title: Figures
Figures
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The original figures from the book are released here under the Creative Commons Attribution 4.0 International License (CC BY 4.0). When you use a figure for your own work, please, cite the book appropriately, for example, like this: Christian Tominski and Heidrun Schumann. "Interactive Visual Data Analysis". AK Peters Visualization Series, CRC Press, 2020.
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Figure Overview
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Figure Details
Chapter 1
Figure
Original book figure.
Node-link diagram visualizing a graph's structure and attributes. (a) Plain structure.
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Original book figure.
Node-link diagram visualizing a graph's structure and attributes. (b) Encoding degree via color.
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Node-link diagram visualizing a graph's structure and attributes. (c) Encoding degree via color and size.
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Node-link diagram visualizing a graph's structure and attributes. (d) Encoding weight via line width.
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Dynamic filtering to focus on relevant parts of a climate network. (a) Full graph with 6,816 nodes and 116,470 edges.
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Dynamic filtering to focus on relevant parts of a climate network. (b) Filtered graph with 938 nodes and 5,324 edges.
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Multiple-views visualization of a climate network.
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Guidance provides assistance during data navigation. (a) Where should I go next?
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Guidance provides assistance during data navigation. (b) Visual cues hint at candidates!
Chapter 2
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Visualization of life satisfaction in Germany. (a) Failing visual representation.
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Visualization of life satisfaction in Germany. (b) Succeeding visual representation.
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Functional dependency between the reference space and the attribute space. For a point in the reference space, there is exactly one point in the attribute space.
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Key terms for characterizing data.
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The scope defines to which extent an observation is valid. (a) Global scope. (b) Local scope. (c) Point scope.
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Visualizing the local scope of measurements of water quality. (a) Data points only.
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Visualizing the local scope of measurements of water quality. (b) Voronoi partitioning.
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Visualizing the local scope of measurements of water quality. (c) Shepard interpolation.
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Meta-data to characterize the data to be analyzed.
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Four-set Venn diagram illustrating different classes of data.
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Different visual encodings to support different tasks. (a) Coloring suited to identifying values.
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Different visual encodings to support different tasks. (b) Coloring suited to locating extrema.
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A target as defined by projection and selection.
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Goals, questions, targets, and means characterize analysis tasks.
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Meteorological measurements over the course of the year. (a) Hours of sunshine.
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Meteorological measurements over the course of the year. (b) Air temperature.
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Meteorological measurements over the course of the year. (c) Cloud cover.
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Histograms of the distribution of cloud cover values. (a) Cloud cover value frequencies in Rostock.
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Histograms of the distribution of cloud cover values. (b) Cloud cover value frequencies in Dresden.
Figure
Original book figure. Adapted from Munzner (2009).
Four nested levels outline how to design interactive visual data analysis solutions.
Figure
Original book figure. Adapted from Chi (2000).
Data-oriented and graphics-oriented stages and operators.
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A network of operators describes the data's transformation through several stages from data values to image data.
Chapter 3
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Illustration of the effect of different visual representations. (a) Line plot.
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Illustration of the effect of different visual representations. (b) Spiral plot (cycle length 32 days).
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Illustration of the effect of different visual representations. (c) Spiral plot (cycle length 28 days).
Figure
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Visual variables.
Figure
Original book figure. Adapted from Mackinlay and Winslow (2014).
Effectiveness ranking of visual variables.
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Color maps for identifying and locating values and classes.
Figure
Original book figure. Adapted from bl.ocks.org/mbostock/4063318.
Applying the color maps from Figure 3.4 to temperature data. (a) Color coding for identification tasks.
Figure
Original book figure. Adapted from bl.ocks.org/mbostock/4063318.
Applying the color maps from Figure 3.4 to temperature data. (b) Color coding for location tasks.
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Basic mapping of a data variable onto a visual variable.
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Enhanced data-dependent visual mapping. (a) Value range expansion. (b) Logarithmic mapping. (c) Box-Whisker mapping.
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Combined color map for comparing two data variables.
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Two-tone visualization of 20 years of daily temperatures.
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Visual encoding via position, area, color, and shape. (a) Mapping to position.
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Visual encoding via position, area, color, and shape. (b) Mapping to area.
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Visual encoding via position, area, color, and shape. (c) Mapping to color.
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Visual encoding via position, area, color, and shape. (d) Mapping to shape.
