Pierre Dragicevic on Visualizing.JP

Faster Than the 3D Bar Chart on Screen, Short of 2D: Jansen et al.'s Evaluation of Physical Visualizations

Sun, 01 Nov 2026 00:00:00 +0900

When the same 3D bar chart was moved from a screen to a physical object, reading data became about 20% faster. It still did not match an on-screen display that combined a flat matrix view with 2D bar charts. This is the result of an experiment published in 2013 by Yvonne Jansen and her colleagues. The “about 20%” is my rough estimate read from the paper’s figures, and what was compared was speed, not accuracy.

The source is the paper “Evaluating the Efficiency of Physical Visualizations” (CHI 2013) by Jansen, Pierre Dragicevic, and Jean-Daniel Fekete. In dataphys.org, the catalog of data physicalization examples, it appears as a 2012 entry titled “Putting Physical Visualizations to the Test.” What follows tracks the body of the paper. Values I read from figures are marked “approximate” each time.

The Debate over 3D Bar Charts Was Only About Screens

In information visualization, the common view is that 3D displays are not recommended. The paper acknowledges this at the start of its background section.

Overall 3D visualizations are not recommended since they are subject to serious occlusion, distortion, and navigation issues.

In short, 3D is discouraged because of occlusion, distortion, and the difficulty of controlling the viewpoint. The authors continue right after:

However, the controversy of 2D versus 3D only concerns the on-screen modality.

That is, the 2D-versus-3D debate has only ever been held about screens. 3D is common in physical visualizations, yet problematic on screen; the abstract gives this as the reason for choosing 3D. In the background are digital fabrication machines and research on shape-changing interfaces. The authors expect that, as either advances, building physical visualizations will become practical. The way of comparing is also distinctive.

The vast majority of infovis studies involve comparisons across visual mappings only. We focus on comparisons across modalities only

Most studies have compared visual mappings; this one declares that it compares only the medium. Height, color, and layout are held constant, and only whether the chart is physical or on screen changes. The authors describe it as the first study to compare physical and on-screen visualizations on information visualization tasks. The paper itself cites earlier work that compared physical and screen media, such as research on spatial memory.

Thirteen Bar Charts Built from Acrylic

The data are 16 sets built from Gapminder country indicators. One set consists of 100 values, 10 countries by 10 years, and the indicators include electricity consumption, birth rate, and car-accident death rate. They were chosen because the indicators are easy to understand, the values vary enough, and the years are evenly spaced.

One acrylic 3D bar chart. Bars for 10 countries by 10 years are color-coded by country, with transparent scale plates and a base engraved with country names and years; a euro coin sits next to it

Source: a work by Yvonne Jansen, Pierre Dragicevic, and Jean-Daniel Fekete (no photographer credited) / from dataphys.org, “2012 – Putting Physical Visualizations to the Test”

The physical charts are made of laser-cut acrylic. For each country, one cross-section of a 2D bar chart is cut out, painted, and assembled. The templates were generated automatically from the data. Each chart measures 8 × 8 cm outside and weighs 270 to 350 g, so it can be held and turned in the hand. Thirteen were built. The mapping is as follows.

Data / variable	Appearance / material	Details
Indicator value	Bar height	Proportional to the value. Each bar is 5 × 5 mm, with 2 mm gaps
Country	Row position and bar color	ColorBrewer qualitative palette. Order sorted by similarity
Year	Column position	10 evenly spaced years
Aid for reading values	Tick lines engraved on the sides of the bars	Engraved on 2 sides in the physical version
Axis scale	Engraved on transparent acrylic plates	2 plates per chart. Tick intervals computed automatically
Country and year labels	Engraved vertically on the 4 sides of the base	Requires turning the chart slightly to read, but allows a compact size

As countermeasures against occlusion, transparent bars, thin bars, and top-face-only display were also tried. The result, in the paper’s words, was “They all turned out to have poor legibility.” All were hard to read, and the authors went back to ordinary bars.

More than ten physical 3D bar charts laid out on a floor. All share the same format; only the pattern of bar heights differs

Source: a work by Yvonne Jansen, Pierre Dragicevic, and Jean-Daniel Fekete (no photographer credited) / from dataphys.org, “2012 – Putting Physical Visualizations to the Test”

Physical Was Faster, and 2D Was Faster Still

In the first experiment, the same data were shown under four conditions.

