Stable Treemaps via Local Moves

Sondag, Max; Speckmann, Bettina; Verbeek, Kevin

doi:10.1109/tvcg.2017.2745140

Cited by 40 publications

(68 citation statements)

References 17 publications

(38 reference statements)

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“…The visual comparison is one of the prevailing evaluation methods for Treemaps, as shown here for Treemap stability approaches [SSV18] Finally, the readability criterion tries to capture how easily a user can to scan a layout to find a particular item. The measure tries to mimic the motion of the reader's eye fixation as the Treemap layout is scanned in order.…”

Section: Figure 22mentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

108

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The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi-and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

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Section: Figure 22mentioning

confidence: 99%

Quality Metrics for Information Visualization

Behrisch

Blumenschein

Kim

et al. 2018

Computer Graphics Forum

108

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“…[4,23]), stable treemap layouts for improved juxtaposition comparisons (e.g. [19,21,44,46,48,52]), and dynamic topology (e.g. [26,29]).…”

Section: Visual Comparisonmentioning

confidence: 99%

Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data

Borland

Wang

Zhang

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Exploratory cohort selection in high-dimensional datasets can lead to selection bias-unintended side-effects in variable distributions-that may go unnoticed by the user. Our selection bias tracking system and detailed cohort comparison visualizations, deployed in a medical temporal event sequence visual analytics tool, include (a) a cohort provenance tree to keep track of created cohorts and indicate when selection bias may have occurred, (b-d) a suite of high-dimensional cohort comparison visualizations that employ hierarchical aggregation to display the differences between two cohorts in detail, and (e) data-type dependent comparisons of individual variable distributions.Abstract-The collection of large, complex datasets has become common across a wide variety of domains. Visual analytics tools increasingly play a key role in exploring and answering complex questions about these large datasets. However, many visualizations are not designed to concurrently visualize the large number of dimensions present in complex datasets (e.g. tens of thousands of distinct codes in an electronic health record system). This fact, combined with the ability of many visual analytics systems to enable rapid, ad-hoc specification of groups, or cohorts, of individuals based on a small subset of visualized dimensions, leads to the possibility of introducing selection bias-when the user creates a cohort based on a specified set of dimensions, differences across many other unseen dimensions may also be introduced. These unintended side effects may result in the cohort no longer being representative of the larger population intended to be studied, which can negatively affect the validity of subsequent analyses. We present techniques for selection bias tracking and visualization that can be incorporated into high-dimensional exploratory visual analytics systems, with a focus on medical data with existing data hierarchies. These techniques include: (1) tree-based cohort provenance and visualization, including a user-specified baseline cohort that all other cohorts are compared against, and visual encoding of cohort "drift", which indicates where selection bias may have occurred, and (2) a set of visualizations, including a novel icicle-plot based visualization, to compare in detail the per-dimension differences between the baseline and a user-specified focus cohort. These techniques are integrated into a medical temporal event sequence visual analytics tool. We present example use cases and report findings from domain expert user interviews.

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“…We measure topological accuracy as the number of lost adjacencies (MADJ) in each of the k computed layouts, normalized by the number of adjacencies k|T |. To measure preservation of relative directions (MREL) with respect to the input map, we use the Relative Position Change Metric [29] which captures the preservation of the spatial mental model (orthogonal order) in a fine-grained way. Each rectangle defines eight zones by extending its sides to infinite lines.…”

Section: Methodsmentioning

confidence: 99%

“…Metrics: stability. We also want to assess stability, or layout similarity, between the DCs by two quality metrics, based on treemap stability metrics [29], interpreting DCs as special treemaps with added whitespace. The first is based on geometric distances between the layouts: the layout distance (SDIS) focuses on the change in position of the squares.…”

Section: Methodsmentioning

confidence: 99%

Computing Stable Demers Cartograms

Nickel

Sondag

Meulemans

et al. 2019

Lecture Notes in Computer Science

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Cartograms are popular for visualizing numerical data for map regions. Maintaining correct adjacencies is a primary quality criterion for cartograms. When there are multiple data values per region (over time or different datasets) shown as animated or juxtaposed cartograms, preserving the viewer's mental-map in terms of stability between cartograms is another important criterion. We present a method to compute stable Demers cartograms, where each region is shown as a square and similar data yield similar cartograms. We enforce orthogonal separation constraints with linear programming, and measure quality in terms of keeping adjacent regions close (cartogram quality) and using similar positions for a region between the different data values (stability). Our method guarantees ability to connect most lost adjacencies with minimal leaders. Experiments show our method yields good quality and stability.

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Stable Treemaps via Local Moves

Cited by 40 publications

References 17 publications

Quality Metrics for Information Visualization

Quality Metrics for Information Visualization

Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data

Computing Stable Demers Cartograms

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