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Analysis and Improvement of the
OpenStreetMap Street Color Scheme for Users
with Color Vision Deficiencies
Johannes Kröger, Jochen Schiewe, Beate Weninger
HafenCity Universität Hamburg
Abstract. In this paper we analyze the street class color scheme of the
“Standard” openstreetmap.org map style in regards to users with color vi-
sion deficiencies. We describe the process of adjusting the existing color
scheme to accommodate these users whilst trying to preserve the overall
appearance. The results of an online user study to test both the original and
the adjusted color scheme are presented.
Keywords: color vision impairment, color vision deficiency, color blind
users, color design, color scheme, OpenStreetMap
1. Introduction
In a field like cartography, where color is used as a graphical variable, color
vision deficiencies can turn interpreting a map into a hard, frustrating or
even impossible task. Affected users tend to confuse certain colors and have
problems to comprehend the presented content. If a map style is designed
without accessibility in mind, problems are very likely. This is especially
true for organically grown map styles like that of the crowdsourced open
map data project OpenStreetMap
1
2. Background and Related Work
. Objective of our research is to evaluate
the color vision deficiency issues of the street class color scheme of the de-
fault map on openstreetmap.org, improve and test it in an online survey.
About 8
% of males are affected by red-green color vision deficiencies
(CVD). For genetic reasons women are much less likely to be affected
1
http://www.openstreetmap.org
(~0.4
%). About 4.6
% of men are affected by deficient green color vision
(deuteranomaly), about 1.3
% by green blindness (deuteranopia). Deficient
red color vision (protanomaly) affects about 1.1
% of men, another 1
% are
red blind (protanopic). (Sharpe et al. 1999)
These deficiencies lead to an altered perception of color. Both deutan and
protan deficiencies lead to confusion of red and green color shades. Sharpe
et al. (1999) could not reference any well researched estimates for the very
rare tritan deficiencies (deficiency or lack of blue color vision), however
they mention numbers from 1:1,000 to 1:65,000 people being affected. Fig-
ure 1 shows the estimated perceived color spectrum of affected observers
that was calculated with the software tool Color Oracle
2
presented by Jenny
and Kelso (2007). Color Oracle allows the user to filter the computer dis-
play's colors as if seen by a color vision deficient user. All simulations in this
paper were done using this software.
Figure 1. Estimated perceived color spectrum of deuteranopic (top), protanopic
(middle) and normal sighted (bottom) observers (Jenny and Kelso 2007).
Color vision deficient users take longer to find information in primarily
color coded graphics, if they can recognize it at all (Cole 2004). To counter
this in cartographic applications Jenny and Kelso (2007) advise to use safe
color combinations with annotations and additional non-color codings. For
the classification of line classes Jenny and Kelso (2007) suggest using dif-
ferent colors, labels and patterns. In a quantitative scheme a change of
stroke width might be appropriate, in a qualitative scheme this would sug-
gest a hierarchical order of likely non-hierarchical classes. Labeling lines
would help tremendously. For complex maps Jenny and Kelso (2007) also
suggest the use of different line patterns. A combination of multiple coding
2
http://colororacle.org/
styles with annotations would be advisable. Brewer (2005) notes that espe-
cially differences in lightness can help CVD affected users to differentiate
between colors. Zhang and Montag (2006), Wijffelaars et al. (2008), and
Ramathan and Dykes (2011) described studies that also evaluated and com-
pared color schemes. However they test colors used in choropleth maps,
images with 256 classes or just evaluated them from an aesthetic perspec-
tive and did not consider CVD. In contrast Brewer and Olson (1997) de-
scribe a study about color selection for users with CVD. Results show that
participants with CVD were as accurate as those with normal vision if ad-
justed colors were used. Olson & Brewer (1997) noted that previous re-
search on the topic of color confusion was based on „averages and on stimu-
li that are far different from maps”. Culp (2012) developed an algorithm
that facilitates a re-coloring of maps after publication for scenarios where
accessibility was not considered in the initial map design.
3. The openstreetmap.org Street Style
The openstreetmap.org (OSM) map uses a dynamic interface that allows the
user to change the map scale on 18 pre-defined zoom levels. Incrementing
the zoom level by one halves the map scale. openstreetmap.org allows the
user to choose different map styles. This paper deals with the “Standard”
style
3
The map style uses five distinctly colored street classes. These are, in de-
scending hierarchical order: “motorway” in blue, “trunk” in green, “prima-
ry” in red, “secondary” in orange and “tertiary” in light yellow (see Table 1).