Condition	Visual mapping	Medium	Interaction
physical	3D bar chart	Physical object	Hold, turn, and touch by hand
stereo	3D bar chart	Screen (stereoscopic)	23-inch polarized 3D monitor and glasses
mono	3D bar chart	Screen	Rotate with the mouse; click to highlight a bar
2D	Matrix view + 2D bar chart	Screen	Selecting a row or column label shows a bar chart of that cross-section

The 2D condition shows values by the size of a square in each matrix cell, and selecting a row or column displays a bar chart of that cross-section. The authors settled on this form after also trying overlaid line charts and small multiples.

the matrix view is analogous to a top view of the 3D model and the 2D bar chart view is analogous to a side cut.

The matrix corresponds to a top view of the 3D model, and the 2D bar chart to a cross-section. The paper also states the role of the 2D condition.

Although our goal is not to compare 2D with 3D visualizations, we included an on-screen interactive 2D condition as a comparison baseline.

In short, comparing 2D with 3D was not the goal; the 2D condition was included as a baseline for comparison.

There were three kinds of tasks: Range, which asks for the range of values for one country; Order, which asks participants to sort the 10 countries for one year by value; and Compare, which asks them to pick the smallest value among three country-year combinations. Sixteen participants answered 576 questions in total.

Errors were few overall and did not differ by condition. The analysis was run on completion time, and the effect of condition was clear (F(3,24) = 165.18, p < 0.001). The authors summarize:

Overall, we can safely conclude that the physical 3D visualization is more efficient than its 3D on-screen counterpart, mono or stereo alike.

The physical 3D bar chart is more efficient than the on-screen 3D bar chart, whether monocular or stereoscopic. By my rough estimate from the paper’s figure, the physical condition’s completion time was about 0.82 times that of the on-screen monocular condition, and the 2D condition’s about 0.63 times that of the physical condition. The “about 20%” in the opening is this value.

Stereoscopy did not help. “Contrary to H3, there is no evidence of stereo outperforming mono.” The hypothesis that stereo is faster than mono was not supported.

2D was fastest on all three tasks. However, the size of the gap varies by task.

So although 2D beats all 3D conditions, the effect is weaker if a task cannot be solved by one 2D cut.

2D beat every 3D condition, but the advantage is smaller when a task cannot be solved with a single cross-section. Reading mean times per task from the figure gives the following.

Condition	Range	Order	Compare
physical	about 24 s	about 47 s	about 39 s
stereo	about 27 s	about 62 s	about 52 s
mono	about 27 s	about 62 s	about 48 s
2D	about 15 s	about 24 s	about 31 s

(All values in the table are my rough estimates read from Figure 5 of the paper.)

Preference rankings matched the speed rankings. Some participants said of on-screen 3D, “I would loose my ‘mark’ easily during rotation,” meaning they lost their place as they rotated the chart. The recordings capture this behavior:

Most subjects used their fingers to temporarily “mark” rows and columns of interest. Eventually, almost everyone switched to putting their fingers directly on the bars relevant to the task.

Most people used their fingers to mark rows and columns, and in the end almost everyone put their fingers directly on the bars relevant to the task.

Part of the Speed Came from Fingers Placed on the Bars

Why is the physical chart faster? The second experiment separated the reasons and measured them. There were four conditions.

Condition	Description
mouse	On-screen 3D bar chart rotated with the mouse. No highlighting
prop	On-screen 3D bar chart rotated with a physical prop containing a rotation sensor
no touch	Physical 3D bar chart. Marking with fingers prohibited
touch	Physical 3D bar chart. Touching encouraged

From mouse onward, each condition adds one element: “rotating directly by hand,” “the look of the physical object,” and “touching.” Only the Compare task was used, with newly made problems in which no viewpoint shows all three bars at once. There were again 16 participants. The paper reports the results numerically in the text.

touch is 15% shorter than no touch (confidence interval 7 to 22%)
no touch is 13% shorter than prop (same, 5 to 20%)
prop is 3% shorter than mouse (same, −4 to 10%)

So the effect of rotating directly, if any, is small. In numbers, touching cut time by 15%, the look of the physical object by 13%, and rotating directly by hand made a difference of 3%.

The authors limit the meaning of touch as follows.

We do not refer to the use of tactile sensory information but to the action of placing fingers to use them as visual or memory aids.

