Danielle Lee- No One Wants to Look at Your Ugly Map

Image courtesy of: http://aschmann.net/AmEng, discovered through http://cartastrophe.wordpress.com/
Danielle Lee, GIS Specialist at AE2S
NO ONE WANTS TO LOOK AT
YOUR UGLY MAP
WI Land Information Association Annual Conference | February 13, 2014
OK, that’s a little harsh…
…but making a nice looking
map really is important.
•
Your presentation
decisions communicate
what is important and
how seriously you should
be taken.
Image courtesy of: http://undertheraedar.blogspot.com/2013/08/naturalearth-for-gis-data.html
A little cartography can go a long way.
Phosphorus Yield Distribution for the Mendota Watershed in lbs/acre
¯
1
2
4
3
Columbia County
46
12
45
44
9
49
15 21
29
30
34
57 56
59
31
94
81
39 84
62
68
65
35
36
85
87
71
73
82
83
70
58
28
64
66 93
80
20
63
60
61
67
18
54
55
25
69
40
47
13
24
37
38
10
53
23
22
4
Miles
11
52
51
32
33
2
17
19
27
7
48
50
16
14
1
6
43
Dane County
26
0
5
72
95
91
89
88
97 90
74
78
86
92
96
77
75
79
98 76
41
42
Legend
lmsw
Phosphorus Yield
0.00 - 0.17
0.18 - 0.40
0.41 - 0.60
0.61 - 0.92
0.93 - 1.27
1.28 - 1.67
Montgomery Associates: Resource Solutions, LLC
119 South Main St., Suite A, Cottage Grove, WI 53527
October 22, 2009
Developed by DYL & AEK
Always Get the Basics Down
•
•
Image courtesy of: http://webhelp.esri.com/arcgisdesktop/9.2/printBooks_topics.cfm?pid=22
Can your map stand
alone?
What are you trying to
say? And to who?
Matching Map Type to Data Type
Image courtesy of: “Thematic Cartography and Geovisualization” Figure 5.1
•
•
Are my data continuous, or
•
discrete?
Are my data smooth or abrupt?
Is the phenomenon occurring at
a point? Over an area? A
surface? A volume?
Matching Map Type to Data Type
Image courtesy of: “Thematic Cartography and Geovisualization” Figure 5.10
Choropleth Maps
•
•
–
+
Good for continuous, abrupt data
that matches enumeration unit.
Do not show raw numbers; only
normalized data.
Assumes uniformity throughout
enumeration unit, and that the
enumeration unit is important. Size
can also be misleading: importance.
Easy to understand and see trends
quickly.
Image courtesy of: http://en.wikipedia.org/wiki/United_States_presidential_election_in_Wisconsin,_2008,
Isopleth Maps
•
•
Lines of equal interval for any
surface.
Good for continuous, smooth
data.
–
Accuracy depends on # control
points. Interpolation methods
vary, and intervals can be
subjective.
+
Good for seeing spatial patterns,
elegant and easy to understand.
Images courtesy of: http://www.fao.org/docrep/003/t0446e/t0446e06.htm,
http://mapanalysisib.blogspot.com/2013_07_01_archive.html
Proportional Symbol Maps
•
•
Symbol size increases with
value.
Good for discrete, abrupt data.
–
Complicated overlap, and can
be tough to get specific values.
Humans are bad at estimating
size (Ebbinghaus Illusion).
+
Easily shows relative size quickly.
Compact symbols match to
location well.
Images courtesy of: http://listverse.com/2007/09/16/20-amazing-optical-illusions/,
http://makingmaps.files.wordpress.com/2007/08/circle-choro1.jpg
Dot Maps
•
•
Good for discrete, smooth
data.
Can be 1-1 or 1-many,
actual dot placement may
be random.
–
Hard to get specific values.
Bad for uniform distributions.
+
Great to show density
relationships and overall
distribution, easy to
understand.
Images courtesy of: http://www.cdc.gov/lyme/stats/maps/interactivemaps.html,
Encoding Information in Multiple Ways
•
•
Image courtesy of: http://understandinggraphics.com/visualizations/information-display-tips/
Bertin’s Visual
Variables
I usually choose
multiple ways to
encode
information.
Problems with Color
•
•
•
•
•
Color is subjective.
Lighting conditions differ.
