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Alan M MacEachren

Publications and source records attributed to Alan M MacEachren.

3 recordsLinked to original sources

Visualization for constructing and sharing geo-scientific concepts.

Representations of scientific knowledge must reflect the dynamic nature of knowledge construction and the evolving networks of relations between scientific concepts. In this article, we describe initial work toward dynamic, visual methods and tools that support the construction, communication, revision, and application of scientific knowledge. Specifically, we focus on tools to capture and explore the concepts that underlie collaborative science activities, with examples drawn from the domain of human-environment interaction. These tools help individual researchers describe the process of knowledge construction while enabling teams of collaborators to synthesize common concepts. Our visualization approach links geographic visualization techniques with concept-mapping tools and allows the knowledge structures that result to be shared through a Web portal that helps scientists work collectively to advance their understanding. Our integration of geovisualization and knowledge representation methods emphasizes the process through which abstract concepts can be contextualized by the data, methods, people, and perspectives that produced them. This contextualization is a critical component of a knowledge structure, without which much of the meaning that guides the sharing of concepts is lost. By using the tools we describe here, human-environment scientists are given a visual means to build concepts from data (individually and collectively) and to connect these concepts to each other at appropriate levels of abstraction.

Environment↗

A visualization system for space-time and multivariate patterns (VIS-STAMP).

The research reported here integrates computational, visual, and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial, and temporal dimensions via clustering, sorting, and visualization. Specifically, the approach involves a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices (including several ordering methods), a geographic small multiple display, and a 2-dimensional cartographic color design method. The coupling among these methods leverages their independent strengths and facilitates a visual exploration of patterns that are difficult to discover otherwise. The visualization system we developed supports overview of complex patterns and, through a variety of interactions, enables users to focus on specific patterns and examine detailed views. We demonstrate the system with an application to the IEEE InfoVis 2005 Contest data set, which contains time-varying, geographically referenced, and multivariate data for technology companies in the US.

Algorithms↗