It refers not to tactile information but to the act of placing fingers as a visual and memory aid. For the 13% gap between no touch and prop, the authors write only with a hedge, “Visual realism might also play a role.” This suggests that even with the same way of rotating, how the chart looks may have affected speed.

Dragicevic and Jansen later reanalyzed the second experiment with 56 different analysis methods and concluded that the results do not depend on the choice of analysis. For the first experiment, the authors themselves caution that, once corrected for multiple comparisons, some effects may become less certain.

How Third Parties Viewed This Study

The assessments fall into three groups: replication, positioning, and reactions at the time of publication.

Replication: In 2020, K. Danyluk and colleagues compared a physical Lego chart with a VR version, using the same format, data, and tasks (18 participants). On the Compare task, physical took 33.6 seconds, VR at palm size 56.6 seconds, and VR at table size 65.7 seconds. They report that every participant traced the bars with their fingers in the physical version, and summarize this as consistent with Jansen et al.’s results.
Positioning: In 2016, S. Stusak and colleagues introduced this study as the first to compare physical and on-screen displays on reading tasks. They also note that Jansen et al. themselves said their tasks and media were limited. Stusak and colleagues then moved their own focus to memory.
Reaction at the time: In a May 2013 blog post, Patrick Durusau asked how closely a VR simulation of touch could approach the real object.

Danyluk and colleagues also list “physical manipulation” as an advantage, but the original paper says rotating directly is not important, so this can be read as slightly off. Durusau’s question can be read as having been tested seven years later in VR by Danyluk and colleagues. Danyluk and colleagues also point out a limitation:

Ultimately, static 3D barcharts like those we used are unlikely to outperform either 2D or 3D visualizations that include interactive features like dynamic filtering or tooltips.

Static 3D bar charts are unlikely to beat displays with interactive features such as filtering or tooltips.

Takeaways

There are three takeaways about choosing a medium and one about evaluation methods.

First, if you use 3D, making it physical is an option. As a design implication, the authors write:

i) if an analyst wishes to use a 3D visualization, making it physical is an option worth considering; ii) physical visualizations should be built to support direct touch and not enclose data.

Making a 3D chart physical is worth considering, and physical visualizations should be built to allow direct touch and not enclose the data. Putting the model in a case would take away the “fingers on the bars” way of reading that mattered in this study.

Second, for data with two categories and one quantity, first consider the pair of a top view and a cross-section. This pair was fastest in the study. However, the study was not designed to settle which of 2D and 3D is better. For comparisons that a single cross-section cannot solve, 2D’s advantage also shrank. These two points need to accompany any citation of this work as grounds for avoiding 3D.

Third, provide a “place to put a finger” on screens too. What affected speed appears to have been less the rotation than the ability to leave a mark. The authors also write that 2D displays would benefit from paper or touch screens. A feature that keeps the selected row or column highlighted can be read as helping reading regardless of medium.

Finally, on evaluation methods: hold the visual mapping constant and change only the medium. Because of this design, “physical is faster” and “2D is faster” could both be stated, without contradiction, within the same experiment.

References

Primary sources

Yvonne Jansen, Pierre Dragicevic, Jean-Daniel Fekete, “Evaluating the Efficiency of Physical Visualizations,” Proceedings of CHI 2013, pp. 2593–2602. doi:10.1145/2470654.2481359 / HAL / PDF
dataphys.org, “2012 – Putting Physical Visualizations to the Test” (Added by Pierre Dragicevic). Entry page
The authors’ project page (the paper says it hosts the datasets, experiment data, and videos; I have not verified this). aviz.fr/phys
Pierre Dragicevic, Yvonne Jansen, “Re-Evaluating the Efficiency of Physical Visualizations: A Simple Multiverse Analysis”. Explorable Multiverse Analyses / Reproduction materials for the R multiverse package
Yvonne Jansen, Physical and Tangible Information Visualization, doctoral dissertation, 2014. HAL

Secondary sources

K. Danyluk, T. Ulusoy, W. Wei, W. Willett, “Touch and Beyond: Comparing Physical and Virtual Reality Visualizations,” IEEE TVCG, 2020. doi:10.1109/TVCG.2020.3023336 / Author version / Abstract
S. Stusak, M. Hobe, A. Butz, “If Your Mind Can Grasp It, Your Hands Will Help,” TEI 2016. PDF
Patrick Durusau, “Evaluating the Efficiency of Physical Visualizations,” Another Word For It, 2013-05-12. Blog

Pulling Out and Rearranging One Chart per Country: The Rearrangeable 3D Bar Chart

Sun, 01 Nov 2026 00:00:00 +0900

The Rearrangeable 3D Bar Chart is a physical 3D bar chart showing unemployment rates for 10 countries over 10 years. Each country’s 2D bar chart is a separate part that can be pulled out and pushed back in. People move the parts by hand to sort the countries, pull them out, and superimpose them to compare.

