On Glyph Design for Wind Information in En-Route Air Trafﬁc Control

Linda Pfeiffer

1 a

, Michelle Martinussen

and Paul Rosenthal

3 b

Institute of Data Science, German Aerospace Center DLR, Jena, Germany

Independent Researcher, Germany

Institute for Visual and Analytic Computing, University of Rostock, Rostock, Germany

Keywords:

Air Trafﬁc Control, Wind Visualization, Empirical Study.

Abstract:

Information about the wind situation is crucial for en-route air trafﬁc controllers. In this paper, we compare

several glyph designs for showing wind direction and speed by the means of an empirical study. The different

designs are based on arrows, wind barbs, and text. During the study, we are measuring response times and

accuracy. Moreover, we collect evidence of the applicability of those designs in en-route air trafﬁc control by

qualitative feedback from air trafﬁc controllers. Our ﬁndings suggest, that the often-used wind barbs are less

suited for assessing wind speed and direction. Instead, a combination of arrow and text should be favored.

1 INTRODUCTION

Since weather phenomena have a vast impact on air-

craft behavior, air trafﬁc controllers have to consider

their effects in the planning of air trafﬁc to ensure safe

and ﬂuent trafﬁc ﬂows. Wind inﬂuences, for example,

the aircraft’s speed and its climb rate. Hence, this in-

formation should constantly be available to the con-

troller. Within this piece of work, we focus on the

representation of wind data for displays in en-route

air trafﬁc control.

Air trafﬁc controllers are facing a highly demand-

ing task that includes the consideration of a multi-

tude of information. Usually, an air trafﬁc controller

is seeking to plan the air trafﬁc about 20 minutes in

advance. However, constant changes in the ﬂight sit-

uation, high trafﬁc load, and unexpected events may

require the controllers to decide within seconds. An

intuitive and quickly perceivable representation of in-

formation is crucial in these situations. Current air

trafﬁc control interfaces in Germany represent wind

either as a 2-dimensional ﬁeld of wind barbs on a

crowded weather display or in purely textual form.

Both approaches are less suitable for fast and intuitive

assessment in time-limited situations.

We conducted initial interviews with 6 air trafﬁc

controllers (4 male, 2 female) with a work experience

ranging from several months to 30 years. We asked

https://orcid.org/0000-0003-0135-6060

https://orcid.org/0000-0001-9409-8931

them about their weather data needs and concrete ex-

periences they have with the current weather repre-

sentations. These interviews showed that an initial

rough overview (no exact numbers) of the wind situa-

tion is needed and sufﬁcient for the controllers. Also,

giving wind information over the whole horizontal ex-

tent of the sector they are responsible for is unnec-

essary, as in en-route sectors the wind changes only

little in the horizontal extent. Instead, wind informa-

tion is needed for several altitudes within the sector,

since the wind situation could change a lot between

altitudes. Based on these spatial requirements we sug-

gest a table-like design, representing wind speed and

direction by glyphs and/or text in several altitudes.

Note, that this design is not meant to be part of the

main radar view that shows the aircraft’s positions,

since it may interfere with tasks like conﬂict detec-

tion. Similar to current weather displays, it is planned

as a separate view next to the radar view.

In this paper we provide a comparison of several

glyph designs based on arrows, wind barbs, and text

that show wind direction and speed. We quantita-

tively compare them in terms of their response times

and accuracy when assessing wind speed and direc-

tion. In addition, we report on the suitability of the

approaches for the application domain, based on ex-

pert feedback on the designs.

164

Pfeiffer, L., Martinussen, M. and Rosenthal, P.

On Glyph Design for Wind Information in En-Route Air Trafﬁc Control.

