lecture 2 ppt

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Introduction to Geographic Information Systems
Spring 2013 (INF 385T-28437)
Dr. David Arctur
Lecturer, Research Fellow
University of Texas at Austin
Lecture 2
Jan 24, 2013
Map Design
Outline
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Choropleth maps
Colors
Vector GIS display
GIS queries
Map layers and scale thresholds
Hyperlinks and map tips
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Lecture 2
CHOROPLETH MAPS
Choropleth maps
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Color-coded polygon maps
Use monochromatic scales or saturated
colors
Represent numeric values (e.g. population,
number of housing units, percentage of
vacancies)
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Choropleth map example

Percentage of vacant housing units by
county
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Classifying data
Process of placing data into groups (classes or
bins) that have a similar characteristic or value

Break points
 Breaks the total attribute
range up into these intervals
 Keep the number of intervals
as small as possible (5-7)
 Use a mathematical progression
or formula instead of picking
arbitrary values
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Break
points
6
Classifications

Natural breaks (Jenks)
 Picks breaks that best group similar values
together naturally and maximizes the differences
between classes
 Generally, there are relatively large jumps in
value between classes and classes are uneven
 Based on a subjective decision and is the best
choice for combining similar values
 Class ranges specific to the individual dataset,
thus it is difficult to compare a map with another
map
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Classifications

Quantiles
 Places the same number of data values in each
class
 Will never have empty classes or classes with
too few or too many values
 Attractive in that this method produces distinct
map patterns
 Analysts use because they provide information
about the shape of the distribution.
 Example: 0–25%, 25%–50%, 50%–75%,75%–
100%
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Classifications

Equal intervals
 Divides a set of attribute values into groups that
contain an equal range of values
 Best communicates with continuous set of data
 Easy to accomplish and read
 Not good for clustered data

Produces map with many features in one or two classes
and some classes with no features
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Classifications
Use mathematical formulas when possible.

Exponential scales
 Popular method of increasing intervals
 Use break values that are powers such as 2n or
3n
 Generally start out with zero as an additional
class if that value appears in your data
 Example: 0, 1–2, 3–4, 5–8, 9–16, and so forth
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Classifications
Use mathematical formulas when possible

Increasing interval widths
 Long-tailed distributions
 Data distributions deviate from a bell-shaped
curve and most often are skewed to the right
with the right tail elongated
 Example: Keep doubling the interval of each
category, 0–5, 5–15, 15–35, 35–75 have interval
widths of 5, 10, 20, and 40.
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Original map (natural breaks)
U.S. population by state, 2000
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Equal interval scale
Not good because too many values fall into low classes
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Quantile scale
Shows that an increasing width (geometric) scale is needed
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Custom geometric scale

Experiment with exponential scales with powers
of 2 or 3.
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Beware
empty
statistics
http://xkcd.com/1138
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Normalizing data
Divides one numeric attribute by another in order to
minimize differences in values based on the size of
areas or number of features in each area
Examples:
 Dividing the number of vacant housing units by the
total number of housing units yields the percentage
of vacant units
 Dividing the population by area of the feature yields
a population density
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Nonnormalized data
Number of vacant housing units by state, 2000
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Normalized data
Percentage vacant housing units by state, 2000
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Nonnormalized data
California population by county, 2007
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Normalized data
California population density, 2007
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Lecture 2
COLORS
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Color overview
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Hue is the basic color
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Value is the amount of white or black in the color

Saturation refers to a color scale that ranges
from a pure hue to gray or black
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Color wheel
Device that provides guidance in choosing colors


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Use opposite colors to
differentiate graphic features
Three or four colors equally
spaced around the wheel are
good choices for differentiating
graphic features
Use adjacent colors for
harmony, such as blue, blue
green, and green or red, red
orange, and orange
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Light vs. dark colors
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Light colors associated with low values
Dark colors associated with high values
Human eye is drawn to dark colors
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Contrast
The greater the difference in value between an
object and its background, the greater the
contrast
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Monochromatic color scale
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Series of colors of the same hue with color
value varied from low to high
Common for choropleth maps
The darker the color in a monochromatic
scale, the more important the graphic feature
Use more light shades of a hue than dark
shades in monochromatic scales
 The human eye can better differentiate among
light shades than dark shades
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Monochromatic map
Values too similar
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Monochromatic map
A better map, more contrast
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Dichromatic color scale
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An exception to the typical monochromatic
scale used in most choropleth maps
Two monochromatic scales joined together
with a low color value in the center, with color
value increasing toward both ends
Uses a natural middle point of a scale, such as
0 for some quantities (profits and losses,
increases and decreases)
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Dichromatic map
Symmetric break points centered on 0 make it easy to
interpret the map
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Color tips
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Colors have meaning
 Political and cultural

Cool colors
 Calming
 Appear smaller
 Recede

Warm colors
 Exciting
 Overpower cool colors
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Color tips


