Academic journal article Cartography and Geographic Information Science

Supporting the Process of Exploring and Interpreting Space-Time Multivariate Patterns: The Visual Inquiry Toolkit

Academic journal article Cartography and Geographic Information Science

Supporting the Process of Exploring and Interpreting Space-Time Multivariate Patterns: The Visual Inquiry Toolkit

Article excerpt

Introduction

Exploring and analyzing large space-time-attribute data sets is challenging due to data complexity (i.e., potential interactions among space, time, and attributes) and tool scalability issues (i.e., the challenge of coping with both data volume and high dimension). In this paper, space-time-attribute refers to geographically referenced, time-varying data involving multiple thematic attributes; the focus of methods and tools described is on identifying and interpreting spatio-temporal, multivariate patterns in these data. Existing approaches to pattern identification and interpretation, from entirely computational to visually led methods, are limited in analyzing complex patterns that include space, time, and attribute components together. Moreover, traditional information visualization methods do not support analysis of large data sets. Pattern recognition, machine learning, and other computational methods have been developed explicitly to deal with large and high-dimensional data sets, but typically do not provide ways to incorporate both space and time, nor do they leverage the power of human vision and cognition to help analysts notice and quickly interpret patterns in complex data. The goal of this research is to bridge this gap by developing analytic methods that couple visual, computational methods and human expertise in productive ways. The approach presented here was developed within the broad research framework provided by visual analytics, defined as "the science of analytical reasoning facilitated by interactive visual interfaces" (Thomas and Cook 2005, p. 4).

This research introduces a Visual Inquiry Toolkit (VIT) which provides information analysts with a flexible interface to integrated visual, computational, and cartographic methods that support an overview +detail strategy for identifying and interpreting patterns in space-time-attribute datasets of relatively large size. Overview +detail describes a strategy for supporting multiple levels of detail in an interactive visual display (Plaisant et al. 1995). This strategy is best known through Shneiderman's (1996) information-seeking mantra: overview first, zoom and filter, with details on demand. We propose adding a step to Shneiderman's mantra--information synthesis, which refers to capturing novel, relevant patterns and reorganize them to yield more useful information. Beyond support for the extended visual overview +detail strategy, the VIT also emphasizes flexible interaction strategies designed to enable human knowledge and judgment to be coupled productively with computational pattern-finding methods to support an iterative analysis process.

The remainder of the paper is organized as follows. In the next section, we review related literature. Following that, we discuss our strategy and methodologies, with a focus on representation issues; then, we demonstrate an interactive visual analytics approach for identifying and interpreting spatio-temporal multivariate patterns. Finally, the advantages and limitations of the approach and possible further work are discussed.

Related Work

A starting point for our approach is past work on visualizing multivariate data. The commonly used data representations for multivariate visualization include tables and scatter plots; more sophisticated methods include scatterplot matrices (Andrews 1972), parallel coordinate plots (Inselberg 1985), matrix permutation (Makinen and Siirtola 2000; Bertin 1981), and multivariate glyphs (Pickett et al. 1995). A comprehensive review of the methods can be found in a paper by Keim et al. (2005). All of these methods, however, have difficulty representing large data sets. As the number of data items/variables goes up, the potential for over-plotting on displays goes up as well. Two major solutions have been proposed to address this problem. One is to reduce the data size being displayed by grouping individual data records into subsets (e. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.