DOI: 10.5220/0010227701640172

In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 3: IVAPP, pages

164-172

ISBN: 978-989-758-488-6

2 RELATED WORK

Techniques for visualizing wind are mainly subject in

the domain of ﬂow and vector ﬁeld visualization (Bu-

jack and Middel, 2020; Johnson and Hansen, 2004;

Laramee et al., 2004; Post et al., 2003). Within

this domain, there are three groups of common ap-

proaches: glyphs (Borgo et al., 2013), showing mag-

nitude and direction of the ﬂow, geometric struc-

tures (McLoughlin et al., 2010; Reina et al., 2019),

like streamlines or path lines, and techniques that blur

textures with the ﬂow (e.g. Line Integral Convolution

(LIC) (Cabral and Leedom, 1993)).

As we are not interested in representing a whole

vector ﬁeld and all of its details, we focus on the

glyph-based designs that better reﬂect single data

points. For representing the ﬂow’s direction and mag-

nitude, classical designs like arrows or wind barbs,

often used in meteorology, exist, but also more ad-

vanced designs that try to encode further information.

Examples are the Flow Radar Glyphs (Hlawatsch

et al., 2011) and the pathline glyphs (Hlawatsch et al.,

2014) by Hlawatsch et al., that include information

about time dependent dynamics, as well as the glyph

design suggested by Wittenbrink et al. (Wittenbrink

et al., 1996), that includes uncertainty information.

There is also a bunch of work assessing percep-

tual effects of and comparing classical glyph designs

to other vector ﬁeld representations by the means of

an empirical study. Their ﬁndings suggest that arrows

on a regular grid induce larger errors on an advection

task than various streamlines, streamlets, and LIC ap-

proaches (Laidlaw et al., 2005; Pineo and Ware, 2010;

Ho et al., 2015). In addition, they are less accurate and

sometimes even slower when identifying and classi-

fying critical points in a ﬂow ﬁeld (Laidlaw et al.,

2005; Ho et al., 2015). Otherwise, arrows encoding

the ﬁeld’s magnitude by length are better suited than

streamlets, streamlines, and LIC for tasks where a vi-

sual attraction to regions with high speed is needed

(Ho et al., 2015). However, these tasks are speciﬁc to

the visualization of a whole ﬂow ﬁeld, while we are

more interested in the assessment of orientation and

magnitude at a speciﬁc point.

One step into the direction of this aim was done

by Martin et al. (Martin et al., 2008). They found, that

when reading wind barbs and estimating these over an

area people usually underestimated wind speed and

had a counter-clockwise bias in direction estimation.

The task of estimating wind speed and direction at a

speciﬁc point in a ﬂow ﬁeld was investigated in two

studies by Ware, Pilar, and Plumlee. They found that

streamlines with arrowheads and curved wind barbs

are more accurate than classical wind barbs in esti-

mating wind direction, while they did not ﬁnd any dif-

ference in speed assessment (Pilar and Ware, 2013).

The other study compared wind barbs with an arrow-

like design encoding magnitude by relative size and

traces/pathlets. They did not ﬁnd any difference in the

direction assessment. However, they found that path-

lets outperformed wind barbs, which outperformed

the arrow-like design in speed estimation (Ware and

Plumlee, 2013).

Based on theories in neuroscience, Ware derived

recommendations on the visualization of ﬂow ﬁelds

(Ware, 2008). He recommended using ﬂow parallel

streamlines instead of arrows for representing orien-

tation and suggested several approaches for the design

of asymmetry in order to perceive the direction’s sign.

The application of these results can only partially

be transferred to our designated scenario. For one

thing, ﬂow parallel methods, like streamlines, are not

applicable in a scenario with only a few data points

among one spatial dimension and the comparison of

glyph based designs is restricted to wind barbs and

arrowheads encoding speed by relative size. For an-

other, we expect the assessment of wind speed and

direction at a certain point to be less complex in our

scenario, as participants only need to interpolate be-

tween two glyphs instead of four glyphs in a whole

vector ﬁeld.

