University of Southern Denmark
But Why? Design choices made while creating “Regional population structures at a glance”
Schöley, Jonas; Kashnitsky, Ilya
Published in:
New Generations in Demography
DOI:
10.18267/pu.2019.fis.2302.6
Publication date:
2019
Document version:
Final published version
Citation for pulished version (APA):
Schöley, J., & Kashnitsky, I. (2019). But Why? Design choices made while creating “Regional population
structures at a glance”. In J. Fischer, P. Mazouch, K. Hulíková Tesárková, & O. Kurtinová (Eds.), New
Generations in Demography: New Challenging Adventures in the Population Science (pp. 55-70). Oeconomica
Publishing House. https://doi.org/10.18267/pu.2019.fis.2302.6
Go to publication entry in University of Southern Denmark's Research Portal
Terms of use
This work is brought to you by the University of Southern Denmark.
Unless otherwise specified it has been shared according to the terms for self-archiving.
If no other license is stated, these terms apply:
• You may download this work for personal use only.
• You may not further distribute the material or use it for any profit-making activity or commercial gain
• You may freely distribute the URL identifying this open access version
If you believe that this document breaches copyright please contact us providing details and we will investigate your claim.
Please direct all enquiries to puresupport@bib.sdu.dk
Download date: 29. Oct. 2023
6
But Why? Design choices
made while creating
“Regional population
structures at a glance”
6
Jonas Schöley, Ilya Kashnitsky
In the days following the publication of “Regional population structures at a glance”
[Kashnitsky and Schöley, 2018] we were busy participating in the discussions of our
article’s main figure (reproduced in Fig. 6.1) on social media. Various groups were
sharing their thoughts on the colorful map of Europe showing the population agestructure on a regional level publicly discussing topics like European immigration,
abortion legislation in Ireland, Italy’s low fertility, East-West migration in Germany
and more. Meanwhile designers and visualization researchers debated the question
“clever or too clever?”. The hundreds of comments we received post-publication gave
us plenty of opportunity to reflect on our design. In this essay we want to explain
the design choices we made during the creation of the map, explore and discuss
alternative designs and evaluate our visualization against the goals we had when
starting this project. But first let’s take a look at the map in question:
6.
But Why? Design choices made while creating
“Regional population structures at a glance”
55
Figure 6.1 | Regional deviations from the average European age structure in 2015 as
published in the Lancet letter “Regional population structures at a glance”.
Source: Kashnitsky and Schöley, 2018
In the year 2015 Europe’s population was composed of 16.7% youths (ages 0–14),
65.8% working age (ages 15–64) and 17.4% elderly (65 years and above). The age
structure of any given region however may differ substantially from the European
average. Fig. 6.1 shows those regional deviations by the means of a (centered) ternary
color scale: Regions close to the European average show as dark shades of grey, e.g.
the Czech Republic or the south of Spain. The further a regions age structure deviates
from the aggregate age structure, the brighter and more vivid it is colored with the hue
of the color indicating the direction of deviation: Yellowish regions have a larger
share of elderly, e.g. Eastern Germany or the north of Spain; pinkish regions are
comparatively young and blueish regions have a larger than average share of workingage population.
Important choices in the design of this visualization were the use of a ternary
color coding scheme, the expression of regional age structure as a deviation from
56
Jakub Fischer, Petr Mazouch, Klára Hulíková Tesárková, Olga Kurtinová (eds.):
New Generations in Demography: New Challenging Adventures in the Population Science
the European average, the use of continuous colors and the use of an equal-area
map projection (vs. a cartogram). In the following we will explain these choices
and demonstrate alternatives. The direct quotations spread throughout the text are
taken from discussions of our map on social media.
6.1 But why ternary color-coding?
“The design took me a second to get used to but now I’m a fan.”
Simon Kuestenmacher (@simongerman600), July 24, 2018
“Might be a red flag when your legend needs a legend.”
Will Morris (@willmorris), July 25, 2018
Ternary diagrams are not widely used outside the fields of compositional data
analysis, chemistry and geology – using a color coded ternary diagram as a legend
for a map is mostly unheard of 27 and makes for a true xenograph [Lambrechts, 2018],
i.e. a visualization that most viewers are completely unfamiliar with. So why did we
choose such a weird technique, what did we achieve and what could we have done
instead?
