introduction to geographic information systems fall 2013 (inf 385t-28620) dr. david arctur

Introduction to Geographic Information Systems Fall 2013 (INF 385T-28620)

Dr. David ArcturResearch Fellow, Adjunct Faculty

University of Texas at Austin

Lecture 2Sept 5, 2013

Map Design

Outline Choropleth maps Colors Vector GIS display GIS queries Map layers and scale thresholds Hyperlinks and map tips

INF385T(28620) – Fall 2013 – Lecture 2 2

CHOROPLETH MAPSLecture 2

Choropleth maps 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

INF385T(28620) – Fall 2013 – Lecture 2

Classifying dataProcess of placing data into groups

(classes orbins) 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 valuesINF385T(28620) – Fall 2013 – Lecture 2 6

Break points

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


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%


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.


U.S. population by state, 2000

12INF385T(28620) – Fall 2013 – Lecture 2

Original map (natural breaks)

Not good because too many values fall into low classes


Equal interval scale

Shows that an increasing width (geometric) scale is needed


Quantile scale

Custom geometric scale Experiment with exponential scales with

powers of 2 or 3.


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Beware empty statistics

http://xkcd.com/1138


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


Normalizing data

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Nonnormalized dataNumber of vacant housing units by state,

2000


Normalized data

Percentage vacant housing units by state, 2000


California population by county, 2007


Nonnormalized data


California population density, 2007

Normalized data

COLORSLecture 2


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Hue is the basic color

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


Color overview

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Device that provides guidance in choosing colors

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


Color wheel

Light colors associated with low values Dark colors associated with high values Human eye is drawn to dark colors


Light vs. dark colors

ContrastThe greater the difference in value

between an object and its background, the greater

the contrast


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Monochromatic color scale 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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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)


Dichromatic color scale


Symmetric break points centered on 0 make it easy to interpret the map

Dichromatic map

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Color tips Colors have meaning

Political and cultural Cool colors

Calming Appear smaller Recede

Warm colors Exciting Overpower cool colors


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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


Color tips

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Graphics for colorblind users


Two-meter air temperature anomalies (i.e., differences from the 1971–2000 mean) for January 1998 (during a recent El Niño):

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Graphics for colorblind users


Two-meter air temperature anomalies (i.e., differences from the 1971–2000 mean) for January 1998 (during a recent El Niño):

VECTOR & RASTER DATALecture 2


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Points, lines, polygons Point

x,y coordinates Line

starting and ending point and may have additional shape vertices (points)

Polygon three or more lines joined to form a closed

area


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Feature attribute tables Store characteristics for vector features Layers can be displayed using

attributes


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Displaying points Single symbols All CAD calls


Displaying points Same features, different points Based on attributes


Displaying points Industry specific (e.g. crime analysis) Good for large scale (zoomed in) maps


Displaying points Industry specific (e.g. schools)

Not good for multiple features at smaller scales

Simple points better for analysis


Displaying points Quantities

Use exaggerated sizes


Displaying linesFor analytical maps, most lines are

groundfeatures and should be light shades (e.g.

grayor light brown)


Displaying linesConsider using dashed lines to signify

lessimportant line features and solid lines for

theimportant ones


Displaying polygonsConsider using no outline or dark gray forboundaries of most polygons

Dark gray makes the polygons prominent enough, but not so much that they compete for attention with more important graphic features


Displaying polygonsConsider using texture for black and

whitecopies


Assign bright colors (red, orange, yellow, green, blue) to important graphic elements

Features are known as figure

All features in figure


Graphic hierarchy

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


Graphic hierarchy

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


Graphic hierarchy

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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


GIS QUERIESLecture 2


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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


GIS queries

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Simple query criterion <data attribute>< logical operator><value> NatureCode ='DRUGS' DATE >= '20040701'

% 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)


Simple attribute queries


Simple attribute queries

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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


Compound attribute queries


Compound attribute queries

LAYER GROUPS, SCALE THRESHOLDS

Lecture 2

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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)INF385T(28620) – Fall 2013 – Lecture 2

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Large Scale vs. Small ScaleWhich is larger scale:

zoomed in (small areas appear large) or zoomed out (large areas appear small)?

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 scale1:50,000,000 is small scale

Layer groups Organizes layers Groups and names logically


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Minimum scale threshold 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


HYPERLINKS AND MAP TIPSLecture 2

Links images, documents, Web pages, etc. to features on a map


Hyperlinks

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Map tips Provide an additional way to find

information about map features Pop up as you hover the mouse pointer

over a feature


Summary Choropleth maps Colors Vector GIS display GIS queries Map layers and scale thresholds Hyperlinks and Map tips


introduction to geographic information systems fall 2013 (inf 385t-28620) dr. david arctur

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