3 WIND VISUALIZATION

DESIGNS TO BE COMPARED

As mentioned above, en-route air trafﬁc controllers

need only one wind data point per sector in the hor-

izontal extent but several data points in the vertical

extent. These data points should be regularly sampled

over the whole vertical extent of a ﬂight sector (e.g.,

every 50th ﬂight level, that represents 5,000 feet in

altitude) having an overlap of at least 30 ﬂight lev-

els to the sectors above and beneath. Using this sam-

pling rate, 6 data points are enough to cover an aver-

age en-route sector in Germany. We decided to en-

code altitude by position on the y-axis, since this best

supports the usual mental model of altitude. This de-

cision leads to a table-like design positioning glyphs

and/or text, encoding wind magnitude and direction,

equidistantly on top of each other.

As wind direction and speed should be easily as-

sessable , we compare several data point representa-

tions (see Figure 1) by means of an empirical study.

Encoding wind direction by slope best supports spa-

tial reasoning. Thus, all glyphs encode wind di-

rection by slope, leaving several options for encod-

ing wind speed. Length is, according to Mackinlays

On Glyph Design for Wind Information in En-Route Air Trafﬁc Control

165

100

150

200

250

300

75kn

50kn

25kn

100kn

0kn

0°

from N to S

90°

from E to W

180°

from S to N

270°

from W to E

(a) ProgressBarArrow

100

150

200

250

300

5kn

25kn

10kn

50kn

75kn

100kn

0°

from N to S

90°

from E to W

180°

from S to N

270°

from W to E

(b) WidthArrow

100

150

200

250

300

0-9kn

10-19kn

20-29kn

30-39kn

40-49kn

50-59kn

60-69kn

70-79kn

80-89kn

90-99kn

>=100kn

0°

from N to S

90°

from E to W

180°

from S to N

270°

from W to E

100

150

200

250

300

18kn

11kn

12kn

1kn

10kn

0°

from N to S

90°

from E to W

180°

from S to N

270°

from W to E

(d) TextArrow

100

150

200

250

300

18kn

11kn

12kn

1kn

10kn

55°

80°

95°

150°

335°

0°

0° = from N to S

90° = from E to W

180° = from S to N

270° = from W to E

(e) TextText

100

150

200

250

300

0kn

5kn

10kn

25kn

50kn

75kn

100kn

0°

from N to S

90°

from E to W

180°

from S to N

270°

from W to E

(f) WindBarbs

Figure 1: Illustration of the wind visualizations, compared in our study. The ﬁrst column represents the altitude followed by

wind speed and direction.

ranking of visual variables, after position most effec-

tive for encoding quantitative data (Mackinlay, 1986).

Hence, we included a design using a progress bar

(ProgressBarArrow) and a design encoding speed

by line width (WidthArrow). Color value is less effec-

tive (Mackinlay, 1986), but it seemed natural to com-

bine color value with an arrow without affecting its

shape, so we included the design ValueArrow. Due

to comparison to the representations used in current

interfaces we also included textual representations as

well as a classical wind barb design.

4 USER STUDY

In a context with limited time for decision making,

such as air trafﬁc control, the fast and accurate ex-

traction of information is of major importance. Thus,

we set up an online experiment in order to judge the

intuitive assessment of wind, using the suggested vi-

sualization designs in terms of response time and ac-

curacy. This quantitative part of the study is comple-

mented by qualitative feedback from air trafﬁc con-

trollers, to get a deeper understanding of the applica-

bility of the designs in practice.

4.1 Methods

Participants. We decided to conduct the quanti-

tative part with both, novices and experts and the

qualitative part with expert users only. On the one

hand, this allows us to measure the intuitive assess-

ment of the glyphs and, thus, prevents any bias to-

wards one option because of extensive training. On

the other hand, despite restricted access to air traf-

ﬁc controllers, we get an impression on the speciﬁc

characteristics of the application domain. 54 novices

(mainly students) participated in the experiment (16

female, 37 male, 1 unknown). Their age ranged be-

tween 18 and 63 years with an average of 28 years.

Furthermore 9 experts aged between 22 and 48 years

with an average of 31 years (2 female and 7 male)

completed the study. 8 of the experts were working

as air trafﬁc controllers in Germany and 1 was an air

trafﬁc control student. Their work experience in en-

route sectors ranged from 1 year to 26 years with an

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166

Figure 2: Screenshot of the interface presented to the participants.

average of 10 years. Except for 6 persons, all partic-

ipants claimed to have normal or corrected to normal

vision. One person had dyschromatopsia. The partic-

ipants did not receive any reward.

