Intelligent Cartographic Presentations for Emergency Situations

Ernst, Vera Hernández; Ostrovskii, Mark

doi:10.1007/978-3-540-75668-2_9

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…More detailed analysis shows the use of the GIS environment [36][37][38] with recently developed methodologies to support the decision-making process in crisis management at the local level [29,39] and it is emphasized that its essential part was the visualisation of crisis progress, shown with the use of interactive, realistic, large-scale simulations [40].…”

Section: Figurementioning

confidence: 99%

The Assessment of COVID-19 Vulnerability Risk for Crisis Management

et al. 2022

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The subject of this article is to determine COVID-19 vulnerability risk and its change over time in association with the state health care system, turnover, and transport to support the crisis management decision-making process. The aim was to determine the COVID-19 Vulnerability Index (CVI) based on the selected criteria. The risk assessment was carried out with methodology that includes the application of multicriteria analysis and spatiotemporal aspect of available data. Particularly the Spatial Multicriteria Analysis (SMCA) compliant with the Analytical Hierarchy Process (AHP), which incorporated selected population and environmental criteria were used to analyse the ongoing pandemic situation. The influence of combining several factors in the pandemic situation analysis was illustrated. Furthermore, the static and dynamic factors to COVID-19 vulnerability risk were determined to prevent and control the spread of COVID-19 at the early stage of the pandemic situation. As a result, areas with a certain level of risk in different periods of time were determined. Furthermore, the number of people exposed to COVID-19 vulnerability risk in time was presented. These results can support the decision-making process by showing the area where preventive actions should be considered.

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Section: Figurementioning

confidence: 99%

The Assessment of COVID-19 Vulnerability Risk for Crisis Management

et al. 2022

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“…The common approaches for decluttering visual displays, such as a priori (Cai et al 2006;Jul & Furnas, 1998;Ward 2002), human operator specified (Ernst & Ostrovskii 2007, Woodruff, Landay & Stonebraker, 1998 and random (Ellis & Dix, 2006) are not appropriate for first response domains, and in particular CBRNE incidents in which every incident differs. The a priori approach cannot anticipate all information that may arise during such an event, while the random approach may remove highly relevant information and retain highly irrelevant information.…”

Section: Introductionmentioning

confidence: 99%

Increasing Robot Saliency in Cluttered Visual Displays

Mou

Baker

Adams

2016

Proceedings of the Human Factors and Ergonomics Society Annual

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It has been shown that modulating the saliency of a dense amount of information presented as icons on a map-based interface can reduce cognitive workload and improve user performance. Further, first response teams, particularly those responding to complex events, such as Chemical, Biological, Radiological, Nuclear and Explosive device incidents will incorporate robots into their future teams to assist human team members and collect additional information. The deployment of such robots will require a human team member to supervise and task the various robots associated with the team. As the complexity of an incident increases and the number of responders with different specialties increases, for example Police, Emergency Medical Services, and Hazardous Materials, it will be harder to track the robots associated with a particular team, especially by the human team member responsible for the robots. A new algorithm, the Robot Visualization Algorithm, was developed to improve the saliency of robots for which the human team operator (e.g., Emergency Medical Services) is responsible, while generally minimizing the saliency of the robots from other teams (e.g., Police and Hazardous Materials) that are not relevant to the team operator. The presented Robot Visualization Algorithm makes the other teams’ robots more salient if their activities will impact the operator’s team. The within-subjects evaluation determined that the Robot Visualization Algorithm allowed operators to have a better awareness and lower cognitive workload than a base visualization condition. A number of proposed algorithm refinements are also discussed.

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