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Illustration of focus+context for a table-based visualization of the Iris flower dataset. Focused rows are magnified to accommodate labels. (a) Regular visualization.
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Illustration of focus+context for a table-based visualization of the Iris flower dataset. Focused rows are magnified to accommodate labels. (b) Focus+context distortion of rows.
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Multiple coordinated views for analyzing multivariate data.
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Two-tone colored table-based visualization of the Cars dataset.
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Table Lens with textual labels for focused data tuples.
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9 × 9 scatter plot matrix of meteorological data. Color is used to ease the recognition of data variables.
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Visualization with polylines across parallel and star-shaped axes. (a) Parallel coordinates plot. (b) Radar chart.
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Visual patterns between pairs of parallel axes.
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The same data as in Figure 3.19 visualized as scatter plots.
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Parallel coordinates with histograms showing demographic data.
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Examples of classic glyphs for visualization. (a) Autoglyph. (b) Stick figures. (c) Chernoff faces.
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Corn glyph for representing six ordinal data values.
Figure
By Thomas Nocke. Licensed under CC BY.
Pixel-based visualization of daily values of six meteorological attributes collected for more than hundred years in the city of Potsdam.
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Mosaic plot visualizing the survival of passengers of the Titanic.
Figure
Original book figure. Adapted from Allen (1983).
Temporal relations for time instants and time intervals. (a) Instant relations. (b) Interval relations.
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Aspects of time to be considered when visualizing temporal data.
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Small multiples visualization of the number of people diagnosed with problems of the upper respiratory tract.
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Time Wheel visualization of human health data.
Figure
Original book figure. Adapted from Qiang et al. (2012).
Visual representation of intervals using the triangular model. (a) Standard interval representation. (b) Intervals in the triangular model.
Figure
Original book figure. Adapted from bl.ocks.org/mbostock/4060954.
Stream graph with randomly generated data.
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Spiral display with four years of daily temperatures in Rostock.
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Comparison of a regular line plot (top) and a cycle plot (bottom).
Figure
Original book figure. Adapted from Aigner et al. (2005).
Visualization of uncertain time intervals for planning purposes.
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The TimeViz Browser provides an illustrated overview of more than a hundred techniques for visualizing time and temporal data.
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Spatial regions at different scales: Federal state, districts, zip-code regions.
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Geo-spatial data can refer to different spatial units. (a) Points. (b) Lines. (c) Areas. (d) Volumes.
Figure
Original book figure. Adapted from bl.ocks.org/mbostock/3757119.
Different map projections preserve different spatial properties. (a) Equirectangular.
Figure
Original book figure. Adapted from bl.ocks.org/mbostock/3757132.
Different map projections preserve different spatial properties. (b) Mercator.
Figure
Original book figure. Adapted from bl.ocks.org/mbostock/4479477.
Different map projections preserve different spatial properties. (c) Natural Earth.
Figure
Original book figure. Adapted with permission by Nicolas Belmonte from philogb.github.io/page/myriahedral/.
Myriahedral projections of the Earth.
Figure
Original book figure. Generated with mapshaper.org.
Map representation at different resolutions. (a) Original data 100%.
Figure
Original book figure. Generated with mapshaper.org.
Map representation at different resolutions. (b) Data reduced to 50%.
Figure
Original book figure. Generated with mapshaper.org.
Map representation at different resolutions. (c) Data reduced to 10%.
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Reducing overlap of stream graph glyphs on a map. (a) Straightforward placement.
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Reducing overlap of stream graph glyphs on a map. (b) Overlap-optimized placement.
Figure
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Indirect visualization of geo-spatial data. (a) Univariate choropleth map plus multivariate parallel coordinates plot.
Figure
By Thomas Butkiewicz. Licensed under CC BY.
Indirect visualization of geo-spatial data. (b) Flexible visualization via probes.
Figure
Original book figure. Adapted from Dübel et al. (2014).
Systematic view of 2D and 3D representations of geo-spatial data and geographic space.
Figure
By Steve Dübel. Licensed under CC BY.
3D visualization of the trajectory of an aircraft approaching Sion airport.
Figure
By Martin Röhlig. Licensed under CC BY.
Visibility widgets help users identify obscured information in 3D geo-visualizations.
Figure
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Visualization of movement trajectories. (a) 2D map with 2D paths. (b) 2D map with stacked 3D bands.
Figure
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Visualizing spatio-temporal data using 3D glyphs on a 2D map. (a) Pencil glyphs for linear trends.