Materials. We planned the experiment as an online

experiment, which allowed us to have more partici-

pants in a short time period, but hindered us from con-

trolling hardware-related displaying issues, like dif-

ferences in color.

We prepared six scenarios based on weather

datasets from Nancy, France. They were obtained

from the open-source access of the National Oceanic

and Atmospheric Administration of the U.S. Depart-

ment of Commerce (NOAA Datasets, 2016). In order

to map the values on every 50th ﬂight level of a sec-

tor ranging from the 105th to the 256th ﬂight level,

a linear interpolation of the original data points was

computed. From each of the scenarios, we gener-

ated one stimulus with each of the six visualizations.

As the experiment followed a within-subjects design

approach, each participant saw each of the scenar-

ios in another visualization, while the mapping be-

tween scenario and visualization type was randomly

assigned.

Procedure. After a welcome text and the partici-

pant information sheet, the experiment started by col-

lecting some demographic data (gender, age, and vi-

sion) and information about the professional experi-

ence in air trafﬁc control (e.g. years of experience,

country of employment, and whether the participant

is mainly operating in en-route sectors).

In order to get familiar with the upcoming task,

a training example was presented to the participants.

Then, they were asked to judge the wind direction

and wind speed at a certain altitude (See Figure 2 for

a screenshot). We advised participants to answer as

fast as possible. The altitude in question was usu-

ally located between two of the displayed data points.

Participants solved this task once with each visualiza-

tion type in a randomized order. Per participant, the

scenarios were randomly assigned to the visualization

type, such that each scenario was seen once by the

participant. Each visualization type was brieﬂy intro-

duced to the participants by the explanations shown

in Figure 1, before answering the questions regarding

this visualization type.

We measured the error in speed judgments in

knots, the error in direction judgments on an eight-

point wind rose, as well as response times in sec-

onds. Expert users ﬁnished by answering a post-

questionnaire about what they liked and disliked

about the designs.

Quantitative Analysis. First, we transformed the

individual answers to an comparable error measure.

For wind speed, we subtracted the correct value be-

longing to the corresponding scenario from the esti-

mated value. Negative values, hence, indicate an un-

derestimation, positive values an overestimation. For

calculating the absolute wind direction error, the dis-

On Glyph Design for Wind Information in En-Route Air Trafﬁc Control

167

Figure 3: Average response time for novices (orange) as

well as experts (green), visualized with the corresponding

95% bootstrapped conﬁdence intervals for each visualiza-

tion design. Letters denote signiﬁcance groups. That is, two

conditions sharing the same letter do not differ signiﬁcantly.

Designs enclosed by a violet border show a signiﬁcant dif-

ference between novices and experts.

tance to the correct answer on a 8-point wind rose was

calculated. Hence, the wind direction error is a value

between 0 and 4. For the non-absolute direction er-

ror positive values indicate a clockwise bias and neg-

ative values indicate a counter-clockwise error, while

an 180

◦

error was set to 0.

In order to compare the visualization designs ac-

cording to the errors in estimating wind speed and di-

rection as well as response time, we computed for

each design 95% conﬁdence intervals, respectively.

Since the data did not reﬂect the assumption of nor-

mality, we decided to use a bootstrapping approach

instead of computing conventional conﬁdence inter-

vals. We drew 10,000 bootstrap samples, computed

their means, and computed the 95% conﬁdence inter-

vals using the percentile method (Efron and Tibshi-

rani, 1986). Accordingly, we calculated 95% conﬁ-

dence intervals for the paired differences between the

designs for inferential analysis. If one of the latter

conﬁdence intervals does not contain the value zero,

their difference can be considered as statistically sig-

niﬁcant at a signiﬁcance level of α = 0.05. We did

these computations once for the group of novices and

once for the group of experts. Furthermore, we cal-

culated 95% conﬁdence intervals for the differences

between experts and novices.