The creators are Yvonne Jansen and Pierre Dragicevic, then at the AVIZ team of Inria. The work is presented as one of eight case studies in their paper “An Interaction Model for Visualizations Beyond The Desktop” (IEEE TVCG, 2013). In dataphys.org, the catalog of data physicalization examples, it is listed as a 2012 example. What follows is based on this paper and the dataphys.org entry.

Ten 2D Bar Charts in One Piece

The dataphys.org entry describes the work in a single sentence.

A modular physical visualization like this rearrangeable 3D bar chart allows people to sort, filter, compare and examine data by direct physical manipulation.

In other words, a physical visualization made of separate parts lets people sort, filter, compare, and examine data by moving the parts directly with their hands. The entry was added by Dragicevic, and it is classified as “Passive physical visualization,” meaning it has no electronic components.

According to the paper, the chart shows “unemployment rates over 10 years for 10 countries.” With 10 countries and 10 years, that makes 100 bars by calculation. The parts were cut automatically from the data with a laser cutter, and painting and assembly were done by hand.

Left: a photo of a finger pinching a blue row and lifting it up. Right: a photo of a hand holding a whole pink row pulled out of the chart

Source: Yvonne Jansen and Pierre Dragicevic, dataphys.org, “2012 – Rearrangeable 3D Bar Chart”

In the left photo, a finger lifts a blue row. On the right, a whole pink row has been pulled out. Each row has a different color, and year labels beginning with “2001” line up on the red base at the front. The paper does not explain what the colors or the base mean, so what follows here is a reading from the photos. The table below summarizes how data maps to physical properties.

Data	Physical property	Basis
Unemployment rate (quantity)	Bar height	Paper. Scale values and proportions are unknown
Country (10 countries)	A row in the depth direction. One country is one part that can be pulled out and pushed back in	Paper
Year (10 years)	The horizontal arrangement of bars within each row. Fixed within the part	The 10 years are from the paper; the arrangement is a reading from the photos
Order of countries	Not determined by the data. Can be changed by hand	Paper
Distinguishing countries	Color per row	A reading from the photos
Year labels	The base at the front	A reading from the photos

As the source for how it was built, the paper cites the same authors’ CHI 2013 paper. That paper’s physical 3D bar chart and its evaluation experiment are covered in another article, “Faster than On-Screen 3D Bar Charts, but Not Faster than 2D.” I could not confirm whether the two are the same object or different specimens made in the same way. I also could not confirm the materials or dimensions for this work.

Pull Out, Move Apart, Superimpose

The paper divides the operations possible with this bar chart into two levels. The first is operations that change how the chart is seen, such as rotating the chart or pressing a finger on a data point. The second is operations that move the parts.

Each country is a 2D bar chart that can be taken out and manipulated separately. This simple design choice enables a user to perform a range of additional tasks (2): she can sort countries, filter them out by moving them away, or compare countries by superimposing them.

The paper contrasts this with an example of a single-piece model: a 3D-printed coral model used as a tool for manipulating a screen.

Left: a photo of the pink row being pulled out. Right: a diagram drawn with the paper’s model, with the labels “rotate & mark” at (1) and “sort & filter” at (2)

Source: Yvonne Jansen and Pierre Dragicevic, “An Interaction Model for Visualizations Beyond The Desktop” (IEEE TVCG, 2013), Fig. 9

The right side of Figure 9 is a diagram that describes these operations with the paper’s model. The arrow at (1) is labeled “rotate & mark” and the one at (2) “sort & filter.”

In the paper’s terms, filtering means “moving the parts away,” and comparing means “superimposing” them. Filtering here is not a process that deletes data but one that takes parts out of view, which can be read this way. Both can be read as leaving the data itself untouched.

