Interactive analysis of event data using space-time cube

Gatalsky, Peter; Andrienko, Natalia; Andrienko, Gennady

doi:10.1109/iv.2004.1320137

Cited by 142 publications

(115 citation statements)

References 12 publications

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“…The creation of meaningful spatiotemporal visualization for humanities specialists involves not only cartographic design approaches but also data processing and management. Previous research on space-time cubes has focused mainly on point and polyline features to visualize states or paths within a certain timeframe (Gatalsky et al 2010, Kveladze et al 2013. A major issue of creating spatiotemporal visualizations for geo-historical data is to cope with detailed scale analyses of polygon features that have been chosen for representing the buildings at different epochs.…”

Section: Study Area Data Sources and Methodsmentioning

confidence: 99%

“…The idea of a space-time cube was developed by Hägerstrand (1970) and originates from humanities research focused on social interactions and human movements in space and time (Kveladze et al 2013). Within the space-time cube, the two-dimensional geographical space is represented by the base of the cube (Gatalsky et al 2010), while time is considered as the third, vertical dimension (cube's height). Cultural heritage research has adapted the ideas of the space-time cube as one of the methods of challenging and enhancing the previous knowledge.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Study Area Data Sources and Methodsmentioning

confidence: 99%

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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“…• Recommendation systems (Klerkx, Duval, & Meire, 2004;Ng & Martin, 2004;Nguyen, Huang, & Hawryszkiewycz, 2004) • Approaches that focussed only on contextual information without taking any user interaction footprints into account (Gatalsky, Andrienko, & Andrienko, 2004).…”

Section: Current Perception Of the Learning Interaction Supportmentioning

confidence: 99%

Smart indicators to support the learning interaction cycle

Glahn

Specht

Koper

2008

IJCEELL

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This paper focuses on the relevant information for collecting and presenting contextual information, its effects and impact on the learning interaction cycle, and mechanisms of controlling it. The core principle of the learning interaction cycle is the interaction of learners with their learning environment. Previous research highlights that such interaction is important for the experience and progress of learners. However, we identified a conceptual gap between the learner actions within a learning environment and the responses that are provided to the learners. To bridge this gap we adopted a layered model of context-aware systems that meets the requirements for supportive responses. The model has four layers and describes the information processing of interaction footprints of learners in a learning environment: The sensor layer, the semantic layer, the control layer, and the indicator layer. We applied this model to analyse the results as they were reported in the literature. Prof. Dr. Rob Koper holds a PhD in educational technology from the Open University of the Netherlands. Since 1998 he has been a full professor in educational technology, specifically in e-learning technologies. As director of RTD into learning technologies he was, among other things, responsible for the development of Educational Modelling Language (the predecessor of IMS Learning Design). He led the OUNL's contribution to the IMS LD specification activities. His research focuses on self-organised distributed learning networks for lifelong learning, including RTD into software agents, educational semantic web, interoperability specifications and standards.

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“…Estos fueron inicialmente conceptualizados por Hägerstraand (1970), donde el espacio es representado en un plano de dos dimensiones, mientras que el tiempo es representado en la tercera dimensión, correspondiente al eje-Z. Su función inicial fue identificar trayectorias entre puntos origen/destino, donde podían identificarse patrones referentes a la velocidad y sitios de parada (Gatalsky, Andrienko, & Andrienko, 2004). Por tanto, siguiendo su propósito inicial de análisis de patrones de movimiento, pueden identificarse tendencias dentro de sets de datos de movilidad para caracterizar en cierta medida los problemas de transporte en una ciudad.…”

Section: Introductionunclassified

Análisis exploratorio de comportamientos de ciclistas voluntarios mediante minería de patrones espacio-temporales en Cuenca, Ecuador

Abad

Orellana

2018

MSKN

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RESUMENEl uso de técnicas de minería de datos combinadas con metodologías como los cubos espacio-temporales potencian en gran medida el análisis de bases de datos multidimensionales. Una de las áreas de aplicación más importantes es el estudio de la movilidad no motorizada, la cual constituye una de las prioridades más importantes de la mayor parte ciudades alrededor del mundo. Este estudio busca extraer patrones en una base de datos de movilidad de ciclistas voluntarios en Cuenca (Ecuador) para identificar lugares y momentos relevantes para el diseño y aplicación de estrategias para mejorar la movilidad en bicicleta. La metodología está basada en la caja de herramientas para la minería de patrones espaciotemporales de ArcGIS. Los resultados demuestran la viabilidad de la metodología propuesta para la caracterización de la movilidad no motorizada y su potencial uso en otros conjuntos de datos de movilidad. Palabras clave: Movilidad no motorizada, ciclismo urbano, análisis exploratorio, minería de patrones espacio-temporales, cubos espacio-temporales. ABSTRACTData mining techniques combined with space-time cubes empower the analysis of multidimensional data. A study area in which these advanced analysis techniques can be applied pre-eminently is urban mobility, where investigation of non-motorized mobility patterns is a main priority for several cities around the world. The presented work aimed to extract spatio-temporal patterns from a human movement database containing volunteer-generated cycling data in Cuenca (Ecuador) with the objective to detect places and times where strategies can be applied that promote urban cycling. The methodology takes advantage of the capabilities of the space-time pattern mining toolbox in ArcGIS. The results demonstrate the viability of the proposed methodology for the characterization of non-motorized mobility patterns and its potential for analyzing other mobility datasets.

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Interactive analysis of event data using space-time cube

Abstract: In exploratory data analysis, the choice of tools depends on the data to be analyzed and the analysis tasks, i.e

Cited by 142 publications

References 12 publications

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

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

Smart indicators to support the learning interaction cycle

Análisis exploratorio de comportamientos de ciclistas voluntarios mediante minería de patrones espacio-temporales en Cuenca, Ecuador

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