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Geovisualization applications to examine and explore high-density and hierarchical critical infrastructure data

Preprint published in 2018 by Robert Edsall, Harvey Hembree
This paper is available in a repository.
This paper is available in a repository.

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Preprint: policy unknown
Question mark in circle
Postprint: policy unknown
Question mark in circle
Published version: policy unknown

Abstract

The geospatial research and development team in the National and Homeland Security Division at Idaho National Laboratory was tasked with providing tools to derive insight from the substantial amount of data currently available – and continuously being produced – associated with the critical infrastructure of the US. This effort is in support of the Department of Homeland Security, whose mission includes the protection of this infrastructure and the enhancement of its resilience to hazards, both natural and human. We present geovisual-analytics-based approaches for analysis of vulnerabilities and resilience of critical infrastructure, designed so that decision makers, analysts, and infrastructure owners and managers can manage risk, prepare for hazards, and direct resources before and after an incident that might result in an interruption in service. Our designs are based on iterative discussions with DHS leadership and analysts, who in turn will use these tools to explore and communicate data in partnership with utility providers, law enforcement, and emergency response and recovery organizations, among others. In most cases these partners desire summaries of large amounts of data, but increasingly, our users seek the additional capability of focusing on, for example, a specific infrastructure sector, a particular geographic region, or time period, or of examining data in a variety of generalization or aggregation levels. These needs align well with tenets of in-formation-visualization design; in this paper, selected applications among those that we have designed are described and positioned within geovisualization, geovisual analytical, and information visualization frameworks.

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