Sorting Rewrites a “Free” Variable

The core of the paper is a model of the visualization pipeline. It divides the path into stages: raw data becomes a figure, the figure takes on a physical form, and a person reads it. The key to the explanation of sorting is free visual variables.

We call those free visual variables, to contrast them with encoding visual variables that are constrained by the visual mapping.

Visual variables that are not constrained by the mapping from data are called free visual variables, as opposed to encoding visual variables. In the paper’s example, the box plot as a mapping does not determine where along the horizontal axis each box is placed.

Free visual variables are fixed at the stage of the presentation mapping. The paper calls assigning them so as to make the chart easier to read optimization, and gives the example of ordering box plots by participant ID. It also distinguishes operations that locally override a uniform process: moving the nodes of a graph by hand, for instance, is an optimization-overriding operation. For this bar chart, the paper writes:

rearranging the visualization amounts to modifying free visual variables at the presentation mapping level, or more specifically, performing optimization overriding operations.

The order of countries is a variable the data never determined in the first place. Sorting changes neither the unemployment values nor the bar heights; only the placement changes. This work can be read as turning that free variable into a physical part and handing it to the user’s hands. The order of years, on the other hand, is fixed inside each part and cannot be changed by hand. The paper does not state this explicitly; it is a reading from the structure.

On a Screen, Separate Instruments; by Hand, One Motion

The paper describes the difference from a screen as follows.

Such operations are supported through true direct manipulation and are very versatile. In contrast, on a desktop system these tasks would typically be considered separately and supported by different instruments.

In the paper, an instrument is something that mediates between a person and a visualization; a sort button or a filter slider are examples. With this bar chart, every task comes down to one motion: pinching a part and moving it. It can be read as not needing to add an instrument for each task.

The same paper also describes the limits of manual operation in its conclusion.

Still we will always need instrumental operations as those allow to carry out complex tasks that have no real world counterpart (e.g., automatic sorting, brushing & linking).

Tasks with no real-world counterpart, such as automatic sorting or brushing and linking, will always need operations through instruments. Elsewhere, the paper also writes that tasks too complex or too repetitive to do by hand, such as reordering a matrix, are better left to the computer.

Another limit is the link to the data. The paper gives printed matter and 3D fabrication as examples of one-shot propagation: if the source data changes after the object is made, the form is not updated. This bar chart, too, is described as built by “semi-automatic one-shot propagation.”

How Third Parties Saw This Work

The evaluations fall into three: what the way of dividing parts means, users’ hesitation, and rearrangement by machine.

The first is a survey of the physicalization fabrication process presented at EuroVis in 2021 by H. Djavaherpour et al.

the way in which a physicalization is designed to be assembled can determine what types of interactions end-users can have with its represented data.

The point is that how a piece is designed to be assembled determines what kinds of operations users can perform on the data. The survey goes on to write that if Jansen et al.’s assembled 3D bar chart had been glued together, users could not have engaged with the data so deeply. However, the survey cites the CHI 2013 paper, and I have not confirmed whether it refers to the same object as this work. Also, Jansen is among the co-authors, so this is not a fully independent third party.

The second is Simon Stusak of LMU Munich. A different group built a physical visualization aimed at sorting and filtering by separating layers. A 2013 poster says that with a model whose layers can each be rotated, similar items can be grouped only along the time axis, and values within a layer cannot be reordered. In his 2016 doctoral thesis, he built a modular version whose parts are joined with hook-and-loop fasteners and compared it with a fixed version.

Four participants did not disassemble the modular physical visualization, two because it seemed impractical, two because of fear to break something.

Four participants did not take the modular version apart: two because it seemed troublesome, and two out of fear of breaking it. One participant who tried to take it apart broke a part. This was not Jansen et al.’s bar chart, but it can be read as showing that being able to pull parts out and push them back is different from people actually doing so.

The third is EMERGE by Faisal Taher et al. It is a physical bar chart with 10 x 10 bars raised and lowered by motors. The 2015 paper names the drawbacks of passive physical visualizations as being static and, once made, cut off from the data source. The IEEE VIS 2016 paper reports that in a presentation setting, some participants swapped rows and columns and built elaborate ways of showing the data. Jansen is a co-author of both.

Takeaways

In the 2015 paper “Opportunities and Challenges for Data Physicalization,” the same group wrote about the degree of freedom in rearranging parts as follows.