Our design process was informed by two overarching goals and one technical
limitation:
1. make people understand how European regions differ in their age structures,
2. reach a wide audience, and
3. do it in a single plot 28.
Reach a wide audience: We knew that we had interesting data in our hands.
Regional age-structures compared to the European average show very clear spatial
patterns and provide a snapshot of the state of the demographic transition in Europe.
The data has the potential to inform the public debate about population ageing in
Europe – a debate that all too often simply invokes the image of an ageing continent
without addressing the regional diversity of the demographic transition.
But having interesting data is not enough to catch people‘s attention – using
a novelty viz-technique which shows clear global patterns at first sight as well as local
detail may however successfully engage users [Hullman et al., 2011]. We had some
prior experience using the ternary color scale [Schöley and Willekens, 2017] and found
that it was a technique well suited to get the interest of fellow academics who, after
a short introduction, were able to correctly infer information from the plot. So yes,
we were both aware of the flashiness of ternary color-coding and capitalized on this
very property to arrive at our secondary design goal. However, we wouldn’t have
chosen the ternary color coding technique if we found it to be ineffective at conveying
information.
27 For earlier examples of such maps see Brewer [1994], Dorling [2012, the corresponding PhD thesis
was published in 1992], and Schöley and Willekens [2017].
28 Due to “The Lancet” only allowing a single plot for submitted letters.
6.
But Why? Design choices made while creating
“Regional population structures at a glance”
57
Make people understand (in a single plot): Upon seeing the first crude version
of our ternary color map (a playful experiment at this point) we were amazed by
the sheer number of clearly visible spatial patterns, all of which related to meaningful
differences in the underlying data. This good result was unexpected. Using color
mixtures to represent multivariate data has experimentally been shown to be ineffective
in situations where it is important to make accurate judgements about the numeric
value of the involved quantities [Wainer and Francolini, 1980]. The main reason for
that is the inability of people to separate a mixed color into their primary components,
which is compounded by the difficulty of reading exact values from color encodings
of even univariate data [Ware, 1988]. Luckily, exact quantitative judgements about
the separate parts of the composition aren’t at all necessary in order to achieve our
first design goal, rather, the task of understanding the regional age-structure patterns
is one of similarity judgements (which regions look similar, which look different),
nominal judgements (does a region have more old, more young or more working age
compared to the average), and ordinal judgements (among two regions, which is further
away from the average), all of which can be performed effectively without requiring
the impossible feat of primary decomposition:
Similarity judgements
“I‘m surprised at the drastic difference between neighboring countries
(e.g. Poland/Germany) I would have expected a more gradual transition”
LockRay July 23, 2018
A basic principle when using color for data visualization is that similar colors should
represent similar data and colors perceived as dissimilar should represent dissimilar
data [e.g. Silva et al., 2011]. Being guided by this simple rule a reader will be able to
tell that East- and West-Germany feature greater similarities in their age structure
then Turkey and Poland. This is because the color-coding technique we used assigns
colors based on the magnitude and direction of deviation of a regional age structure
from the European average. Regions which differ from the European average by
similar degrees and in similar parts of the composition will feature similar colors.
Ware and Beatty [1988] found that a multidimensional color encoding allows for such
similarity judgements29.
Nominal judgements
“Why is Ireland so young?[...]”
drodrey July 23, 2018
In order to make qualitative statements about a region’s age-structure one simply
needs to recognize the meaning of three colors: Yellowish colors mark a higher share
of elderly people compared to the European average, blueish colors mark regions
with a comparatively large share of working age population and pink regions mark
relatively youthful regions. Reading the plot like this allows to correctly identify
features such as the comparatively high share of working age population in Poland
29 They tested whether subjects were able to identify clusters based on color similarity in a multidimensional data set.
58
Jakub Fischer, Petr Mazouch, Klára Hulíková Tesárková, Olga Kurtinová (eds.):
New Generations in Demography: New Challenging Adventures in the Population Science
as opposed to the high share of elderly in Germany. By using unique and distinct
hues for each of the three directions of compositional deviation from the average we
ensured that the ternary-balance scheme can be understood as a simple categorical
color scale which is known to be an effective encoding for nominal data [e.g. Ware,
2013; Munzner, 2015].
