MapSets: Visualizing Embedded and Clustered Graphs

In geographic data analysis, one is often given point data of different categories (such as facilities of a university categorized by department). Drawing upon recent research on set visualization, we want to visualize category membership by connecting points of the same category with visual links. Existing approaches that follow this path usually insist on connecting all members of a category, which may lead to many crossings and visual clutter. We propose an approach that avoids crossings between connections of different categories completely. Instead of connecting all data points of the same category, we subdivide categories into smaller, local clusters where needed. We do a case study comparing the legibility of drawings produced by our approach and those by existing approaches. In our problem formulation, we are additionally given a graph G on the data points whose edges express some sort of proximity. Our aim is to find a subgraph G′ of G with the following properties: (i) edges connect only data points of the same category, (ii) no two edges cross, and (iii) the number of connected components (clusters) is minimized. We then visualize the clusters in G′. For arbitrary graphs, the resulting optimization problem, Cluster Minimization, is NP‐hard (even to approximate). Therefore, we introduce two heuristics. We do an extensive benchmark test on real‐world data. Comparisons with exact solutions indicate that our heuristics do astonishing well for certain relative‐neighborhood graphs.

Section: Related Workmentioning

confidence: 99%

ClusterSets: Optimizing Planar Clusters in Categorical Point Data

Geiger

Cornelsen

Haunert

et al. 2021

“…The GMap approach [GHK10, HGK10, HKV14] creates a map of contours that are adjacent to each other using a Voronoi tessellation. In contrast, the contours within MapSets [EHKP14] are generated based on noncrossing spanning trees of points belonging to the same cluster. The trees can be grown to contiguous non-overlapping regions that are optimized with respect to their convexity.…”

Section: Contour Overlaymentioning

confidence: 99%

Visualizing Group Structures in Graphs: A Survey

Vehlow

Beck

Weiskopf

2016

Graph visualizations encode relationships between objects. Abstracting the objects into group structures provides an overview of the data. Groups can be disjoint or overlapping, and might be organized hierarchically. However, the underlying graph still needs to be represented for analyzing the data in more depth. This work surveys research in visualizing group structures as part of graph diagrams. A particular focus is the explicit visual encoding of groups, rather than only using graph layout to indicate groups implicitly. We introduce a taxonomy of visualization techniques structuring the field into four main categories: visual node attributes vary properties of the node representation to encode the grouping, juxtaposed approaches use two separate visualizations, superimposed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. The derived taxonomies for group structure and visualization types are also applied to group visualizations of edges. We survey group-only, group-node, group-edge and group-network tasks that are described in the literature as use cases of group visualizations. We discuss results from evaluations of existing visualization techniques as well as main areas of application. Finally, we report future challenges based on interviews we conducted with leading researchers of the field.

“…In map-based diagrams, the visualization is enriched by enclosing in a contiguous region elements that belong to the same set; see Figure 1-Right. This is the output of several recent InfoVis techniques which visualize sets, groups, and clusters: BubbleSets [CPC09], LineSets [ARRC11], GMap [GHK10], KelpFusion [MRS * 13], and MapSets [EHKP14].…”

Section: Visualization Number Of Tasks Phasementioning

confidence: 99%

“…More recently several fully automated tools were developed to generate map-based visualizations for non-spatial data. This is the output of several recent InfoVis techniques which visualize sets, groups, and clusters: BubbleSets [CPC09], LineSets [ARRC11], GMap [GHK10], KelpFusion [MRS * 13], and MapSets [EHKP14]. This is the output of several recent InfoVis techniques which visualize sets, groups, and clusters: BubbleSets [CPC09], LineSets [ARRC11], GMap [GHK10], KelpFusion [MRS * 13], and MapSets [EHKP14].…”

Section: Introductionmentioning

confidence: 99%

Map‐based Visualizations Increase Recall Accuracy of Data

Saket

Scheidegger

Kobourov

et al. 2015

Self Cite

Figure 1: We investigate the memorability of relational data represented with node-link (left-side) and map-based (right-side) visualizations; shown are a node-link and a map-based visualization with 200 nodes and 500 links from the LastFM dataset. AbstractWe investigate the memorability of data represented in two different visualization designs. In contrast to recent studies that examine which types of visual information make visualizations memorable, we examine the effect of different visualizations on time and accuracy of recall of the displayed data, minutes and days after interaction with the visualizations. In particular, we describe the results of an evaluation comparing the memorability of two different visualizations of the same relational data: node-link diagrams and map-based visualization. We find significant differences in the accuracy of the tasks performed, and these differences persist days after the original exposure to the visualizations. Specifically, participants in the study recalled the data better when exposed to map-based visualizations as opposed to node-link diagrams. We discuss the scope of the study and its limitations, possible implications, and future directions.