Qualitative Analysis. For analyzing the qualitative

feedback for each design, we encoded and classiﬁed

the data using a bottom up coding approach. In a sec-

Figure 4: Average absolute speed error for novices (orange)

as well as experts (green) visualized with the corresponding

95% bootstrapped conﬁdence intervals for each visualiza-

tion design. Letters denote signiﬁcance groups. That is, two

conditions sharing the same letter do not differ signiﬁcantly.

ond cycle, we classiﬁed whether each comment was

positive about the design in question or negative and

counted those.

4.2 Quantitative Part: Results

The point estimates, their corresponding 95% boot-

strapped conﬁdence intervals, as well as signiﬁcance

groups (signiﬁcant differences only exist between

those groups) are illustrated in Figures 3, 4, and 5.

The paired differences between the designs are shown

in the appendix.

Response Time. Most obvious, air trafﬁc con-

trollers performed with a mean of 25.83 seconds

about 15.67 seconds faster than novices. The differ-

ences between the expert group and the novices were

signiﬁcant for each design. Both groups performed

signiﬁcantly fastest ((α < 0.05) with the TextArrow

design (signiﬁcance group A). In contrast to the ex-

perts, there was a third signiﬁcance group (C) con-

sisting of the WindBarbs and TextText designs that

showed signiﬁcantly slower response times than any

other design for novices.

Speed Error. Concerning the absolute speed error,

the resulting signiﬁcance groups are not as clearly

separated as for the response times. Nevertheless,

novices were signiﬁcantly more accurate using one

of the designs encoding speed as text than they were

using the designs ProgressBarArrow, ValueArrow,

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Figure 5: Average direction error for novices (orange) as

well as experts (green) visualized with the corresponding

95% bootstrapped conﬁdence intervals for each visualiza-

tion design. Letters denote signiﬁcance groups. That is, two

conditions sharing the same letter do not differ signiﬁcantly.

Designs enclosed by a violet border show a signiﬁcant dif-

ference between novices and experts.

or WindBarbs. Within the experts group, this ten-

dency is less clear. Air trafﬁc controllers, however,

showed additionally signiﬁcant differences between

the ProgressBarArrow design and the WindBarbs

and ValueArrow designs. There were no signiﬁcant

differences between air trafﬁc controllers and novices.

Considering the non-absolute values of the speed

error, the conﬁdence intervals of the design TextText

showed a systematic overestimation of the val-

ues in both groups (Novices: [1.2830, 4.6981],

Experts: [3.3846, 17.3850]). Additionally, the

designs ProgressBarArrow and WindBarbs were

overestimated by the novices ([0.6159, 10.4810],

[4.0566, 13.3770]).

Direction Error. Regarding the direction error, we

barely found signiﬁcant differences for novices. Only

with ProgressBarArrow participants seemed to per-

form more accurate than with TextText. Although

the results are not signiﬁcant, it seems that partic-

ipants tend to be more accurate with an arrow de-

sign than with TextText or WindBarbs. This ten-

dency is conﬁrmed by the results of the experts group.

WindBarbs led to signiﬁcantly less accurate answers

than the arrow-based designs did. Except for the

TextArrow design, there were also signiﬁcant differ-

ences between the TextText design and the arrow-

based designs. Surprisingly, we found only one sig-

niﬁcant difference between air trafﬁc controllers and

novices. Even though air trafﬁc controllers should be

trained in reading wind barbs, they performed signif-

icantly less accurate with the WindBarbs design than

novices did.

Considering the non-absolute direction error,

we only found a slight but signiﬁcant bias for

the ValueArrow design in the novices group.

Novices tended to have a slight clockwise bias

([0.0370, 0.2037]). Beyond this, we didn’t ﬁnd any

further biases for the assessment of direction.

