Geovisual analytics to support crisis management: Information foraging for geo-historical context

Brian Tomaszewski, Alan M. MacEachren

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

Information foraging and sense-making with heterogeneous information are context-dependent activities. Thus visual analytics tools to support these activities must incorporate context. But, context is a difficult concept to define, model, and represent. Creating and representing context in support of visually-enabled reasoning about complex problems with complex information is a complementary but different challenge than that addressed in context-aware computing. In the latter, the goal is automated system adaptation to meet user application needs such as location-based services where information about the location, the user, and user goals filters what gets presented on a small mobile device. In contrast, for visual analytics-enabled information foraging and sense-making, the user generally takes an active role in foraging for the contextual information needed to support sense-making in relation to some multifaceted problem. In this paper, we address the challenges of constructing and representing context within visual interfaces that support analytic reasoning in crisis management and humanitarian relief. The challenges stem from the diverse forms of information that can provide context and difficulty in defining and operationalizing context itself. Here, we focus on document foraging to support construction of geographic and historical context for facilitating monitoring and sense-making. Specifically, we present the concept of geo-historical context and outline an empirical assessment of both the concept and its implementation in the Context Discovery Application (CDA), a web-based tool that supports document foraging and sense-making. We also discuss the CDA's transition into applied use for the United Nations to demonstrate the generality of underlying CDA concepts.

Original languageEnglish (US)
Pages (from-to)339-359
Number of pages21
JournalInformation Visualization
Volume11
Issue number4
DOIs
Publication statusPublished - Oct 1 2012

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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