Nominal-Ordinal judgements
“I‘m wondering if the old East Germany thing is
a side effect of the wall coming down.”
PoorEdgarDerby July 23, 2018
The next level of understanding would be to recognize the significance of chroma
and brightness: The closer a regions age structure is to the European average the darker
and greyer it is colored. Using the chroma/brightness cue one can see that former
East Germany deviates more from the European average than former West Germany
and that while both Ireland and East Turkey have a young population, it is the Turkish
population that deviates further from the European average. Regions in the Czech
Republic and the south of Spain are quite representative of the European average in
terms of their three component age structure. Burns and Sheep [1988] have shown that
brightness and chroma are effective ordinal encodings even if hue is varying.
“[...]I‘m struggling a bit with the intermediate colors.
I would have preferred three maps with each of the variables
in their own monochrome scale.”
Bo Schwartz Madsen (@BoSchwartz) July 25, 2018
How does the ternary color-coded map fair when compared with a more conventional
encoding? We pick up the suggestion above to show the three components
of the composition in separate maps. As we are interested in deviations from the average
European age structure, we use divergent color scales with a mid-point centered on
the European share of the respective age-group.
6.
But Why? Design choices made while creating
“Regional population structures at a glance”
59
Figure 6.2 | Instead of showing the deviations of a three-part composition from
some average in a single map using the ternary-balance-scheme one can also show
the deviations for each part on a separate map. This way it’s easier to judge patterns
relating to any single part of the composition but arguably harder to make holistic
statements about the three-part composition.
Source: Authors; Data: Eurostat, Population on 1 January 2015 by broad age group, sex and NUTS 3
region (table "demo_r_pjanaggr3")
Sadly, a small-multiples map as shown in Fig. 6.2 was out of the question as
the publication format limited us to a single figure. A big strength of the repeated maps is
the perfect separability of the three compositional components. This facilitates analytic
judgements about each of the age-groups in separation from the others. On the other
hand, holistic judgements about the joint three-part composition (representative/nonrepresentative of European average, type of deviation from average) may be harder to
make because they require the integration of information from all three maps.
60
Jakub Fischer, Petr Mazouch, Klára Hulíková Tesárková, Olga Kurtinová (eds.):
New Generations in Demography: New Challenging Adventures in the Population Science
“Interesting approach with the ternary color scale,
but I can‘t decide if ‘clever’ or ‘too clever’.[...]”
Moritz Stefaner (@moritz_stefaner) July 25, 2018
Figure 6.3 | The color and orientation of a line encode the direction of deviation
of a region’s age structure from the European average: orange and forward leaning means
more young, purple and backward leaning more old and pink horizontals represent more
people in working ages. Two line symbols may be combined in a case where two groups
have a higher than average representation. The magnitude of deviation from the average
is encoded via line length and width with longer lines representing larger deviations.
Source: Figure by Moritz Stefaner, reproduced with permission of the author; Data: Eurostat
Shortly after publication our map caught the interest of German information
visualization designer Moritz Stefaner. He initiated a discussion among viz-professionals
and researchers regarding the effectiveness of our ternary color-coding. A consensus
emerged that the color encoding we used does not allow for accurate numeric estimates
of the data at display. An interesting alternative, using line symbols, is shown in Fig.
6.3. The superimposed lines can indeed easily be judged separately and line length is
known to be an accurate visual encoding [Cleveland, 1986]. Crossed lines are but one
among many alternative solutions proposed by famous cartographer Bertin [2010, first
edition 1967] for the problem of mapping multivariate data. Unfortunately, we did not
explore these solutions. It would be interesting to compare the ternary balance scheme
against these alternatives not only with regards to accuracy but also with regards to
the nominal and ordinal judgement tasks stated above.
6.
But Why? Design choices made while creating
“Regional population structures at a glance”
61
6.2 But why show deviations from an average?
“[...]Arguably only representing the differences in a trivariate rep. obscures the data
more by obfuscating the underlying data.[...]”
Danielle Szafir (@dalbersszafir) July 26, 2018
Figure 6.4 | Europe’s age compositions naturally is highly skewed towards the broadest age-group 15–65. Due to this narrow clustering using a regular ternary balance
scheme (as seen on the left) makes it impossible to gain any insight into the regional
variability of population structures. In order to see the internal variation of the data
we shifted the grey-point of the ternary color scheme to the location of the average
European age structure, thereby visualizing deviations from that average.