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Visualizing spatio-temporal data using 3D glyphs on a 2D map. (b) Helix glyphs for cyclic patterns.
Figure
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Creation of a non-planar 3D slice through space-time. (a) Topological path. (b) Geometrical path. (c) Extruded slice.
Figure
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Spatial-temporal visualization along a wall on a map.
Figure
Original book figure. Adapted from Hadlak et al. (2015).
Facets to be considered when visualizing graphs. (a) Structure (b) Attributes (c) Time (d) Space (e) Groups
Figure
Original book figure. Created with gephi.org.
Node-link diagram of flights connecting US airports.
Figure
Original book figure. Adapted from van Ham et al. (2009).
Node-link diagram and corresponding matrix representation.
Figure
Original book figure. Adapted from Shen and Ma (2007).
Graph patterns represented as matrices and node-link diagrams.
Figure
Original book figure. Adapted from bost.ocks.org/mike/miserables/.
Differently ordered matrix representations of the same data. (a) Ordered by name.
Figure
Original book figure. Adapted from bost.ocks.org/mike/miserables/.
Differently ordered matrix representations of the same data. (b) Ordered by frequency.
Figure
Original book figure. Adapted from bost.ocks.org/mike/miserables/.
Differently ordered matrix representations of the same data. (c) Ordered by community.
Figure
Original book figure. Adapted from Schulz et al. (2011).
Node-link representation compared to implicit representations. (a) Node-link. (b) Inclusion. (c) Overlap. (d) Adjacency.
Figure
Original book figure. Software courtesy of Steffen Hadlak.
Implicit visualizations of a classification hierarchy. (a) Squarified Treemap. (b) Information pyramids. (c) 3D sunburst.
Figure
Original book figure. Adapted with permission by Jean-Daniel Fekete from www.aviz.fr/Research/Nodetrix.
NodeTrix visualization of a co-author network.
Figure
Original book figure. Adapted from Hadlak et al. (2015).
Spatial composition of graph facets in a single representation. (a) Juxtaposition. (b) Superimposition. (c) Nesting.
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Map with tree layouts embedded into selected regions.
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Three map layers visualize the data of three consecutive time steps. Spikes and lines indicate differences between the layers.
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Multiple coordinated views for multi-faced graph visualization.
Chapter 4
Figure
Original book figure. Adapted from Cooper et al. (2007).
Conceptual separation across different models.
Figure
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Spatial separation between the graphical user interface (right) and the visual representation in the main view (center).
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Model-view-controller pattern.
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Rubberband selection for marking multiple data elements. (a) Selection by inclusion.
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Rubberband selection for marking multiple data elements. (b) Selection by intersection.
Figure
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Four steps of selecting multiple trajectories using modifier keys.
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Selecting segments based on their color, which represents speed. (a) How to select slow-speed segments?
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Selecting segments based on their color, which represents speed. (b) Select via an interactive legend!
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Selecting nodes based on their data attributes. (a) How to select high-degree nodes?
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Selecting nodes based on their data attributes. (b) Select via slider handles!
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Strategies for visual emphasis of relevant data and attenuation of less-relevant data.
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Visual feedback for selections in visual representations of graphs. (a) Original visual representation.
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Visual feedback for selections in visual representations of graphs. (b) Highlighting by encircling nodes.
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Visual feedback for selections in visual representations of graphs. (c) Dimming nodes and edges.
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Visual feedback for selections in visual representations of graphs. (d) Filtering nodes and edges.
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Brushing a range (red) of an axis for binary and fuzzy selection. (a) Binary selection in the range.
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Brushing a range (red) of an axis for binary and fuzzy selection. (b) Fuzzy selection beyond the range.
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Brushing & linking in a multiple-views graph visualization.
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Using V^= to scale relevant data to fit the display space.
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Illustration of the conceptual model of zoomable interfaces.
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Geometric zooming of a node-link visualization.
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Semantically enhanced zooming of a node-link visualization.
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A zoomable graph visualization and its controls.
Figure
Original book figure. Adapted from Gladisch et al. (2013).
Visual cues for pointing to off-screen data.
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Bring & go with radar view and proxy nodes.
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Viewports during an animated transition.
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Snapshots of the viewport animation outlined in Figure 4.21.
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A range slider controls the time period mapped to a spiral visualization of the daily average temperature for the city of Rostock.
