Module 9 - Map Symbolization

LEARNING OBJECTIVES

1. Describe a map symbol
2. Discuss basic map symbolization issues
3. List several questions it is helpful to ask to assist in symbolizing your data.
4. List the visual variables that help guide basic map symbolization decisions
5. Compare and contrast the visual variables of shape and size
6. Define color hue and describe the type data it might and might not be used to symbolize appropriately
7. Define color value and describe the type data it might and might not be used to symbolize appropriately
8. Describe how color intensity is best used to symbolize data
9. Desribe some considerations when using texture to symbolize data
10. Define bivariate symbolization
11. Describe the different ways aggregate data can be mapped

TERMS TO KNOW

• map symbolization
• symbol
• symbol by convention
• symbol by resemblence
• points
• lines
• areas
• qualitative
• quantitative
• individual
• aggregate
• visual variables
• shape
• size
• color hue
• color value
• color intensity
• texture
• bivariate
• choropleth
• graduated symbol
• dotmap
• surface map
• cartogram

READING ASSIGNMENT

Chapter 9 of your text - MakingMaps: A Visual Guide to Map Design for GIS

The author's outline for this chapter from the class he teaches using this book - Thanks for sharing Dr. Krygier!

Topographic Map Symbols

ACTIVE LEARNING EXERCISE

Mapping and Modeling Groundwater Chemistry - by importing Excel spreadsheets into ArcGIS 9.2

STUDY QUESTIONS

1. What is a map symbol?
2. Provide an example of a symbol by resemblance.
3. Provide an example of a symbol by convention.
4. What types of visual variables are appropriate for qualitative data?
5. What types of visual variables are appropriate for quantitative data?
6. Provide an example of a map/layer that would be appropriately symbolized using the visual variable of shape.
7. Provide an example of a map/layer that would be appropriately symbolized using the visual variable of size.
8. Provide an example of a map/layer that would be appropriately symbolized using the visual variable of color hue.
9. Provide an example of a map/layer that would be appropriately symbolized using the visual variable of color value.
10. Describe a choropleth map.
11. How do dot maps symbolize data?
12. What types of data are best represented using graduated symbol maps?
13. What are bivariate maps?
14. What is a cartogram?
15. Describe surface maps. Make sure to mention some design issues.

Module 8 - Map Generalization and Classification

LEARNING OBJECTIVES

1. Describe how maps generalize a very complex world into something easier to understand.
2. Define data classification
3. Compare and contrast qualitative and quantitative classification
4. Discuss the different types of quantitative classification
5. Utilize ArcWeb Services to generate a web-based map

TERMS TO KNOW

• generalization
• classification
• simplification
• smoothing
• selection
• displacement
• quantitative
• qualitative
• quantile scheme
• equal-interval scheme
• natural-breaks scheme
• unique scheme
• ArcWeb Services
• ArcGIS Online

READING ASSIGNMENT

Chapter 8 of your text - MakingMaps: A Visual Guide to Map Design for GIS

The author's outline for this chapter from the class he teaches using this book - Thanks for sharing Dr. Krygier!

Ways to map quantitative data - ESRI ArcGIS 9.2 WebHelp

ACTIVE LEARNING EXERCISE

First, read about the new ArcGIS online here

Second, listen to the Instructor Series Overview of ArcGIS online podcast

Third, read about ArcWeb Services

Finally, do the tutorial from this issue of ArcUser Online - 3 Steps in One Hours - ArcWeb Services JavaScript API Tutorial - I'm still trying to determine the best way for you to show me that you have completed this work so for now just do it...

STUDY QUESTIONS

1. Sometimes, fewer data are often better. Give an example of this.
2. What is the point of map generalization and data classification?
3. List and describe the types of map generalization techniques.
4. Why do we classify data?
5. What is the difference between qualitative classification and quantitative classification? Give an example of each.
6. When determining the number of classes to put your data into, what are some things to consider about whether to use relatively few classes or more classes?
7. What is an advantage and disadvantage to using the quantile scheme for classifying your data?
8. When is an equal-interval classification a good choice?
9. When is an unique scheme a good choice for data classification?