Source: Authors; Data: Eurostat, Population on 1 January 2015 by broad age group, sex and
NUTS 3 region (table "demo_r_pjanaggr3")
A central question in data analysis is “compared to what?”, e.g. does Poland have
a high life-expectancy (compared to Russia, compared to other EU members, compared
to other former Warsaw-Pact member states, compared to pre-1990 etc.). Changing
the point of reference changes the question. Some visualization techniques implicitly
define the comparison to take place: In case of the standard ternary balance scheme
(Fig. 6.4 left) lightness and hue encode the magnitude and direction of deviation from
a perfectly balanced composition. The resulting map is of little help, showing only that
the age structure of every European region is far from balanced and skewed towards
the working ages. By moving the grey-point of the ternary color scale from the location
of perfect balance to the location of the average age structure in Europe in 2015, we
change the comparison and research question to something much more interesting:
How does the age structure of a region deviate from the European average?30
30 We call this technique the “centered ternary balance scheme” and describe it in detail in Schöley
[2018b].
62
Jakub Fischer, Petr Mazouch, Klára Hulíková Tesárková, Olga Kurtinová (eds.):
New Generations in Demography: New Challenging Adventures in the Population Science
6.3 But why continuous colors?
“I have the impression that the 6 discrete color scheme shown on the lower legend
would have already sufficed, and possibly make the figure clearer.”
Selim Onat (@sel_onat) July 25, 2018
Selim Onat had a great idea on how to make the map more approachable: use only
6 colors. The resulting conventional choropleth map will be more familiar to the
audience with the added advantage that each of the 6 colors represents a distinct and
clear situation. Consequence of such a stark discretization though is a complete loss of
subtlety and detail. Ireland, according to the discrete map, is just as young as Eastern
Anatolia. Of course, other discretization schemes are possible but they all require
a balancing
act color
between
clarity
and detail.
Weeasier
optedtofor
maximumas
detail.
Figure
6.5 | Six
scheme
makes
the map
understand
discrete color
scales are widely used to encode qualitative information. The downside of this simplification
is the
substantial
bias. Compositional
gradiFigure 6.5 | The
sixintroduction
color schemeof
makes
the mapquantization
easier to understand
as discrete color
scales
ents
become
invisible,
the qualitative
age-structures
of Eastern
Anatolia
andofIreland
become
are widely
used
to encode
information.
The
downside
this simplification
indistinguishable
similar regions
in Germany
assigned highly
contrasting
is the introductionwhile
of substantial
quantization
bias.are
Compositional
gradients
become
colors
(Data:
Eurostat)
invisible, the age-structures of Eastern Anatolia and Ireland become indistinguishable
while similar regions in Germany are assigned highly contrasting colors.
Source: Authors; Data: Eurostat, Population on 1 January 2015 by broad age group, sex and NUTS 3
region (table "demo_r_pjanaggr3")
6.
But Why? Design choices made while creating
“Regional population structures at a glance”
63
6.4 But why no cartogram?
“Be careful over-interpreting this! Like all data maps,
it emphasizes large rural provinces and de-emphasizes cities.[...]
A lot of what we‘re seeing here is differences
in urbanism and youth mobility rather than age.”
agate_ July 23, 2018
“[...]I actually thought that was the most interesting thing about the map – London
as a spot of youthfulness in a wider landscape of middle aged-to-elderly folk is
probably actually a pretty accurate and telling feature of the UK[...]”
-burrito- July 23, 2018
Whenever maps depict relative quantities like rates or, in our case, shares
of a whole, information about the size of the affected population is lost. Instead
attention is drawn to large, sparsely populated areas. This bias is very relevant for maps
of the European continent given that more than half of the territory of the European
Union is uninhabited and roughly 3/4 of the EU’s population is concentrated in less
than 6% of its area [European Union, 2016]. In consequence our map of European age
structures over-represents sparse rural populations (e.g. large parts of Scandinavia)
and under-represents the urban majority (e.g. London, Berlin, Paris etc.) – this bias
can be corrected by using the cartogram technique.