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Adjusting a time period at different input scales. (a) Regular range slider with global scale.
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Adjusting a time period at different input scales. (b) A slider with increased precision is dynamically added to the range slider.
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Integrated sliders for nD pan and zoom in the TimeWheel. (a) Plain non-interactive axes.
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Integrated sliders for nD pan and zoom in the TimeWheel. (b) Axes with integrated sliders.
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Integrated range slider for per-axis pan and zoom.
Figure
Original book figure. Adapted from Tominski et al. (2017).
Model of a lens pipeline attached to a standard visualization.
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Fundamental effects of lens functions. (a) Alteration.
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Fundamental effects of lens functions. (b) Suppression.
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Fundamental effects of lens functions. (c) Enrichment.
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Lenses with different shapes and orientation. (a) Circular.
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Lenses with different shapes and orientation. (b) Rectangular orientable.
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Lenses with different shapes and orientation. (c) Content-adaptive shape.
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Direct manipulation of lenses. (a) Move and resize.
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Direct manipulation of lenses. (b) Adjust parameters.
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Magnifying details in a map visualization with a fish-eye lens. (a) Regular map visualization.
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Magnifying details in a map visualization with a fish-eye lens. (b) Details magnified with a fish-eye lens.
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Graph lenses for exploring structural relationships. (a) Node-link diagram without lens.
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Original book figure.
Graph lenses for exploring structural relationships. (b) Local-edge lens.
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Graph lenses for exploring structural relationships. (c) Bring-neighbors lens.
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Graph lenses for exploring structural relationships. (d) Composite lens.
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A lens to query temporal characteristics of movement data.
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Original book figure.
Orthogonal node-link diagram of a biological network.
Figure
Original book figure. Adapted from Gladisch et al. (2014).
Editing using the edit lens. (a) Place lens to insert.
Figure
Original book figure. Adapted from Gladisch et al. (2014).
Editing using the edit lens. (b) Adjust lens to update.
Figure
Original book figure. Adapted from Gladisch et al. (2014).
Editing using the edit lens. (c) Flick lens to delete.
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Original book figure.
Visual designs for comparison tasks.
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Original book figure.
Natural behavior of people comparing information on paper. (a) Side-by-side.
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Natural behavior of people comparing information on paper. (b) Shine-through.
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Natural behavior of people comparing information on paper. (c) Folding.
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Original book figure.
Creating sub-views for comparison. A red frame indicates where the left sub-view has been detached from the main view.
Figure
Original book figure. Adapted from Tominski et al. (2012).
Overview of natural interaction techniques for visual comparison.
Figure
Original book figure. Adapted from Tominski et al. (2012).
Information-rich, natural, and occlusion-free folding styles.
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Original book figure.
Relocating selected regions to form a ring for easier comparison. The map background has been desaturated for the purpose of illustration.
Figure
Original book figure.
Visualizing future appointments with a SpiraClock.
Figure
Original book figure. Adapted from Spindler et al. (2010).
Extended interaction with tangible views.
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Original book figure.
A circular tangible lens for magnification purposes.
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Original book figure.
Comparing matrix data with two tangible views.
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Original book figure.
Tangible views for different visualizations. (a) Parallel coordinates.
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Original book figure.
Tangible views for different visualizations. (b) Node-link diagram.
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Original book figure.
Tangible views for different visualizations. (c) Space-time cube.
Figure
Original book figure. Adapted from Lehmann et al. (2011).
Interacting by physical movements. (a) Zones for global control.
Figure
Original book figure. Adapted from Lehmann et al. (2011).
Interacting by physical movements. (b) Gaze plus lens for local control.
Chapter 5
Figure
Original book figure.
Procedure of determining visual density in parallel coordinates. (a) Binned axes. (b) Bin map. (c) Categorization. (d) Drawing.
Figure
By Helwig Hauser. Licensed under CC BY.
User-selected data in red compared against general trends in green.
Figure
Original book figure. Adapted from bl.ocks.org/mbostock/4341134.
Visualization of dependencies in a software class hierarchy. (a) Conventional representation.
Figure
Original book figure. Adapted from bl.ocks.org/mbostock/4341134.
Visualization of dependencies in a software class hierarchy. (b) Hierarchical edge bundling (Holten, 2006).
Figure
Original book figure.
Illustration of Furnas' DoI function. (a) Distances to focus node. (b) Adding node levels. (c) Extracted subtrees.