This qualitative color scheme corresponds with street class colors tradition-
ally used in the United Kingdom. All these street classes are displayed as
continuous lines, on higher zoom levels with casings. Other street classes
such as “residential”, “living_street” or “road” are displayed in gray, white
with a gray border, or with different stroke styles. In the following the street
classes are named by their main color hue, as to ease understanding for the
reader: motorway is Blue, trunk is Green, primary is Red, secondary is Or-
ange and tertiary is Yellow.
which is the default general-use style (previously also known as
“Mapnik” style).
3
Displayed by default on http://www.openstreetmap.org, selectable by using the layer menu
in the upper right and choosing “Standard”
Original color scheme
Class
Adjusted color scheme
HSL value Color Color HSL value
152,86,160
Motorway (Blue)
168,86,144
85,103,181
Trunk (Green)
85,95,132
254,176,194
Primary (Red)
240,255,194
24,249,210
Secondary (Orange)
31,190,192
42,255,217
Tertiary (Yellow)
42,255,217
Table 1. HSL values and colors of the original and the adjusted color schemes.
The presentation of the street classes changes with the zoom level: Blue and
Green are displayed at zoom level 5 and above, Red at zoom level 7 and
above, Orange at zoom level 9 and above, Yellow at zoom level 10 and
above. The streets are also displayed with different stroke widths, generally
getting wider with an increasing zoom level. Additionally a casing in a dark-
er shade of the main color of the class is added at zoom level 12 and above.
The Orange class' color is changed to a slightly lighter shade at zoom level
12 when the casing is added. The color of the Yellow class is changed from a
gray to a light yellow with gray casing at zoom levels 13 and above.
Regarding problems for CVD affected users the following hypotheses were
formed based on analyzing an intersecting grid of colored shapes (Figure 2)
as well as map images (Figure 3) with Color Oracle:
The main problems are confusions and indistinguishability between the
classes. When displayed with a considerable stroke width and directly adja-
cent, Green and Red can be differentiated by both deutan and protan defi-
cient users, however the identification is problematic. If displayed with a
thin stroke width as on low zoom levels, these classes are indistinguishable
for deuteranopic users. Green and Orange are problematic for users with
protan deficiencies, regardless of zoom level or combination. Another prob-
lem for them is the Blue class in combination with Red on lower zoom lev-
els. The Yellow class can be distinguished by all users in all cases. Users
affected by tritan deficiencies should have no particular problems with the
existing street class color scheme.
For a normal sighted users the street class color scheme has no intuitive
hierarchical order. For deuteranopic or protanopic users however all street
colors apart from the Blue class have a seemingly similar green-yellow-
gray-brown-ish hue. The Red class then appears darker than Green, so if
the similar hue induces the color vision deficient user to interpret the colors
in a hierarchical order, then the user might falsely interpret the hierarchy as
Blue, Red, Green etc., with Red and Green interchanged.
Figure 2. The original street class colors arranged in a grid displayed in simulated
color blindnesses.
Figure 3. Example map data rendering using the original colors on zoom
level 13 in simulated color deficient vision (Map data: © OpenStreetMap
contributors, CC-BY-SA 2.0).
4. Adjusting the Color Scheme
To solve these problems we attempted to find colors which
• are distinguishable,
• allow the unambiguous identification of street classes,
• do not suggest a wrong hierarchy,
• closely resemble the original color scheme.
The HSL color space was used as its parameters hue, saturation and light-
ness resemble human color perception (Brewer 2005) and thus simplify the
application of appropriate color changes.
To account for the uncontrolled user environment the research was con-
ducted on a standard, uncalibrated TFT monitor. Findings were evaluated
on several other uncalibrated end-user displays, such as a netbook, a
smartphone and a tablet before the user study.
Colors were mixed in an iterative process, one HSL parameter was changed
at a time. Changes were focused on hue and lightness. For evaluation the
colors were visualized both in an intersecting grid (Figure 4) as well as ren-
dered with actual OSM data (Figure 5). The darker casings were created
using the adjusted color scheme's hue with a lightness difference equal to
that in the original scheme. Color Oracle was used to simulate CVD. The
color scheme’s appeal to a normal sighted observer was taken into account.
Table 1 shows the final color scheme.
Figure 4. The adjusted street class colors arranged in a grid displayed in simulat-
ed color blindnesses.
Figure 5. Example map data rendering using the adjusted colors on zoom
level 13 in simulated color deficient vision (Map data: © OpenStreetMap
contributors, CC-BY-SA 2.0).
5. Test Stimuli and Experimental Design
To test both the original and the adjusted color scheme a comprehensive
experiment was designed and conducted as online survey.