Population cartograms distort geography in such a way that regions with equal
population counts occupy the same area on a map [Dougenik, 1985]. Compared to
a regular map of Europe a cartogram pulls attention away from the rural regions
towards the densely populated urban centers as illustrated by the cartogram version
of our age-structure map in Fig. 6.6: The shrunken regions of Scandinavia, rural
France & Spain and Eastern Europe are balanced by magnified urban centers such as
Paris, Berlin, Rome and Ankara.
64
Jakub Fischer, Petr Mazouch, Klára Hulíková Tesárková, Olga Kurtinová (eds.):
New Generations in Demography: New Challenging Adventures in the Population Science
Figure 6.6 | Cartograms perturb the physical geography of a place by population size
resulting in a map where regions occupy an area proportional to their populous. Notice
that on this map Iceland and Scandinavia have largely disappeared while the larger
cities are magnified.
Source: Authors; Data: Eurostat, Population on 1 January 2015 by broad age group, sex and NUTS 3
region (table "demo_r_pjanaggr3")
Note: We used the continuous cartogram algorithm by Dougenik [1985]
“There are places I‘ll remember // All my life, though some have changed“
The Beatles 1965
Geography is personal. On a world-map we can locate the places we and our family
and friends have lived and worked, the places we have visited, and places we have heard
stories about. This makes statistical maps a very engaging form of data visualization.
Showing regional level data for a whole continent gives the audience the opportunity
to learn about places they care about – but only if they can locate them. The redrawn
geography of a cartogram, while being the defining feature, is also its biggest
drawback. With familiar coastlines being distorted and the space between capitals
squashed and stretched orientation on a cartogram is no easy feat: note the difficulty
6.
But Why? Design choices made while creating
“Regional population structures at a glance”
65
in recognizing the outlines of East Germany, Wales or Austria, also, where is London?
Because we wanted the audience to recognize places of interest on the map we opted
against the use of a cartogram. Also, we felt that having to explain the relatively
unfamiliar cartogram technique in addition to the ternary balance scheme would
probably overstretch the audience’s patience.
6.5 What worked, what didn’t?
“I find it amazing that there are such clear differences between regions. You could
almost take away the national borders and still clearly know where they are.”
kenbw2 July 23, 2018
“Wow! Heaps of „Elderly“ across Europe!”
Rene Heim (@ReneHJHeim) Jul 24, 2018
We designed the map so that it would reach a wide audience and make them
understand the regional diversity of European age-structures. Did we succeed? What
worked, what did not and what have we learned?
We achieved our goal to reach a wide audience. In addition to the exposure gained
by publishing in a high-impact medical journal our map of regional age structures
in Europe was widely viewed, shared and discussed on social media. Within one
day of publication close to 300,000 people have seen our map on reddit (an online
discussion board) alone leaving hundreds of comments. We achieved similar exposure
on twitter albeit over a longer time period. A few online news outlets reported on
our article. The map inspired others to experiment with the centered ternary balance
scheme, producing maps of municipal age structures in Finland, income distribution in
Canadian cities and forecasted age structures of US counties (Fig. 6.7). We noticed that
the map was polarizing. Many people felt engaged by our visualization and seemed
to enjoy the complexity, others were appalled by the unfamiliar and dense encoding.
While we reached a wide audience, it was also a very select audience of people who
enjoy reading visualizations and are willing to invest some time engaging with them.
Reaching to a more general audience would have possibly required a more well-known
encoding, maybe even a simplification/categorization of the data.
66
Jakub Fischer, Petr Mazouch, Klára Hulíková Tesárková, Olga Kurtinová (eds.):
New Generations in Demography: New Challenging Adventures in the Population Science
Figure 6.7 | By publishing all the data and code necessary to reproduce our map
along with an R package implementation of our centered ternary color scale [Schöley,
2018a] we made it easy for others to produce their own maps. Here are some examples
of work inspired by our publication: A) Municipal age structures in Finland 2017 (by
Jani Miettinen, reproduced with permission of the author); B) Adjusted family income
distribution in Vancouver 2016 (by Jens von Bergmann, reproduced with permission
of the author); C) Adjusted family income distribution in Toronto 2016 (by Jens von
Bergmann, reproduced with permission of the author).