4.3 Quantitative Part: Discussion

Within the application domain, a fast and accurate as-

sessment of the current wind speed and direction on

each ﬂight level is necessary. Our results show that

air trafﬁc controllers are signiﬁcantly faster in making

their decisions at a similar accuracy level than novices

are. The, nevertheless, quite long response times be-

tween 20 and 30 seconds on average are no reason

to worry, since they can be explained by the time

needed for entering the responses and the unfamil-

iar representation (remember that participants judged

wind speed and direction only once for each visual-

ization type).

Our results for speed error suggest using a design

representing speed as text. However, this result has

to be seen within the realm of the limitations of this

study. One limitation may be the measuring method.

We asked participants to report speed in knots. There

is thus a natural favor for the textual representations,

as they directly provide this value, so that the partic-

ipants did not have to transfer the representation and

the only error that remains arises from interpolation

between levels. Nevertheless, the results for the ex-

perts group do also suggest rather using a progress

bar for speed representation than using wind barbs or

an encoding via value, which perfectly aligns with

Mackinlays ranking of visual variables (Mackinlay,

1986).

The results in direction error suggest using an ar-

row based design in the application domain for repre-

senting direction instead of text or wind barbs. Espe-

cially air trafﬁc controllers were less accurate with the

latter designs than they were with most of the arrow-

based designs.

In accordance with the before-mentioned encod-

ings, the response times clearly suggest using the

TextArrow design. However, these results have to

be seen in the light of the same shortcomings regard-

ing the measurement method. Despite the choice of

the best suitable glyph design for representing wind

direction and speed in en-route air trafﬁc control, our

results suggest that the currently used designs are a

fairly bad choice. Novices are signiﬁcantly slower

On Glyph Design for Wind Information in En-Route Air Trafﬁc Control

169

when using purely textual representations or wind

barbs. Using more intuitive designs would especially

help air trafﬁc control students. Even more critical is

the ﬁnding that actual air trafﬁc controllers are clearly

less accurate when using wind barbs.

When comparing our results to the related work,

we were not able to conﬁrm the ﬁndings by Martin et

al. (Martin et al., 2008). In contrast to their ﬁnding

that wind barbs lead to a systematic underestimation

of wind speed when assessed over an area, we found

a systematic overestimation of wind speed when in-

terpolated between two wind barbs. Neither could we

ﬁnd any counterclockwise bias when assessing wind

direction. Also, a general conclusion whether arrows

or wind barbs are generally better suited for speed and

direction assessment is not feasible. While Ware and

Plumlee showed that arrows, encoding speed by size,

performed worse than wind barbs in speed assessment

(Ware and Plumlee, 2013), we did not ﬁnd any sig-

niﬁcant difference between wind barbs and the arrow

designs encoding speed by width or value. And while

our results suggest, either by being slower or by less

accurate results, that wind barbs are less suited for

representing direction than arrows are, the authors did

not ﬁnd any signiﬁcant differences between their de-

signs in direction error.

4.4 Qualitative Part: Results and

Discussion

Encoding Direction. For the different encodings of

wind direction, there were most positive comments

(28) about representing wind direction by an arrow.

Arrows were perceived by the controllers as easy

and fast to read, intuitive, and as a clear encoding.

While for most of the participants the arrow’s ac-

curacy was good, two controllers still had concerns

about it. These controllers, however, did enjoy the

good readability and quick overview provided by ar-

rows. Adding some pop-up information, showing the

exact numbers when focusing a certain arrow, may

solve the issues about accuracy. Encoding wind di-

rection as text was mainly enjoyed due to the accu-

racy, but was criticized due to the slow reading speed.

Most questionable was the encoding of wind direc-

tion with wind barbs as at least one participant was

confused about the direction and four more found the

direction hard to determine. The issues with reading

direction from wind barbs are also reﬂected by the air

trafﬁc controllers accuracy in the quantitative part of

the study.

Encoding Speed. In contrast to our expectation,

there were only a few positive comments on the sev-

eral graphical encodings of speed. Encoding speed as

width or value of an arrow was perceived by two con-

trollers as suitable for a good but imprecise overview.

