Download Data Visualization - Christina Friedle

DATA VISUALIZATION
Introduction to GIS| Winter 2015
Understanding your map data
Qualitative vs. Quantitative Data
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Qualitative
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Quantitative
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Data classified by category
e.g. Soil types, Animal Species
Data grouped by numerical value
e.g. Population (# of people), Forest cover (%)
Why is understanding this so important?

Data type will influence your choice of map symbolization and
visualization
Showing qualitative data on maps
Features
Features (Showing Location)
Each geographic feature is represented by a single color
Categories—Unique Values
Categories (Unique Values)
Each geographic feature is represented by a different color
Categories—Grouped Values
Categories (Unique Values – Grouped)
Geographic features are grouped and each group is represented by a color
Showing quantitative data on maps
Quantitative Map Types
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Choropleth (Classification)
Isoline
Cartogram
Density
3D Visualization
Choropleth Maps
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Widely-used method for showing quantitative data
Based on numeric attributes of non-overlapping
areas
Areas are shaded based on the value of the
attribute
“Spatially-sensitive” values should be normalized

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Convert “raw” count data to ratio (density); this is done
by dividing raw data by another attribute (e.g. area)
e.g. Raw count data: Total Population per County
e.g. Density data: Population/mi2 per County
Choropleth Maps |Graduated Colors
Graduated
Colors in Arc
is used for
creating a
Choropleth
Map
How do
these %
values get
grouped
into
classes?
Choropleth maps | Classification


Different ways to show how data is grouped into
classes
Classification goals:



Make the map easy to read and understand
Communicate info that is not self-evident about an
area
Cartographer must decide which method to use…
Natural Breaks (Jenks)


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
Default in ArcGIS and
appropriate for most data
Classes are based on
natural groupings of data
Statistical method that
minimizes the total variance
within each group (i.e.
similar things are put into
the same group)
Cartographer sets number
of classes; ArcMap finds
natural groupings
Natural Breaks
Cartographer
chooses
classes
Quantile method



Each class contains
(approx) the same # of
features
Best suited for data that is
uniformly distributed; data
that does not have a
disproportionate number of
features with similar values
Cartographer sets number
of classes; ArcMap assigns
data to each class
Quantile method
Cartographer
sets classes,
Arc assigns
values
Each class has
50 values,
regardless of
the range of
variance
Interval nethods


Equal Intervals: Features are
divided equally into a set number
of intervals. Cartographer sets
number of intervals (e.g. 3);
ArcMap calculates interval range
(e.g. 0-5, 6-10, 11-15 etc.) and
assigns data to each interval.
Defined Intervals: Features are
divided based on a set interval
range. Cartographer sets
intervals (e.g. 0-7, 7-9, 9-15);
ArcMap assigns data to each
interval.
Equal Interval method
Cartographer
sets the
number of
intervals
Arc calculates
interval range,
in this case
intervals of 23
Defined Interval method
Arc assigns
classes
needed to
accommodate
chosen
interval
Cartographer
sets interval
size
Standard Deviation method

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
Shows distance from the
data mean (average)
Places class breaks at
intervals (1/4, 1/5, or 1)
away from mean, based
on the type of standard
deviation
Cartographer sets type;
ArcMap assigns data to
intervals
Standard Deviation method
Arc assigns
classes
needed to
accommodate
standard
deviation
chosen
Cartographer
sets standard
deviation
desired
Normal Distribution Curve
Which method(s) are appropriate for data values
that naturally “clump” into different groups?
Which method(s) are appropriate for data that has
uniformly distributed values?
Which method(s) are appropriate for data values
that follows a normal distribution?
Classification Methods
Natural
groupings of
features into
classes
Class
values
are
equally
spaced
Features
classified by
distance
from mean
Same # of
features in
each class
Normalizing Data


When geographic features vary in size, then data
should be displayed as densities; i.e. “Spatiallysensitive” values should be normalized
“Raw count” data converted to a ratio (density)


Divide raw data by another attribute (e.g. area)
Let’s look at a visual example…
If Raw count data is Total Population per State,
then…
The corresponding density is Population per mi2 (by
State)
Isoline Maps
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Lines joining points of equal value (usually
generalized)
Used for continuous surfaces; phenomena must vary
smoothly across the map
Common example is contour lines… but isolines can
show other data besides elevation!
Two types:

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Isometric (measured values)
Isopleth (areal averages)
Example - Isometric map
Example - Isopleth map
Cartograms
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Distort area, shape or distance of map symbols for
a specific purpose
Reveal or enhance patterns that might not be
visually apparent on a “normal” map
Sometimes used to promote legibility
National Debt compared to GDP, by nation
Source: Jonathan Crowe, http://www.mcwetboy.net/
Density Maps
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Uniform symbols repeated across map
Exact quantities not shown
Instead, goal is to give an overall impression of
data distribution across an area (i.e. density)
Distribution of symbols represents distribution of
data
3D Visualization

Can be created with a wide variety of tools
 Google
Earth
 ArcScene
 Sketch up

Data needs to have a Z-factor (x,y,z)
3D Visualization
3D Visualization
Temporal Visualization
GAPMINDER WORLD
Hans Rosling – Statistics shown using
Graphs & Maps
http://www.gapminder.org/world
Interactive/Crowd-Sourced Visualizations
TED Talk: Aaron Koblin
“Artfully visualizing our humanity”
www.ted.com/talks/aaron_koblin.html
www.aaronkoblin.com/work/flightpatterns