Have people been able to draw correct inferences from the map? Given the extensive
discussions of the map on social media we were able to gain some anecdotal insight
into how well the audience understood the visualization. Regions of Europe which are
qualitatively different in terms of their age structure were reliably understood: People
consistently pointed to Germany as an “old country” and to Turkey and Ireland as
“young”. The spatial discontinuity in age-composition at the German-Polish border
was pointed out multiple times. Some also correctly interpreted more subtle differences
between regions, such as the younger age structure of Ireland compared to Northern
Ireland and the larger share of people aged 65+ in the East of Germany compared to
the Western part.
When discussing features of the map people expressed them either in absolute terms
(i.e. region X is very old) or in cross-regional comparisons (i.e. region X is younger
than region Y). Rarely did anyone mention regional deviations from the European
average age composition which was our intended reading of the map and the colour
scale. In some cases, we observed gross misunderstandings such as judging Germany’s
population to be mostly elderly, or Ireland‘s population mostly young. We believe that
the cause for these misinterpretations is not so much the ternary color coding as it
is a design flaw of the legend: The color scale we used has annotations explaining
how ternary diagrams work. What we should have focused on instead is to explain
6.
But Why? Design choices made while creating
“Regional population structures at a glance”
67
how our color coding works. In Fig. 6.8 we show a re-design of the original legend
that emphasizes the deviation from the European average and explicitly states how
the color scale can be interpreted.
Figure 6.8 | While the original legend on the left explains how to read proportions from
a ternary diagram, the redesigned legend on the right stresses the meaning of the colors
and the percent point deviation from the European average.
Source: Authors; Data: Eurostat, Population on 1 January 2015 by broad age group, sex and NUTS 3
region (table "demo_r_pjanaggr3")
6.6 Summary
In summer 2018 we published “Regional population structures at a glance” [Kashnitsky
and Schöley, 2018] – a map showing how regions across Europe compare against
the European average in terms of their population age structure. We spent half a year
designing the map for the purposes of reaching a wide audience and making them
understand regional age patterns in Europe. Our map was widely shared and discussed
on social media which prompted us to reflect, post-hoc, on our design choices and their
effectiveness with regards to the stated purpose of the map. While we are more than
happy with the impact of our publication and the discussions of European demographics
it sparked (spanning such diverse topics as Irish abortion legislation, the rural-urban
divide, effects of the German reunification, European immigration policies, Poland’s
late second demographic transition, more traditional family structures among Kurds
in Turkey...) reflecting on the feedback we were also able to identify some problems
with our design ultimately stemming from too little user-testing and an incomplete
consideration of alternatives.
68
Jakub Fischer, Petr Mazouch, Klára Hulíková Tesárková, Olga Kurtinová (eds.):
New Generations in Demography: New Challenging Adventures in the Population Science
For every set of data there are countless ways to visualize it, some of which
are more effective at conveying the intended message than others.31 By trying out
different alternatives, testing them on colleagues, reflecting on the collected feedback
and finally sharing the result of our work alongside our reflections we would treat
the practice of visualizing data just as we treat the practice of modelling data: as
a design process.
6.7 One last note
When publishing a map showing European demographics one has to expect political
discussion. We’ve seen people using our map to argue for more immigration, against
immigration, for pro-natalist policies, against pro-natalist policies and so on… As
stated above the map was designed to be engaging, interesting and consequently to
invite discussion. Both of us gave our best to be available online, mostly on twitter
and reddit, to answer questions, correct misunderstandings and dispel falsehoods
regarding the demographics or the viz. Our most important task however was to keep
the discussion free of hate. There were some attempts to infuse radical right side
ideology into the discussion of the plot and the data and we saw it as our job to speak
out against it. We believe that it is the responsibility of researchers to participate in
the public discussion of their work. Especially in the age of social media, especially
when the work is of public interest.
References
BERTIN, J., 2010. Semiology of Graphics: Diagrams, Networks, Maps. Redlands, CA: ESRI Press.
ISBN 9781589482616.
BREWER, C. A. Color Use Guidelines for Mapping and Visualization. In Visualization in Modern
Cartography. Oxford, UK: Pergamon, 1994, pp. 123–147. ISBN 0080424163.
BURNS, B. and B. E. SHEPP. Dimensional interactions and the structure of psychological
space: The representation of hue, saturation, and brightness. Perception & Psychophysics.