However, limited reconfigurability can be desirable. Information as to which states and operations are meaningful can be conveyed as physical constraints

Limiting how a piece can be reconfigured can be desirable, and which states and operations are meaningful can be conveyed through physical constraints. What can be taken from this work overlaps with this passage.

The first is to decide where to split the parts starting from the operations you want to allow. This bar chart is divided between countries, so the order of countries can change and the order of years is preserved. The position of the split decides at once which operations are allowed and which are not. Whether to glue, to make parts pull-out, or to make them snap in is part of the same decision.

The second is to think of sorting as separate from changing the data. In the paper’s framing, sorting is overriding a free visual variable, and no value changes. On a screen, too, if you treat the order as a variable the data does not determine, you can add operations like dragging rows without strain.

The third is to build ease of pulling out and putting back into the design. In Stusak’s observations, some people did not disassemble the piece for fear of breaking it. The design needs a form that shows a part may be pulled out and is easy to return.

The fourth is to identify the operations not to leave to the hand. With 10 countries, sorting by hand works, but as rows increase it is better left to computation. The fact that data is not updated once the object is made is also worth considering before choosing physicalization.

References

Primary sources

dataphys.org, “2012 – Rearrangeable 3D Bar Chart” (Added by Pierre Dragicevic): https://dataphys.org/list/rearrangeable-3d-bar-chart/
Yvonne Jansen, Pierre Dragicevic, “An Interaction Model for Visualizations Beyond The Desktop,” IEEE TVCG 19(12): 2396–2405, 2013. doi:10.1109/TVCG.2013.134 / Full text I read (mirror): https://typeset.io/pdf/an-interaction-model-for-visualizations-beyond-the-desktop-3p3eh2iru9.pdf / HAL (not checked by the author): https://hal.science/hal-00847218 / Authors’ page (the author could not connect): http://www.aviz.fr/beyond
dataphys.org, “2012 – Putting Physical Visualizations to the Test”: https://dataphys.org/list/testing-physical-visualizations/
Yvonne Jansen, Pierre Dragicevic, Petra Isenberg, Jason Alexander, Abhijit Karnik, Johan Kildal, Sriram Subramanian, Kasper Hornbæk, “Opportunities and Challenges for Data Physicalization,” CHI ‘15, pp. 3227–3236: https://eprints.lancs.ac.uk/id/eprint/72691/1/OA_2015_04_CHI_DataPhys.pdf
Yvonne Jansen, Pierre Dragicevic, Jean-Daniel Fekete, “Evaluating the Efficiency of Physical Visualizations,” CHI ‘13, pp. 2593–2602 (cited by the paper as the source for how it was built; the author has not read the full text): https://hal.archives-ouvertes.fr/hal-00781831

Secondary sources

H. Djavaherpour, F. Samavati, A. Mahdavi-Amiri, F. Yazdanbakhsh, S. Huron, R. Levy, Y. Jansen, L. Oehlberg, “Data to Physicalization: A Survey of the Physical Rendering Process,” Computer Graphics Forum 40(3), 2021 (EuroVis STAR): https://arxiv.org/pdf/2102.11175
Simon Stusak, Aurélien Tabard, Andreas Butz, “Can Physical Visualizations Support Analytical Tasks?” IEEE VIS 2013 poster: https://www.medien.ifi.lmu.de/pubdb/publications/pub/stusak2013visPoster/stusak2013visPoster.pdf
Simon Stusak, Exploring the Potential of Physical Visualizations, Dissertation, LMU München, 2016: https://edoc.ub.uni-muenchen.de/20190/1/Stusak_Simon.pdf
Faisal Taher, John Hardy, Abhijit Karnik, Christian Weichel, Yvonne Jansen, Kasper Hornbæk, Jason Alexander, “Exploring Interactions with Physically Dynamic Bar Charts,” CHI ‘15: https://www.kasperhornbaek.dk/papers/CHI2015_Emerge.pdf
Faisal Taher, Yvonne Jansen, Jonathan Woodruff, John Hardy, Kasper Hornbæk, Jason Alexander, “Investigating the Use of a Dynamic Physical Bar Chart for Data Exploration and Presentation,” IEEE TVCG 23(1), 2017: https://yvonnejansen.fr/articles/VIS2016%20-%20Investigating%20the%20Use%20of%20a%20Dynamic%20Physical%20Bar%20Chart%20for%20Data%20Exploration%20and%20Presentation.pdf