Visual data fusion for applications of high-resolution numerical weather prediction

Treinish,

doi:10.1109/visual.2000.885736

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…Treinish [20,21] has conducted various research works on weather data visualization but his approaches lean more toward scientific visualization rather than information visualization. Healey et al [9] used nonphotorealistic brush strokes for visualizing multidimensional information spaces like weather data.…”

Section: Related Workmentioning

confidence: 99%

Visual Analysis of the Air Pollution Problem in Hong Kong

Chan

et al. 2007

IEEE Trans. Visual. Comput. Graphics

113

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Abstract-We present a comprehensive system for weather data visualization. Weather data are multivariate and contain vector fields formed by wind speed and direction. Several well-established visualization techniques such as parallel coordinates and polar systems are integrated into our system. We also develop various novel methods, including circular pixel bar charts embedded into polar systems, enhanced parallel coordinates with S-shape axis, and weighted complete graphs. Our system was used to analyze the air pollution problem in Hong Kong and some interesting patterns have been found.

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Section: Related Workmentioning

confidence: 99%

Visual Analysis of the Air Pollution Problem in Hong Kong

Chan

et al. 2007

IEEE Trans. Visual. Comput. Graphics

113

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“…For example, it is possible to transform and merge multiple geospatial datasets together into a common viewing framework. 12 Alternately, multiple coordinated views of the different datasets may be provided, [13][14][15] such that manipulations in one representation (e.g. panning and zooming) automatically update the configurations of the other views.…”

Section: Related Workmentioning

confidence: 99%

A geovisual analytics approach for analyzing event-based geospatial anomalies within movement data

Hoeber

Hasan

2017

Information Visualization

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Comparing data collected on the movement of an entity to data on the location where the entity was reported to have been can be useful in monitoring and enforcement situations. Anomalies between these datasets may be indicative of illegal activity, systematic reporting errors, data entry errors, or equipment failure. While finding obvious anomalies may be a simple task, the discovery of more subtle inconsistencies can be challenging when there is a mismatch in the temporal granularity between the datasets, or when they cover large temporal and geographic ranges. We have developed a geovisual analytics approach called Visual Exploration of Movement-Event Anomalies (VEMEA) that automatically extracts potential anomalies from the data, visually encodes these on a map, and provides interactive filtering and exploration tools to allow expert analysts to investigate and evaluate the anomalies. Using two case studies from the fisheries enforcement domain, the value of VEMEA is illustrated for both confirmatory and exploratory data analysis tasks. Field trial evaluations conducted with expert fisheries data analysts further support the benefits of the approach.

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“…In addition to the traditional approach of layering multiple datasets on the same map, more advanced approaches have been explored. For example, it is possible to transform and merge multiple geospatial datasets together into a common viewing framework (Treinish, 2000). Alternately, multiple coordinated views of the different datasets may be provided (Johansson and Jern, 2007;Mandiak et al, 2005), such that manipulations in one representation (e.g., panning and zooming) affect the configuration of the others.…”

Section: Related Workmentioning

confidence: 99%

Supporting Event-based Geospatial Anomaly Detection with Geovisual Analytics

Hoeber

Hasan

2015

Proceedings of the 6th International Conference on Information Visualization Theory and Applications

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Collecting multiple geospatial datasets that describe the same real-world events can be useful in monitoring and enforcement situations (e.g., independently tracking where a fishing vessel travelled and where it reported to have fished). While finding the obvious anomalies between such datasets may be a simple task, discovering more subtle inconsistencies can be challenging when the datasets describe many events that cover large geographic and temporal ranges. This paper presents a geovisual analytics approach to this problem domain, automatically extracting potential event anomalies from the data, visualizing these on a map, and providing interactive filtering tools to allow expert analysts to discover and analyze patterns that are of interest. A case study is presented, illustrating the value of the approach for discovering anomalies between commercial fishing vessel movement data and their reported fishing locations. Field trial evaluations confirm the benefits of this geovisual analytics approach for supporting real-world data analyst needs.

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Visual data fusion for applications of high-resolution numerical weather prediction

Cited by 14 publications

References 9 publications

Visual Analysis of the Air Pollution Problem in Hong Kong

Visual Analysis of the Air Pollution Problem in Hong Kong

A geovisual analytics approach for analyzing event-based geospatial anomalies within movement data

Supporting Event-based Geospatial Anomaly Detection with Geovisual Analytics

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