September 1988, Vol. 43, No. 5, pp. 494–507.
https://doi.org/10.3758/BF03207885
CLEVELAND, W. S. and R. MCGILL. An experiment in graphical perception. International Journal
of Man-Machine Studies. November 1986, Vol. 25, No. 5, pp. 491–500.
https://doi.org/10.1016/S0020-7373(86)80019-0
DORLING, D., 2012. The Visualization of Spatial Social Structure. Chichester, UK: Wiley. Wiley Series
in Computational and Quantitative Social Science.
ISBN 9781119962939.
DOUGENIK, J. A., CHRISMAN, N. R. and D. R. NIEMEYER. An Algorithm to Construct Continuous
Area Cartograms. The Professional Geographer. February 1985, Vol. 37, No. 1, pp. 75–81.
https://doi.org/10.1111/j.0033-0124.1985.00075.x
EUROPEAN UNION. Urban Europe. Statistics on cities, towns and suburbs: 2016 edition [online].
Eurostat, 2016. [cit. 2019-01-08]. ISBN 9789279601392. Available from:
http://ec.europa.eu/eurostat/statistics-explained
31 In fact the situation is such that the space of possible designs is massive but contains only few
effective solutions [Munzner, 2015].
6.
But Why? Design choices made while creating
“Regional population structures at a glance”
69
HULLMAN, J., ADAR, E. and P. SHAH. Benefitting InfoVis with visual difficulties. IEEE Transactions
on Visualization and Computer Graphics. 2011, Vol. 17, No. 12, pp. 2213–2222.
https://doi.org/10.1109/TVCG.2011.175
KASHNITSKY, I. and J. SCHÖLEY. Regional population structures at a glance. The Lancet. July
2018, Vol. 392, No. 10143, pp. 209–210. https://doi.org/10.1016/S0140-6736(18)31194-2
LAMBRECHTS, M. Xeno.graphics. [online]. 2018. [Accessed 2018-10-26]. Available from:
https://xeno.graphics/
MUNZNER, T., 2015. Visualization Analysis & Design. 1. Boca Raton, U.S.A: CRC Press.
ISBN 978-1-4665-0893-4.
SCHÖLEY, J. and F. WILLEKENS. Visualizing compositional data on the Lexis surface. Demographic
Research. 17 February 2017, Vol. 36, No. 21, pp. 627–658. https://doi.org/10.4054/
DemRes.2017.36.21
SCHÖLEY, J. and I. KASHNITSKY. Tricolore: A Flexible Color Scale for Ternary Compositions.
[online]. CRAN. 1.2.0, 2018. [cit. 2019-01-08]. Available from: https://cran.r-project.org/
package=tricolore.
SCHÖLEY, J. The centered ternary balance scheme. A technique to visualize surfaces
of unbalanced three part compositions. Forthcoming. [online]. 2018b
[Accessed 2018-10-30]. Available from: https://github.com/jschoeley/ctbs.
SILVA, S., SOUSA SANTOS, B. and J. MADEIRA. Using color in visualization: A survey. Computers &
Graphics. April 2011, Vol. 35, No. 2, pp. 320–333. https://doi.org/10.1016/j.cag.2010.11.015
VON BERGMAN, J. Understanding Income Distributions Across Geographies and Time.
[online]. 2018. [Accessed 2018-11-2]. Available from: https://doodles.mountainmath.ca/
blog/2018/10/28/understanding-income-distributions-across-geographies-and-time/
WAINER, H. and C. M. FRANCOLINI. An Empirical Inquiry Concerning Human Understanding
of Two-Variable Color Maps. The American Statistician. 1980, Vol. 34, No. 2, pp. 81–93.
WARE, C. Color sequences for univariate maps: theory, experiments and principles. IEEE
Computer Graphics and Applications. September 1988, Vol. 8, No. 5, pp. 41–49.
https://doi.org/10.1109/38.7760
WARE, C. and J. C. BEATTY, J. C. Using Color Dimensions to Display Data Dimensions. Human
Factors. 1988, Vol. 30, No. 2, pp. 127–142.
WARE, C., 2013. Information Visualization. Perception for Design. 3. Waltham, MA: Elsevier.
ISBN 978-0-12-381464-7.
70
Jakub Fischer, Petr Mazouch, Klára Hulíková Tesárková, Olga Kurtinová (eds.):
New Generations in Demography: New Challenging Adventures in the Population Science