Computer Monitors vs.
Printers
Color vision impairment
Simultaneous Contrast
Adelson’s Checker Shadow Illusion
Images courtesy of: http://en.wikipedia.org/wiki/Color_blindness, http://listverse.com/2007/09/16/20-amazing-optical-illusions/
Color Brewer: http://colorbrewer2.org
Matching Color to Data Type
Sequential
example: # car
owners per square
mile
Diverging
example: % votes for
Democratic or
Republican candidate
Qualitative
example: most popular
pet by county
Shades of Grey Are Your Friend
•
•
•
Don’t use “all the crayons
in the box.”
“Grey down” or soften the
layers that don’t need to
pop.
De-emphasize the base
layer by upping the
transparency or increasing
brightness.
Images courtesy of: http://godizcandy.blogspot.com/2012_02_01_archive.html,
http://mappingcenter.esri.com/index.cfm?fa=ask.answers&q=2163
Shades of Grey Are Your Friend
•
•
•
Reserve the color black just
for the most important text.
High contrast is taxing.
Stroke labels with a shade of
grey, or give them a halo.
Recommend 1-1.5 point.
Map Text
vs
vs
Map Text
Danielle’s Top 10 Tips
10
Tidy up. Remove all
information that you don’t
need.
9
Consider whitespace and
using a grid to organize
map elements.
Image courtesy of: http://resources.arcgis.com/en/help/main/10.2/index.html#//00s900000007000000
Danielle’s Top 10 Tips
8
Use a sans serif typeface. Going beyond Arial
really makes a difference. Font sizes: go small.
7
Take the time to craft good templates and keep
them updated. Don’t reinvent the wheel.
Comic Sans
Century Gothic
Calibri
Gill Sans
Image courtesy of: http://triggersandsparks.com/blog/seven-simple-steps-to-better-design-sans-designer/
Danielle’s Top 10 Tips
6
Label things clearly and
repeatedly.
5
Thinner line types are less
overpowering, and more
elegant.
4
Good design can be done in
any medium. You don’t need
Adobe Illustrator.
Images courtesy of: http://upload.wikimedia.org/wikipedia/commons/d/d8/Adobe_Illustrator_Icon_CS6.png,
http://www.idsketching.com/thesketchlab/sketch-basics/
Danielle’s Top 10 Tips
3
2
Be extremely consistent in
your visual hierarchy. Save
labels and symbology.
Always have someone
proof your map. Ask them
what they see first, as it’s
often a good indication of
what is most emphasized.
Be able to defend each of
your map design choices.
Danielle’s Top 10 Tips
1
Constructively critique maps and look to the masters for inspiration.
(USGS, National Geographic, New York Times)
Images courtesy of: http://bits.blogs.nytimes.com/2014/01/06/a-makeover-for-maps, http://nationalmap.gov/ustopo/,
http://blog.dwtkns.com/2011/generic-stream-terms/, http://www.wired.com/wiredscience/2013/08/fictional-koana-islands-maps/
A little cartography can go a long way.
Phosphorus Yield Distribution for the Mendota Watershed in lbs/acre
¯
1
2
4
3
Columbia County
46
12
45
44
9
49
15 21
29
30
34
57 56
59
31
94
81
39 84
62
68
65
35
36
85
87
71
73
82
83
70
58
28
64
66 93
80
20
63
60
61
67
18
54
55
25
69
40
47
13
24
37
38
10
53
23
22
4
Miles
11
52
51
32
33
2
17
19
27
7
48
50
16
14
1
6
43
Dane County
26
0
5
72
95
91
89
88
97 90
74
78
86
92
96
77
75
79
98 76
41
42
Legend
lmsw
Phosphorus Yield
0.00 - 0.17
0.18 - 0.40
0.41 - 0.60
0.61 - 0.92
0.93 - 1.27
1.28 - 1.67
Montgomery Associates: Resource Solutions, LLC
119 South Main St., Suite A, Cottage Grove, WI 53527
October 22, 2009
Developed by DYL & AEK
Thanks! Feel free to get in touch.
Danielle Lee
GIS Specialist at AE2S
Located in Madison, WI
[email protected]
http://www.ae2s.com/gisblog/
Image courtesy of:
http://maps.stamen.com/watercolor/#12/
43.1005/-89.3266

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