Apart from the lack of accuracy, these designs were

criticized for not being intuitive, readable, and for

their ’ugly’ appearance. Similarly, the wind barbs’

speed encoding was perceived by one controller as

suitable for a fast overview, but, apart from this, it

was perceived as slow to read (because of count-

ing), not intuitive, and awkward. Even though the

ProgressBarArrow design was more accurate than

the ValueArrow and WindBarbs designs, air trafﬁc

controllers did not ﬁnd any positive words about this

design. Similarly, as the aforementioned designs, it

was criticized for its lack of accuracy and for neither

being intuitive nor easily readable. In contrast to the

other encodings for wind speed, text was liked by 7

controllers due to its accuracy and clarity. Even if the

quantitative results may be in favor of the text based

encoding due to the measuring method, it is still the

most liked encoding for speed.

Further Design Issues. Two controllers indicated

to prefer designs combining direction and speed in

one symbol, which reduces the number of objects on

the screen. Unfortunately, these designs were per-

ceived as hard to read, even by the aforementioned

two controllers. There were some comments for im-

provement about the designs, showing wind direction

and speed in separated glyphs, since in aviation the

usual way to declare wind is to show the direction in

degrees ﬁrst, followed by the speed. Thus, changing

the order of the columns may increase the readability

for air trafﬁc controllers. Additionally, the ordering

of the rows should be inverted to better represent the

increasing altitude from the ground.

Summary. Similar to the quantitative results, the

qualitative results suggest using a combination, en-

coding direction by an arrow and speed by a number,

while wind barbs seem to be less suited for the ap-

plication domain. When judging a glyph design, air

trafﬁc controllers are highly concerned about the ac-

curacy a design offers but prefer also a design pro-

viding a quick overview. For better applicability in

the domain, columns and rows of the table should be

ordered such that speed representation follows direc-

tion and that ﬂight levels follow an ascending order-

ing from the ground to the top.

IVAPP 2021 - 12th International Conference on Information Visualization Theory and Applications

170

5 CONCLUSION

We compared the assessment of wind direction and

speed using several glyph and text-based represen-

tations by means of an empirical experiment as

well as qualitative feedback by air trafﬁc controllers.

Quantitative and qualitative results, both suggest that

WindBarbs are less suited for a quick but fairly ac-

curate overview of the wind data. In addition, both

results highlight a design, encoding speed by text and

direction by an arrow (TextArrow) for the applica-

tion in en-route air trafﬁc control. However, the exact

numbers should be accessible on demand, as these are

needed for the communication to pilots.

ACKNOWLEDGEMENTS

This work was partly done at the former Visual Com-

puting Laboratory at Chemnitz University of Technol-

ogy, Germany. We thank all of our study participants.

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On Glyph Design for Wind Information in En-Route Air Trafﬁc Control

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APPENDIX

Table 1: 95% bootstrapped conﬁdence intervals based on 10,000 bootstrap samples for the differences between the designs

based on novices data. Conﬁdence intervals not containing the value 0 can be interpreted as signiﬁcant differences at an

signiﬁcance level α = 0.05.

