Component 1: Current and Future Methods for Representing and Interacting with Qualitative Geographic Information

Abstract

Geographically-grounded text information is an increasingly common data type that has the potential to increase our ability to understand place-based activities and processes dramatically if methods can be developed to extract, process, and represent that information as well as to connect the information with more traditional geographic data organized within GIS and related technologies. A variety of approaches exist for visual exploration and analysis of text media, and this report highlights and categorizes known approaches towards handling text information in information visualization and geographic information technologies. In addition, we describe the most common techniques for interacting with textual data and its derivatives in geographic and non-geographic visualization systems. Finally, we propose several graphical methods for using text itself to represent different dimensions of geographic information. These methods, as well as others we review from previous work, help elaborate a path forward for future geographic information technologies that can more effectively leverage geographically-grounded text.

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Document Details

Document Type
Technical Report
Publication Date
Oct 26, 2011
Accession Number
ADA579193

Entities

People

  • Alan M. Maceachren
  • Alexander Savelyev
  • Anthony C. Robinson

Organizations

  • Engineer Research and Development Center

Tags

DTIC Thesaurus Topics

  • Computational Science
  • Computer Graphics
  • Data Analysis
  • Data Visualization
  • Dimensionality Reduction
  • Geographic Information Systems
  • Geography
  • Graphics
  • Information Processing
  • Information Retrieval
  • Information Science
  • Information Systems
  • Social Media
  • Software Development
  • Text Analytics
  • User Interface
  • Visualizations

Fields of Study

  • Computer science

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Neural Network Machine Learning.
  • Systems Analysis and Design