantities of spatiotemporal data, and are more and more frequently used as an aid to gain further insight into human thinking in usability studies in GIScience domain among others. After reviewing some common visualization methods for eye movement data, the limitations of these methods are discussed. This paper proposes an approach that enables the use of the Space-Time-Cube (STC) for representation of eye movement recordings. Via interactive functions in the STC, spatiotemporal patterns in eye movement data could be analyzed. A case study is presented according to proposed solutions for eye movement data analysis. Finally, the advantages and limitations of using the STC to visually analyze eye movement recordings are summarized and discussed.

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“…Cultural heritage research has adapted the ideas of the space-time cube as one of the methods of challenging and enhancing the previous knowledge. Kraak and Koussoulakou (2004) considered the space-time cube as a possible exploration tool for deriving the spatiotemporal characteristics of archaeological sites. Furthermore, the use of the space-time cube in humanities research is not limited to geodata visualizations.…”

Section: Introductionmentioning

confidence: 99%

Mapping historical landscape changes with the use of a space-time cube

Bogucka

Jahnke

2018

Proc. Int. Cartogr. Assoc.

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Abstract:In this contribution, we introduce geographic concepts in the humanities and present the results of a spacetime visualization of ancient buildings over the last centuries. The techniques and approaches used were based on cartographic research to visualize spatio-temporal information. As a case study, we applied cartographic styling techniques to a model of the Royal Castle in Warsaw and its different spatial elements, which were constructed and destroyed during their eventful history. In our case, the space-time cube approach seems to be the most suitable representation of this spatio-temporal information. Therefore, we digitized the different footprints of the castle during the ancient centuries as well as the landscape structure around, and annotated them with monarchies, epochs and time. During the digitization process, we had to cope with difficulties like sources in various scales and map projections, which resulted in varying accuracies. The results were stored in KML to support a wide variety of visualization platforms.

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A Visualization Environment for the Space-Time-Cube

Cited by 37 publications

References 21 publications

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

Visual Exploration of Eye Movement Data Using the Space-Time-Cube

Mapping historical landscape changes with the use of a space-time cube

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