chapter 4 · 1/4/2017 · 1/30/2017 5 map types and data types •single symbol maps •unique...
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Chapter 4
Mapping GIS Data
Copyright © McGraw-Hill Education. Permission required for reproduction or display.
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Outline
• GIS Concepts
– Symbolizing features
– Ways to map data
– Displaying rasters
– Understanding MAUP
– Classifying numeric data
• About ArcGIS
– Dynamic labels
– Symbolizing features
– Symbolizing rasters
Copyright © 2015 by Maribeth H. Price
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Symbolizing features
Copyright © 2015 by Maribeth H. Price
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Differentiating map objects
• Changes in category: vary shape, line type, pattern, color, font
• Changes in quantity: vary size, thickness, color
Copyright © 2015 by Maribeth H. Price
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Using color• May be used for
category or quantity
• HSV color model– Vary hue for changes in
category
– Vary saturation and/or value for changes in quantity
– Vary both for divergent quantities, such as negative/positive or cold/warm values
Copyright © 2015 by Maribeth H. Price
Category Quantity Divergence
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Making symbols effective
• Convention
• Connotation
• Color blindness
Copyright © 2015 by Maribeth H. Price
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Color and quantity
• Rainbow ramps rarely work for polygons
• Better for continuous rasters
Copyright © 2015 by Maribeth H. Price
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2008 Election
Copyright © 2015 by Maribeth H. Price
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Black & White maps
• Occasionally it is desirable to design a map in black and white
– Less expensive to copy or print
– Journals still charge more for color
– Better if map is likely to be faxed or photocopied
* Challenging, but often maps get copied into black and white reports, so know your audience
Copyright © 2015 by Maribeth H. Price
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Designing B&W maps• Special considerations
– Design in B&W
– Use no more than 5 gray levels
– Use different patterns instead of colors
– B&W maps may not be able to show as much detail
Copyright © 2015 by Maribeth H. Price
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Copyright © 2015 by Maribeth H. Price
Geologic map for color printing
Geologic map for B&W printing
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Ways to map data
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Map Types and Data Types
• Single symbol maps
• Unique values maps
• Quantities maps
– Graduated color
– Graduated symbol
– Dot density
• Nominal data
• Categorical data
• Ordinal data
• Interval and Ratio data
Copyright © 2015 by Maribeth H. Price
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Nominal data
• Names or uniquely identifies objects– State names
– Owner of parcel
– Tax ID number
– Parcel ID Number
• Each feature likely to have its own value
• Usually portrayed on a map as labels
Copyright © 2015 by Maribeth H. Price
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Single symbol maps
• Display all features with the same symbol
• Combine with labels to portray nominal data
Copyright © 2015 by Maribeth H. Price
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Categorical data
• Places features into defined number of distinct categories
• Category names may be text or numeric
• Portrayed by different symbol for each category
Copyright © 2015 by Maribeth H. Price
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Unique values maps
• Different symbol for each category or value
Copyright © 2015 by Maribeth H. Price
Geologic unitsVolcano types Road types
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Ordinal data
• Type of categorical data
• Ranks categories along an arbitrary scale– Low, Medium, High slope
– Village, Town, City
– Grades: A, B, C, D, F
– Rank of Best City to Live In: 1, 2, 3…
Copyright © 2015 by Maribeth H. Price
A 0-40%
B 40-70%
C 70-100%
Portrayed as categories but choosing variations in symbol size or color to indicate increase
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Interval or Ratio data
• Interval data places values along a regular numeric scale– Supports addition/subtraction
– Temperature, pH, elevation
• Ratio data places values along a regular scale with a meaningful zero point– Supports addition, subtraction, multiplication,
division
– Population, rainfall, median rent
Copyright © 2015 by Maribeth H. Price
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Eg: Interval Data
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Eg: Ratio Data
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Eg: Ratio Data
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Mapping numeric data
• Interval and ratio data must be divided into classes before mapping
• Mapped using variations in symbol size, thickness, or hue
Copyright © 2015 by Maribeth H. Price
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Classed maps
Copyright © 2015 by Maribeth H. Price
Graduated color map(choropleth map) Graduated symbol map
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Colors for choropleth maps
• Use change in saturation or value to indicate larger quantities
• Avoid using too many colors (hues) which tend to mask high versus low values
Copyright © 2015 by Maribeth H. Price
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Eg: Choropleth Map
Copyright © 2015 by Maribeth H. Price
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Normalizing classed maps
• If the size of the sample impacts the measured value, data should be normalized– By percent of total
• Percent of farms in each state
• Percent of mobile homes in each state
– By another field• Farms divided by area
• Mobile homes divided by total housing units
Copyright © 2015 by Maribeth H. Price
Number of farms
Number of farms per sq. mile
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Eg: Normalizing Data
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Unclassed maps
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Proportional symbol map Dot density map
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Chart Maps
Copyright © 2015 by Maribeth H. Price
Proportional chart map 4-31
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Understanding theModifiable Areal Unit Problem
(MAUP)
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Modifiable Areal Unit Problem
• Arbitrary aggregation units like states or counties may influence values
– Number of farms affected by size of state
– Number of vacant houses affected by population
Copyright © 2015 by Maribeth H. Price
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Minimizing MAUP
• Normalize farms by area of state
Copyright © 2015 by Maribeth H. Price
Number of farms Density of farms / mi2
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Minimizing MAUP
• Normalize vacancies by number of housing units
Copyright © 2015 by Maribeth H. Price
Number of vacancies Fraction of homes vacant
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Normalizing by field percentage
• Divide each value by total of column
Copyright © 2015 by Maribeth H. Price
Percentage of Congress controlled by each state
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Visual MAUP issue
• Large polygons tend to dominate the map
• More prominent, attract greater attention
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Reducing visual MAUP
• Use a graduated symbol or dot density map
Copyright © 2015 by Maribeth H. Price
Number of farms
Density of farms/mi2 4-38