Geo-historical context support for information foraging and sensemaking: Conceptual model, implementation, and assessment

Brian Tomaszewski, Alan M. MacEachren

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Information foraging and sensemaking 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 adaptation of the system to meet user needs for applications such as mobile location-based services where information about the location, the user, and the user goals filters what gets presented on a small mobile device. In contrast, for visual analytics-enabled information foraging and sensemaking, the user is likely to take an active role in foraging for the contextual information needed to support sensemaking in relation to some multifaceted problem. In this paper, we address the challenges of constructing and representing context within visual interfaces that support analytical 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 pay particular attention to document foraging to support construction of the geographic and historical context within which monitoring and sensemaking can be carried out. Specifically, we present the concept of geo-historical context (GHC) and outline an empirical assessment of both the concept and its implementation in the Context Discovery Application, a web-based tool that supports document foraging and sensemaking.

Original languageEnglish (US)
Title of host publicationVAST 10 - IEEE Conference on Visual Analytics Science and Technology 2010, Proceedings
Pages139-146
Number of pages8
DOIs
StatePublished - Dec 1 2010
Event1st IEEE Conference on Visual Analytics Science and Technology, VAST 10 - Salt Lake City, UT, United States
Duration: Oct 24 2010Oct 29 2010

Publication series

NameVAST 10 - IEEE Conference on Visual Analytics Science and Technology 2010, Proceedings

Other

Other1st IEEE Conference on Visual Analytics Science and Technology, VAST 10
CountryUnited States
CitySalt Lake City, UT
Period10/24/1010/29/10

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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