Design and evaluation of line symbolizations for origin–destination flow maps

Abstract

We present the results of a user study comparing variants of commonly used line symbolizations for directed origin-destination flow maps. Our design and evaluation consisted of five line symbolizations that employ a combination of following visual variables: arrowheads, origin-destination coloring (color hue, and value), line shortening, line width, tapered edges (varying width from wide to narrow, and narrow to wide), and curvature asymmetry and strength. To guide our evaluation, we used a task-by-type typology and chose four representative tasks that are commonly used in flow map reading: identifying dominant direction of flows, flows with the highest magnitude (volume), spatial focusing of long flows toward a destination, and clusters of high netexports (net-outflow). We systematically analyzed user responses and task performance which we measured by task completion time and accuracy. We designed a web-based flow mapping and testing framework and recruited the participants from Amazon Mechanical Turk. To demonstrate the application and user experiment, we used 16 commodity flow data sets in the United States from 2007 and systematically rotated the layouts to evaluate the effect of layout orientation. From this study, we can conclude that there is potential usefulness for all of the five symbolizations we tested; however, the influence of the design on performance and perception depends on the type of the task. Also, we found that data and layout orientation have significant effects on performance and perception of patterns in flow maps which we attribute to the change in visual saliency of node and flow patterns in relation to the way users scan the map. We recommend that the choice of line symbolization should be guided by a task taxonomy which end users are expected to perform. We discuss various design trade-offs and recommendations and potential future work for designing and evaluating line symbolizations for flow mapping.

Visualizing water infrastructure with Sankey maps: a case study of mapping the Los Angeles Aqueduct, California

“…To enhance the overall visual clarity and to match the granularity of the available data, we used a simplified depiction of the hydrology (see Figure 5) by aggregating all the tributary streams feeding Mono Lake and Owens River (see Figure 4) into a few nodes representing each watershed, and clustering the nine Owens Valley wellfields into northern and southern nodes. These design decisions align with research into flow map visual preferences that aggregation of flow paths (Koylu & Guo, 2017;Phan et al, 2005; and clustering of edges in graphs (Cui, Zhou, Qu, Wong, & Li, 2008;Purchase et al, 2012) enhances comprehension.…”

“…Much of the contemporary research into flow maps falls into either legibility/visual preferences studies (Jenny et al, 2016(Jenny et al, , 2017Johnson & Nelson, 1998;Koylu & Guo, 2017), or the development of automated mapping algorithms (Boyandin, Bertini, Bak, & Lalanne, 2011;Buchin, Speckmann, & Verbeek, 2011;Guo, 2009;Phan, Xiao, Yeh, & Hanrahan, 2005;, which are both outside the scope or aims of this paper. Koylu and Guo (2016) attempt to resolve the conflicting findings of prior research in visual preferences and the efficacy of origin/destination flow map design tactics.…”

Automated layout of origin–destination flow maps: U.S. county-to-county migration 2009–2013

“…Tapered line widths have been recommended for indicating direction in non-geographic node-link diagrams (Holten & Van Wijk, 2009a), however, Jenny et al (2016 found that arrows result in better flow map interpretation and are the preferred method for indicating direction amongst cartographers and map users compared to tapered lines. Koylu and Guo (2016) also found that the direction and magnitude of flow lines are faster and more accurate to read when using arrowheads instead of color gradients or only tapered line width.…”

Origin-Destination Flow Maps in Immersive Environments

“…Research in cartography [36,40,53] and network visualisation [27,28] has identified design principles and aesthetic criteria that can reduce clutter in flow maps. Jenny et al [36] compiled the following design principles to declutter 2D maps: curving flows, minimising overlap among flows [47] and between flows and nodes [62], avoiding acuteangle crossings [32], radially distributing flows [31], and stacking small flows on top of large flows [16].…”