5.1. Street Combinations
Three relevant street class combinations were identified and tested, see
Figure 6 for an example of each:
A single street segment gives the user no opportunity to compare its
color to an adjacent or nearby segment of another, differently colored
street.
To test a combination of street classes, allowing the user to compare
colors of nearby streets, a combination of multiple unconnected streets was
displayed. They were connected by a white street in a circular crossing.
A network of highly connected streets allows the user to consider di-
rectly adjacent street colors for comparison purposes. Here at least one in-
stance of every street class was displayed and each class was connected at
least once with every other class. The style's rules as to which class was dis-
played “on top” another at crossings were not changed.
5.2. Zoom Levels
Four zoom levels were identified as being representative because of their
significant changes in contrast to the other tested zoom levels. The images
in Figure 6 show an example at each zoom level.
Starting at zoom level 7, the classes Blue, Green and Red are being dis-
played. The streets are displayed at a sub-pixel size with graphical filtering
leading to a slight alteration of color.
From zoom level 10 onwards, all five discussed street classes are being
displayed.
Starting with zoom level 13, the stroke widths of the lines are significantly
increased. There are no color changes past this zoom level in the original
color scheme. Casings were added at zoom level 12.
The styles of street classes are not altered past zoom level 17. The stroke
widths are at their maximum.
5.3. Tasks and Procedure
Suitable simple street courses were created and rendered in both the origi-
nal and adjusted color scheme using the same setup as openstreetmap.org.
The street courses were scaled to look the same at each tested zoom level.
The test images display street courses on the map style's neutral back-
ground color, a very light gray. Testing the colors on various backgrounds
such as green or brown would have required considerably more tests and
was not conducted. For the same reason no other map features were dis-
played in the images. Figure 6 shows examples.
To counter learning effects the test questions were displayed in randomized
order within the survey. The images were randomly rotated to prevent
recognition.
Figure 6. Examples of test images:
1 Highly connected street network, zoom level 7, original color scheme;
2 Single street segment, zoom level 10, adjusted color scheme;
3 Unconnected combination of streets A, zoom level 13, original color scheme;
4 Unconnected combination of streets B, zoom level 17, adjusted color scheme.
A simple legend was displayed showing the relevant street classes. Selected
streets in the images were labeled with letters and the user was asked to
pick the corresponding street class number from the legend. The user could
also choose the option “I cannot identify the class of the street reliably”. The
zoom level was not displayed so the user had no indication of the map's
scale. See Figure 7 for an example.
Participants were asked to participate in the survey under usual working
conditions.
Figure 7. Example of a question in the survey.
6. Results and Analysis
6.1. Participants
We recruited participants online in CVD communities as well as the OSM
community. 129 self-assessed CVD affected participants completed the sur-
vey. One third of the users stated a deuteranopic CVD: 21
% a
deuteranomaly, 9
% deuteranopia. 10
% stated to be affected by a
protanomaly, 5
% by protanopia. One user specified a tritanopic CVD (a
tritanomaly). 3
% of the participants said to be completely color blind. 37
%
chose the option “(...) red-green deficiency (I cannot specify)”.
75
% of the participants rated their ability to read maps to be good or better,
17
% as average. One third of the users stated never to have used
openstreetmap.org before. The remaining 67
% were evenly distributed be-
tween the remaining options from “rarely” to “all the time”.
6.2. Street Class Identification
Using the adjusted color scheme the identification rates generally im-
proved, in some cases dramatically. With the adjusted color scheme 90
% of
the answers were correct compared to 76
% with the original scheme. Below
the results are presented grouped by street class. Figures 8 to 10 show the
results and the absolute change in identification rate in diagrams.
Figure 8. Identification rates using the original color scheme, grouped by class
and zoom level.
Figure 9. Identification rates using the adjusted color scheme, grouped by class
and zoom level.
Figure 10. Absolute change in identification rate per class and zoom level between
original and adjusted color scheme.
The identification rate of the Blue class ranked 94
% and higher with no
significant changes between color schemes except for zoom level 7. On this
zoom level the identification rate deteriorated from a near perfect 91
% to
74
%. 16
% of the participants now falsely identified the class as Red. Anoth-
er less severe decline was observed for the street network on the same zoom
level. Here the identification rate of a Blue instance decreased from 98
% to
92
%.
The identification rate of the Green class was significantly improved. All
but one test resulted in identification rates of 91
% and higher. The excep-
tion being the single street segment at zoom level 7 with only 78
% (18
% up
from 60
% with the original color scheme).
