Chapter 4 · 1/4/2017 · 1/30/2017 5 Map Types and Data Types •Single symbol maps •Unique...

1/30/2017

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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


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Differentiating map objects

• Changes in category: vary shape, line type, pattern, color, font

• Changes in quantity: vary size, thickness, color


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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


Category Quantity Divergence

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Making symbols effective

• Convention

• Connotation

• Color blindness


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Color and quantity

• Rainbow ramps rarely work for polygons

• Better for continuous rasters


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2008 Election


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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


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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


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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


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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


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Single symbol maps

• Display all features with the same symbol

• Combine with labels to portray nominal data


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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


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Unique values maps

• Different symbol for each category or value


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…


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


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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


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Classed maps


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


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Eg: Choropleth Map


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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


Number of farms

Number of farms per sq. mile

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Eg: Normalizing Data


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Unclassed maps


Proportional symbol map Dot density map

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Chart Maps


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


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Minimizing MAUP

• Normalize farms by area of state


Number of farms Density of farms / mi2

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Minimizing MAUP

• Normalize vacancies by number of housing units


Number of vacancies Fraction of homes vacant

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Normalizing by field percentage

• Divide each value by total of column


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


Number of farms

Density of farms/mi2 4-38

Chapter 4 · 1/4/2017 · 1/30/2017 5 Map Types and Data Types •Single symbol maps •Unique...

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