Interactive visualization of spatiotemporal patterns using spirals on a geographical map

Hewagamage, K. P.; Hirakawa, Masakazu; Ichikawa, Tadao

doi:10.1109/vl.1999.795916

Cited by 28 publications

(16 citation statements)

References 11 publications

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“…To solve this problem, the scientific community has also developed several solutions. Some are based on three-dimensional spaces, like space-time cubes [5], or spirals [27], which displays a 3D spiral on the objet, where each lap represents a period of time. [20].…”

Section: Visualization Toolsmentioning

confidence: 99%

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SILKNOWViz: Spatio-Temporal Data Ontology Viewer

Sevilla

Portalés

Gimeno

et al. 2019

Lecture Notes in Computer Science

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Interactive visualization of spatio-temporal data is a very active area that has experienced remarkable advances in the last decade. This is due to the emergence of fields of research such as big data and advances in hardware that allow better analysis of information. This article describes the methodology followed and the design of an open source tool, which in addition to interactively visualizing spatio-temporal data that are represented in an ontology, allows the definition of what to visualize and how to do it. The tool allows selecting, filtering and visualizing in a graphical way the entities of the ontology with spatiotemporal data, as well as the instances related to them. The graphical elements used to display the information are specified on the same ontology, extending the VISO graphic ontology, used for mapping concepts to graphic objects with RDFS/OWL Visualization Language (RVL). This extension contemplates the data visualization on rich real-time 3D environments, allowing different modes of visualization according to the level of detail of the scene, while also emphasizing the treatment of spatio-temporal data, very often used in cultural heritage models. This visualization tool involves simple visualization scenarios and high interaction environments that allow complex comparative analysis. It combines traditional solutions, like hypercube or time-animations with innovative data selection methods.

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Section: Visualization Toolsmentioning

confidence: 99%

“…Source: [19] To the right, two screenshots of an application that use 3D spirals, where each lap is a period of time and has additional information related with the appropriate year. Source: [27]. Spiral shows a lot information, but it is very difficult to analyze it.…”

Section: Visualization Toolsmentioning

confidence: 99%

SILKNOWViz: Spatio-Temporal Data Ontology Viewer

Sevilla

Portalés

Gimeno

et al. 2019

Lecture Notes in Computer Science

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“…Other authors also propose a spiral display, to visualise spatial-temporal patterns of cartography in 3 dimensions [17]. However, in our domain, spatial data refer to a few locations visited frequently, describing simpler spatial-temporal patterns.…”

Section: ) Aberrant Behaviourmentioning

confidence: 99%

Multiple Temporal Axes for Visualising the Behaviour of Elders Living Alone

Juárez

Ochotorena

Combi

2013

2013 IEEE International Conference on Healthcare Informatics

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Elders who live alone are vulnerable population at risk. Despite telemedicine and clinical home monitoring are effective to provide health care, accidents at home are still one of the major risk factors. Important efforts have been done in home monitoring systems to detect potential scenarios of risk. However, the complex technological interaction has a negative impact on the user and intrusive or wearable devices are not recommended. Sensorized houses solve this problem, recording the behaviour of elders at home without a direct interaction. However, the detection of health-risk scenarios is a complex issue, since the large amount temporal data registered by sensors must be correctly interpreted. In such a case, Artificial Intelligence techniques have been used to automatically identify abnormal behaviors. Due to the number false positives of these techniques, the human intervention is still necessary. We consider that a semiautomatic approach, abstracting and visualizing the recorded temporal information, is a more suitable approach. In this work, we propose a specialised visualization model that helps system supervisors to identify through visual mining techniques most common scenarios of risk. We illustrate our proposal in a specific home monitoring system.

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“…One record consists of multiple rows, one for each category of events. Spiral timelines -in which angle represents time interval (time of day, days of week, months, or years)were inspired by the cyclic nature of how we organize time and used to reveal periodic patterns in time series [8,16,37].…”

Section: Related Work Temporal Data Visualizationmentioning

confidence: 99%

LifeFlow

Wongsuphasawat

Gomez

Plaisant

et al. 2011

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

228

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Event sequence analysis is an important task in many domains: medical researchers may study the patterns of transfers within the hospital for quality control; transportation experts may study accident response logs to identify best practices. In many cases they deal with thousands of records. While previous research has focused on searching and browsing, overview tasks are often overlooked. We introduce a novel interactive visual overview of event sequences called LifeFlow. LifeFlow is scalable, can summarize all possible sequences, and represents the temporal spacing of the events within sequences. Two case studies with healthcare and transportation domain experts are presented to illustrate the usefulness of LifeFlow. A user study with ten participants confirmed that after 15 minutes of training novice users were able to rapidly answer questions about the prevalence and temporal characteristics of sequences, find anomalies, and gain significant insight from the data.

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Interactive visualization of spatiotemporal patterns using spirals on a geographical map

Cited by 28 publications

References 11 publications

SILKNOWViz: Spatio-Temporal Data Ontology Viewer

SILKNOWViz: Spatio-Temporal Data Ontology Viewer

Multiple Temporal Axes for Visualising the Behaviour of Elders Living Alone

LifeFlow

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