Design A Design B Response Times Abs Speed Error Abs Direction Error

Lower Upper Lower Upper Lower Upper

DesignA DesignB LowerRT UpperRT LowerSE UpperSE LowerDE UpperDE

ProgressBarArrow WidthArrow -6.2465 2.0909 -2.2495 7.3264 -0.3704 0.2593

ProgressBarArrow ValueArrow -5.9669 0.8138 -2.8846 3.7115 -0.4630 0.2407

ProgressBarArrow TextArrow 1.0194 9.0271 1.2500 8.4038 -0.2963 0.2773

ProgressBarArrow TextText -13.3200 -4.4443 1.6923 9.5385 -0.6667 -0.0370

ProgressBarArrow WindBarbs -17.2430 -6.5378 -8.4231 3.6154 -0.61111 0.1852

WidthArrow ValueArrow -4.8371 3.5065 -5.3585 1.6792 -0.4074 0.2963

WidthArrow TextArrow 3.1122 11.1290 -0.6226 5.4717 -0.2593 0.3148

WidthArrow TextText -11.1120 -2.1104 0.3208 5.3396 -0.6852 0.0741

WidthArrow WindBarbs -15.6310 -3.7218 -10.0940 0.5849 -0.5185 0.2037

ValueArrow TextArrow 3.8624 11.4510 2.1698 6.3774 -0.2037 0.4074

ValueArrow TextText -10.4100 -1.5302 2.2453 7.2075 -0.6296 0.1296

ValueArrow WindBarbs -15.9550 -3.1163 -7.3019 1.5472 -0.4259 0.2593

TextArrow TextText -17.6730 -9.6813 -1.3962 2.2264 -0.7222 0.0370

TextArrow WindBarbs -23.7140 -10.7690 -11.4520 -2.8118 -0.4629 0.0921

TextText WindBarbs -9.0652 2.4856 -11.8300 -3.3774 -0.2778 0.5926

Table 2: 95% bootstrapped conﬁdence intervals based on 10,000 bootstrap samples for the differences between the designs

based on experts data. Conﬁdence intervals not containing the value 0 can be interpreted as signiﬁcant differences at an

signiﬁcance level α = 0.05.

Design A Design B Response Times Abs Speed Error Abs Direction Error

Lower Upper Lower Upper Lower Upper

DesignA DesignB LowerRT UpperRT LowerSE UpperSE LowerDE UpperDE

ProgressBarArrow WidthArrow -4.2253 3.7315 -11.3080 2.3846 -0.4615 0.1539

ProgressBarArrow ValueArrow -11.9680 0.9354 -28.3850 0.0769 -0.2308 0.2308

ProgressBarArrow TextArrow 0.5324 8.6446 -2.1538 5.7692 -1.0000 0.0000

ProgressBarArrow TextText -7.6269 2.3646 -9.4615 6.0000 -1.3846 -0.2308

ProgressBarArrow WindBarbs -6.0369 2.7438 -19.3080 -1.3077 -2.6154 -0.8462

WidthArrow ValueArrow -12.3090 0.8669 -23.2310 6.3058 -0.1539 0.4615

WidthArrow TextArrow 0.5039 8.5784 0.3846 12.3850 -0.8462 0.3077

WidthArrow TextText -9.2322 3.7253 -4.3077 10.1540 -1.3846 0.0000

WidthArrow WindBarbs -5.7967 2.3331 -15.5370 4.5385 -2.3846 -0.6923

ValueArrow TextArrow 5.2793 1.5269 1.7692 28.7690 -1.0769 0.1539

ValueArrow TextText -2.5392 9.3899 0.3846 22.8460 -1.4615 -0.2308

ValueArrow WindBarbs -2.7183 9.7161 -11.2310 16.7690 -2.6923 -0.7692

TextArrow TextText -11.4740 -2.6846 -9.5385 3.0000 -1.0769 0.3846

TextArrow WindBarbs -11.7590 -0.5194 -20.6150 -3.2308 -2.1538 -0.5385

TextText WindBarbs -5.3284 8.3773 -21.5380 2.1538 -1.9231 0.0769

Table 3: 95% bootstrapped conﬁdence intervals based on 10,000 bootstrap samples for the differences between experts and

novices. Conﬁdence intervals not containing the value 0 can be interpreted as signiﬁcant differences at an signiﬁcance level

α = 0.05.

Difference Response Time Absolute Speed Error Absolute Direction Error

Lower Upper Lower Upper Lower Upper

ProgressBarArrow 5.9165 20.7683 -5.7688 6.8269 -0.1595 0.4444

WidthArrow 8.6454 22.4990 -14.1160 1.1726 -0.3746 0.4259

ValueArrow 3.8039 17.8624 -29.2452 2.4469 -0.0826 0.6083

TextArrow 7.3457 18.4133 -8.2467 2.2322 -0.9501 0.3490

TextText 11.0097 28.0319 -13.1233 0.6240 -1.0883 0.4401

WindBarbs 14.1151 33.1170 -19.9535 4.1292 -2.3063 -0.3761

IVAPP 2021 - 12th International Conference on Information Visualization Theory and Applications

172