Figure
By Steffen Hadlak. Licensed under CC BY.
Five years of a dynamic co-author network extracted from DBLP.
Figure
Original book figure. Adapted from Abello et al. (2014).
Glyph design for representing collapsed subgraphs.
Figure
By Christian Eichner. Licensed under CC BY.
Feature specification with an interactive interface. (a) Specification of thresholds.
Figure
By Christian Eichner. Licensed under CC BY.
Feature specification with an interactive interface. (b) Formal feature definition.
Figure
By Christian Eichner. Licensed under CC BY.
Comparison of direct volume visualization of the particle concentration of one protein and ellipsoid-based visualization of features representing high concentrations of two different proteins. (a) Volume visualization.
Figure
By Christian Eichner. Licensed under CC BY.
Comparison of direct volume visualization of the particle concentration of one protein and ellipsoid-based visualization of features representing high concentrations of two different proteins. (b) Ellipsoid visualization.
Figure
By Christian Eichner. Licensed under CC BY.
Visualizing the temporal evolution of features. (a) Node-link diagram of the event graph.
Figure
By Christian Eichner. Licensed under CC BY.
Visualizing the temporal evolution of features. (b) Two features at two time steps.
Figure
By Martin Röhlig. Licensed under CC BY.
Drawing thousands of trajectories of chaotic movements for multiple simulations leads to cluttered and indecipherable visual representations.
Figure
By Martin Röhlig. Licensed under CC BY.
From entities (dot marks) to density map (gray-scale image) to regions (colored image).
Figure
Original book figure.
2D movement reduced to 1D time series of feature values.
Figure
Original book figure.
Feature visualization with overview and detail views.
Figure
By Martin Röhlig. Licensed under CC BY.
Visualization of parameter settings, feature values, and detail information for selected parts of the data. (a) Parameter settings as gray-scale matrix; (b) Feature values over time as color-coded matrix; (c) Chart with selected time series; (d) Trajectory view with selected trajectory segments.
Figure
By Martin Röhlig. Licensed under CC BY.
Visualizing the parameter dependency of average group size.
Figure
By Martin Röhlig. Licensed under CC BY.
Visualization of the average distance to free proteins reveals the sweeping effect. (a) Density map. (b) Average protein-raft distance.
Figure
By Martin Luboschik. Licensed under CC BY.
Visualization of a time series with more than 1.7 million time points, where each black pixel represents about 1,000 data points.
Figure
Original book figure.
One and the same time series at two different scales.
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Original book figure.
Unifying the sample points of two successive scales by mapping and interpolation.
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Original book figure.
Computing the absolute value difference (AVD) and the slope sign difference (SSD) between two successive data scales.
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Original book figure.
Aggregation of data differences with maximum aggregation for the absolute value difference (AVD) and average aggregation for the slope sign difference (SSD) function.
Figure
Original book figure.
Visualizing aggregated differences along with the actual data.
Figure
By Martin Luboschik. Licensed under CC BY.
Time series plot of the simulation outcome and corresponding multi-scale difference bands of SSD and AVD with local color mode.
Figure
By Martin Luboschik. Licensed under CC BY.
Studying the details of the middle peak-notch-spike pattern from Figure 5.28.
Figure
Original book figure.
A decision tree classifying enterprises according to their sales.
Figure
Original book figure. Adapted from Röhlig et al. (2015).
Illustration of activity recognition based on parameter-dependent algorithms that learn from some ground truth.
Figure
By Martin Röhlig. Licensed under CC BY.
Visualization of parameter-dependent classification outcomes for activity recognition. (a) Parameter configurations; (b) Recognized activities; (c) Color legend; (d) Ground truth; (e) and (f) Stacked histograms with aggregated information.
Figure
By Martin Röhlig. Licensed under CC BY.
Highlighting the incorrectly classified time steps in red.
Figure
Original book figure. Adapted from Glaßer (2014) with permission of Sylvia Saalfeld.
Illustration of clustering strategies. (a) Input data. (b) K-means. (c) Ward's method. (d) STING. (e) DBSCAN. (f) Dendrogram.
Figure
Original book figure. Software courtesy of Alexander Lex.
Comparison of clusters generated with hierarchical clustering, k-means, and affinity propagation. (a) Hierarchical clustering. (b) K-means clustering. (c) Affinity propagation.
Figure
Original book figure.
