Design Space of Origin‐Destination Data Visualization

Abstract

Visualization is an essential tool for observing and analyzing origin‐destination (OD) data, which encodes flows between geographic locations, e.g., in applications concerning commuting, migration, and transport of goods. However, depicting OD data often encounter issues of cluttering and occlusion. To address these issues, many visual designs feature data abstraction and visual abstraction, such as node aggregation and edge bundling, resulting in information loss. The recent theoretical and empirical developments in visualization have substantiated the merits of such abstraction, while confirming that viewers' knowledge can alleviate the negative impact due to information loss. It is thus desirable to map out different ways of losing and adding information in origin‐destination data visualization (ODDV). We therefore formulate a new design space of ODDV based on the categorization of informative operations on OD data in data abstraction and visual abstraction. We apply this design space to existing ODDV methods, outline strategies for exploring the design space, and suggest ideas for further exploration.

A Bounded Measure for Estimating the Benefit of Visualization (Part I): Theoretical Discourse and Conceptual Evaluation

“…It offered a theoretical explanation of “visual abstraction” [ 39 ]. It provided a theoretical basis to a design space that was structured according to different ways of “losing information” in origin-destination data visualization [ 40 ]. The work reported in this paper continues the path of theoretical developments in visualization [ 41 ], and is intended to improve the original cost–benefit formula [ 1 ], in order to make it a more intuitive and usable measurement in practical visualization applications.…”

A Short Introduction to Information-Theoretic Cost-Benefit Analysis

“…However, as soon as one becomes accustomed to thinking in alphabets (including their PMFs), one feels liberated, a bit like the feeling when one first realizes being able to swim or ride a bike. This section contains some text extracted from an appendix of a recent paper on origin-destination data visualization (ODDV) [4], where we described some ODDV phenomena using the information-theoretic term of alphabet.…”

“…The principle design criteria are to reduce the potential distortions by maximizing the use of viewers' knowledge, reduce the costs of other human-and machine-processes that handle the data following the information loss, and reduce the negative impact on such processes. In their paper [4], Tennekes and Chen outline a design space categorized based primarily on the notions of what and how. It focuses on different ways of alphabet compression as highlighted in Figure 3, which also show that the commonly-adopted wisdom of "knowing the users and tasks" is also supported by the information-theoretic reasoning.…”

“…Acknowledgement. I would like to thank Amos Golan for our joint work on the cost-benefit ratio [1] and my co-authors of the follow-on papers who have proofread the relevant explanatory texts in the papers and appendices, including David Ebert [2], Kelly Gaither and Nigel W. John [3], Martijn Tennekes [4], Mateu Sbert [5], and Alfie Abdul-Rahman [6]. I am also grateful to the comments and revision suggestions made by anonymous reviewers.…”

“…When several pieces of follow-on work were submitted for review, reviewers often asked for more explanation of the cost-benefit ratio. I have written appendices to accompany a number of submissions (e.g., [2,3,4,5,6]). This introduction was compiled based on these appendices.…”