Information visualization for selection in Design by Shopping

Abstract

Abstract. In Design by Shopping, designers explore the design space to gain an insight into trades and feasible and impractical solutions, as well as to learn about alternatives before optimization and selection. The design space consists of multidimensional sets of data and, in order to select the best design from amongst numerous alternatives, designers may use several different graphs. In this study, we test to find the most appropriate graph to indicate the best solution corresponding to a set of objectives represented by a design scenario (1). A further constraint is that this must be done in the shortest possible time (2). Three graph types are tested in three different design scenarios where one car has to be chosen from a total of 40. A response quality index is proposed which computes the quality of a designer's choice for any given scenario. In total, 90 tests with 30 participants were performed. The Parallel Coordinates Plot proved to be the best graph for selection in Design by Shopping.

Interactive Optimization With Parallel Coordinates: Exploring Multidimensional Spaces for Decision Support

“…Introduced by Inselberg (1985) and extensively described by Inselberg (1997Inselberg ( , 2009, parallel coordinates are similar to radar charts, except the dimensions are displayed as vertical side-byside axes instead of radially. This allows the method to scale well to many dimensions, and facilitates the comparison of values and identification of tradeoffs, trends and clusters in the data (Shenfield et al, 2007;Akle et al, 2017). Data points are depicted as polygonal lines (or polylines), which intersect the axes at their corresponding values.…”

“…The main drawbacks of parallel coordinates include cluttering of the chart when displaying many alternatives, and the impossibility to visualize all pairwise relationships between dimensions in a single chart (Heinrich and Weiskopf, 2013;Johansson and Forsell, 2016). Studies also emphasize the need for users to receive basic training to better harness parallel coordinates (Shneiderman, 1996;Wolf et al, 2009;Johansson and Forsell, 2016;Akle et al, 2017). The recent developments of interactive data visualizations have greatly alleviated these limitations by allowing the user to filter the displayed solutions, reorder axes by dragging them to explore specific pairwise relationships, or change the visual aspect of lines such as color or opacity to reveal patterns across all dimensions (Bostock et al, 2011;Fieldsend, 2016).…”

“…Several studies investigated the practical applicability of parallel coordinates in the context of multiobjective optimization. Akle et al (2017) studied their effectiveness in comparison to radar charts and combined tables for the balancing of multiple criteria and selection of preferred solutions. They found parallel coordinates to be the most effective and engaging approach for exploration, requiring less cognitive load and stress than the other charts.…”

“…Given the widespread attention received by parallel coordinates, their adoption in the context of multiobjective optimization is not surprising. However, their use remains predominantly confined to a posteriori exploration of precalculated solutions (Bagajewicz and Cabrera, 2003;Xiao et al, 2007;Kipouros et al, 2008;Franken, 2009;Raphael, 2011;Rosenberg, 2012;Miettinen, 2014;Ashour and Kolarevic, 2015;Fieldsend, 2016;Akle et al, 2017;Bandaru et al, 2017a). Only a few studies suggested using parallel coordinates to steer the optimization procedures, but all adopt meta-heuristic approaches, limiting their applicability to smaller problems with few variables (Fleming et al, 2005;Stump et al, 2009;Sato et al, 2015;Hernández Gómez et al, 2016).…”

“…Among these, parallel coordinates increasingly stand out among the most efficient approaches. They are known for their intuitive representations (Packham et al, 2005;Akle et al, 2017), as well as for occupying the least amount of space per criterion, making them highly scalable to many criteria (Fleming et al, 2005). Despite these strengths, like other visualizations, parallel coordinates also suffer from a lack of readability when displaying many solutions, and the difficulty to view all pairwise relationships in a single chart.…”