Overall the Red class was slightly improved. While its identification rates
on the higher zoom levels 13 and 17 were already 93
% and higher with the
original color scheme (which was improved to 98
% and higher), the lower
zoom levels remain problematic. At zoom level 7 rates of 43-53
% were im-
proved to 73-87
%. At zoom level 10 identification rates stagnated or wors-
ened as much as 8
% to values between 51
% and 71
%. Here Red was often
confused with the gray of the Yellow class in the legend. Confusions with
Green were mostly eliminated.
Interestingly a difference of up to 18
% identification rate for Red occurred
between the two tested variations of unconnected street combinations at
zoom level 10. In a combination of Blue, Green, Red and Orange, 69
% cor-
rectly identified Red, while in a combination of Green, Red, Red and Or-
ange, only 58
% respectively 51
% identified it correctly. As only the signifi-
cant difference between the images is the addition of an instance of Blue,
this hints at the Blue class being used for comparative identification of Red.
The Orange class was identified without major problems (98-100
%) in
most cases. As with Red zoom level 10 turned out problematic. While im-
proved to an average of 94
% (+14
% from 80
%) in the combination images,
for the single segment this change occurred at a lower point: 67
% in the
original color scheme to 81
% in the new one. The street network improved
similarly to 89-91
% in the adjusted color scheme.
The Yellow class was expected to be unproblematic and thus not tested
well. It was tested in the combination images at zoom levels 13 and 17 plus
as single segment at zoom level 10. On the higher zoom levels the class was
displayed in its main color and was correctly identified by 95-100
% of par-
ticipants in both color schemes. At zoom level 10 however the identification
rate was only 53
% in the original color scheme and worsened to 42
% in the
new one. Here it was displayed in gray as defined in the original color
scheme. At this zoom level confusion with Red by 5
% of the participants
with the original scheme increased to 12% with the new one. With the misi-
dentification of Red as Yellow (as mentioned above) it is safe to say that the
new Red color is too similar the light gray of the Yellow class on zoom level
10.
6.3. Time Measurements
In addition to the improved identification rates with the adjusted color
scheme, we also measured a decrease in the times participants took to an-
swer the questions. Overall the median times were reduced by 10
% from
the original to the adjusted color scheme. Single street segments were iden-
tified 9
% faster, the combinations 6
% and the networks 13
% faster. The
only deterioration happened on zoom level 7 where Blue in the adjusted
color scheme took the participants 17
% longer than with the original
scheme. The largest improvements were recorded for the single Green class
on the zoom levels 10-17 where we observed a 26-27
% faster identification.
7. Discussion and Outlook
Given the results of the survey, adjusting an existing color scheme to better
accommodate CVD affected users showed to be very well possible, albeit
complex. The remaining problems on the lower zoom levels could be solved
in the future by adjusting colors only on and especially for these zoom lev-
els.
The test's distinction between the combination of street instances both con-
nected and unconnected showed to have been redundant for the most part.
The results for zoom level 13 and 17 showed to be very similar with a slight-
ly higher average identification rate for zoom level 17. Testing zoom level 17
in addition to 13 can be considered redundant based on this.
The low scores for some of the single street segments should not be over-
stated. It is not likely for streets to be shown isolated in the map. In hind-
sight these questions might have been an unrealistic case in a complex
street map.
The results for neither OSM users nor participants from the United King-
dom show a significant difference to the rest, so a bias from being previous-
ly exposed to the original color scheme or a similar one can be ruled out. In
future studies these demographic information would not need to be asked
for.
The research was limited to color changes. Several participants mentioned
using the street's widths in their decision making. Adjusting these to be
more distinct might be a simple step towards easier identification, especial-
ly on the lower zoom levels.
The OSM map style is an organically grown style with a wide selection of
displayed features such as differently colored kinds of rural and urban land
use. In our case only few selected feature classes of a highly complex map
style were analyzed in an isolated environment, ignoring the multitude of
different possible background colors and occurring contrast. The so-called
effect of simultaneous contrast has been described by Albers (1970),
Monmonier (1996), Brewer (1997) and Lyons (2000) and could give further
insights for the adaption of the colors.
Utilizing the concept of confusion lines (as done by Olson and Brewer 1997)
would allow for better identification of problematic color combinations and
appropriate color adjustments. Using a perceptually uniform color space
like CIE L*a*b would allow to maximize contrast between colors by measur-
ing color distances. Another approach by Steinrücken et al. (2013) maxim-
izes the minimal distance between colors by formulating an optimization
model that considers cartographic guidelines. The authors state that there-
by it is possible to select clearly distinguishable colors.
Designing a completely new color scheme for OSM focused on color vision
and accessibility might be easier than carefully adjusting colors to suit the
needs of CVD affected users whilst not significantly changing the overall
appearance of an existing scheme. Also the visual appeal to normal sighted
observers could be neglected.
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