Do not use all of the colors of the color
spectrum, as seen from a prism or in a
rainbow, for color coding
If you have relatively few points in a point
layer, or if a user will normally be zoomed in
to view parts of your map, use size instead
of color value to symbolize a numeric
attribute
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Lecture 2
VECTOR & RASTER DATA
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Points, lines, polygons

Point
 x,y coordinates
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Line
 starting and ending point and may have
additional shape vertices (points)
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Polygon
 three or more lines joined to form a closed area
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Feature attribute tables
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Store characteristics for vector features
Layers can be displayed using attributes
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Displaying points
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Single symbols
All CAD calls
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Displaying points
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Same features, different points
Based on attributes
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Displaying points
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Industry specific (e.g. crime analysis)
Good for large scale (zoomed in) maps
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Displaying points
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Industry specific (e.g. schools)
 Not good for multiple features at smaller scales
 Simple points better for analysis
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Displaying points
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Quantities
 Use exaggerated sizes
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Displaying lines
For analytical maps, most lines are ground
features and should be light shades (e.g. gray
or light brown)
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Displaying lines
Consider using dashed lines to signify less
important line features and solid lines for the
important ones
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Displaying polygons
Consider using no outline or dark gray for
boundaries of most polygons
 Dark gray makes the polygons prominent
enough, but not so much that they compete for
attention with more important graphic features
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Displaying polygons
Consider using texture for black and white
copies
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Graphic hierarchy
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Assign bright colors (red, orange, yellow, green,
blue) to important graphic elements
Features are known as figure
All features in figure
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Graphic hierarchy
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Assign drab colors to the graphic elements that
provide orientation or context, especially shades
of gray
Features known as ground
Circles in figure, squares and lines in
ground
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Graphic hierarchy
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
Place a strong boundary, such as a heavy black
line, around polygons that are important to
increase figure
Use a coarse, heavy cross-hatch or pattern to
make some polygons important, placing them in
figure
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Graphic hierarchy example
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Vector – Raster Comparison
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Vector data example
Bolstad, Fig 2-26a
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Raster data example 1
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Raster data example 2
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Converting between vector & raster
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Lecture 2
GIS QUERIES
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GIS queries
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Powerful relationship between data table
and vector-based graphics—unique to GIS
Records from a feature attribute table are
selected by using query criteria
Query will automatically highlight the
corresponding graphic features
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Simple attribute queries
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Simple query criterion
 <data attribute>< logical operator><value>
 NatureCode ='DRUGS'
 DATE >= '20040701'
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% wild card
 % symbol stands for zero, one, or more characters
of any kind
 NAME like ' BUR%'
 Selects any crime with names starting with the
letters BUR, including burglaries (BUR), business
burglaries(BURBUS), and residential burglaries
(BURRES)
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Simple attribute queries
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Compound attribute queries

Compound query criteria
 Combine two or more simple queries with the
logical connectives AND or OR
 "NATURE_COD" = 'DRUGS' AND "DATE" >
20040801
 Selects records that satisfy both criteria
simultaneously
 Result are drug crimes that were committed after
August 1, 2004
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Compound attribute queries
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Lecture 2
LAYER GROUPS, SCALE
THRESHOLDS
First: What is Scale?
Q. What does it mean when a map says
Scale 1:2 million
(1 inch on map = 2 million inches on land)
Q. How about Scale 1:63,360
(1 inch = 1 mile)
(5280 ft x 12 in/ft = 63,360 in)
Q. How about Scale 1:1
(actual size)
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Large Scale vs. Small Scale
Which is larger scale: zoomed in (see small area)
or zoomed out (see large area)?
Which is larger: 1/400 or 1/20,000?
Which is larger scale? 1:400 or 1:20,000?
When we say Scale 1:n, what we’re saying is that
each feature on the map is 1/n of its real size.
So small denominator = LARGE scale (zoomed in)
and large denominator = SMALL scale (zoomed out)
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Map scales
1:5,000 is large scale
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1:50,000,000 is small scale
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Layer groups
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Organizes layers
Groups and names logically
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Minimum scale threshold
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When zoomed out beyond this scale,
features will not be visible
 Tracts not visible when zoomed to the USA
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Minimum scale threshold
 Tracts displayed when zoomed in
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Maximum scale threshold

When zoomed in, features will not be visible
 State population will disappear when zoomed in
to a state
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Lecture 2
HYPERLINKS AND MAP TIPS
Hyperlinks

Links images, documents, Web pages, etc.
to features on a map
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Map tips
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Provide an additional way to find information
about map features
Pop up as you hover the mouse pointer over
a feature
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Summary
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
Choropleth maps
Colors
Vector GIS display
GIS queries
Map layers and scale thresholds
Hyperlinks and Map tips
INF385T(28437) – Spring 2013 – Lecture 2
75
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