Applying hybrid SOM-based clustering to sort rows in a table lens visualization. (a) Unordered rows before clustering.
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Original book figure.
Applying hybrid SOM-based clustering to sort rows in a table lens visualization. (b) Ordered rows after clustering.
Figure
Original book figure. Adapted from Hadlak (2014).
Two-step procedure of clustering nodes based on their attributes. First, nodes with similar attribute behavior are grouped. Second, groups are refined based on connected components. (a) Initial grouping. (b) Refined clusters.
Figure
Original book figure. Adapted from Hadlak (2014).
Structure-based clustering. (a) Initial set of states and transitions based on the sequence of graphs Gi in DG; (b) Hierarchical grouping of states and transitions based on similar structures.
Figure
By Steffen Hadlak. Licensed under CC BY.
Example of a state-transition graph characterizing an underlying dynamic graph.
Figure
By Steffen Hadlak. Licensed under CC BY.
Analyzing a wireless network supported by structure-based clustering. (a) State-transition graph; (b) Average link quality of selected state; (c) Representative graph structure of selected state.
Figure
Original book figure.
Reducing dimensionality with principal component analysis. (a) Original data space. (b) Principal component space. (c) Reduced space.
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Original book figure.
Overview of automatic computational methods to support interactive visual data analysis by reducing the complexity of the data and their visual representations.
Chapter 6
Figure
By Christian Eichner. Licensed under CC BY.
Graphical interface for creating and controlling multi-display visual analysis presentations, including content pool (top), logical presentation structure (middle), and preview (bottom).
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Original book figure.
Basic two-step procedure of the automatic view layout.
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Original book figure.
Changing the content of views by launching visualization software.
Figure
By Christian Eichner. Licensed under CC BY.
Graphical interface for analysis coordination and meta-analysis, including filtering support (top), analysis history graph (middle), and timeline with undo and redo buttons (bottom).
Figure
Original book figure.
A knowledge gap exists when target or path is unknown.
Figure
Original book figure. Adapted from van Wijk (2006).
Adapted variant of van Wijk's model of visualization. Artifacts as boxes: data [D], specifications [S], visualization images [I], and user knowledge [K]. Functions as circles: analytic and visual transformation (T), perception and cognition (P), and interactive exploration (E).
Figure
Original book figure. Adapted from Ceneda et al. (2017).
Conceptual model of guided interactive visual data analysis. *Added artifacts and functions: domain conventions and models [D*], history and provenance [H*], visual cues [C*], options and alternatives [O*], and guidance generation (G*).
Figure
Original book figure.
Navigation recommendations for graph visualization.
Figure
Original book figure. Adapted from Streit et al. (2012).
Tailored domain model as the basis for user guidance.
Figure
Original book figure. Adapted from van Wijk (2006).
Incremental processes highlighted in van Wijk's model of visualization.
Figure
Original book figure. Adapted from Schulz et al. (2016).
Extended notation for operators and transitions for progressive visual data analysis.
Figure
Original book figure. Adapted from Schulz et al. (2016).
Simple example of a progressive transformation pipeline.
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Original book figure.
A multi-threading architecture for progressive visual data analysis.
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Original book figure.
Illustration of asynchronous processing threads operating on data chunks stored in priority queues.
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Original book figure.
Comparison of single-thread and multi-thread solutions. (a) Regular single-thread solution. (b) Progressive multi-thread solution.
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Original book figure.
The three typical scenarios of progressive visual data analysis: progressive data processing, progressive visualization, and progressive display.
Figure
By Marco Angelini. Licensed under CC BY.
Visualization of progressively processed data chunks of car crashes from a database with more than 370,000 entries. (a) Regular progression of chunks.
Figure
By Marco Angelini. Licensed under CC BY.
Visualization of progressively processed data chunks of car crashes from a database with more than 370,000 entries. (b) Prioritized progression of chunks.
Figure
Original book figure.
Progressive force-directed layout of a social network with 747 nodes and 60,050 edges.
Figure
Original book figure.
Progressive visualization of a climate network with about 6,816 nodes and 232,940 edges.
Figure
Original book figure. Adapted from Rosenbaum et al. (2011).
Device-dependent progressive transmission of a treemap visualization image.
Figure
By Axel Radloff-Delosea. Licensed under CC BY.
Progressive display for a dynamically defined region of interest. (a) Global view. (b) Overview+detail view. (c) Focus+context view.
