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• Cover Page
• Half-Title Page
• Title Page
• Copyright Page
• Dedication
• Contents
• Preface
• Acknowledgments
• About the Authors
• Chapter 1 Introduction
• 1.1 Overview
• 1.2 Learning Objectives
• 1.3 What Is a Thematic Map?
• 1.4 How Are Thematic Maps Used?
• 1.5 Basic Steps for Communicating Map Information
• 1.6 Technological Change in Cartography and Its Consequences
• 1.7 What Is Geovisualization?
• 1.8 Related GIScience Techniques
• 1.9 Cognitive Issues in Cartography
• 1.10 Social and Ethical Issues in Cartography
• 1.11 Summary
• 1.12 Study Questions
• References
• Part I Principles of Cartography
• Chapter 2 A Historical Perspective on Thematic Cartography
• 2.1 Introduction
• 2.2 Learning Objectives
• 2.3 A Brief History of Cartography
• 2.4 History of Thematic Cartography
• 2.4.1 The Rise of Social Cartography
• 2.5 History of U.S. Academic Cartography
• 2.5.1 Period 1: Early Beginnings
• 2.5.1.1 John Paul Goode
• 2.5.1.2 Erwin Raisz
• 2.5.1.3 Guy-Harold Smith
• 2.5.1.4 Richard Edes Harrison
• 2.5.2 Period 2: The Post-War Era and the Building of Core Academic Programs
• 2.5.2.1 University of Wisconsin
• 2.5.2.2 University of Kansas
• 2.5.2.3 University of Washington
• 2.5.3 Period 3: Growth of Secondary Programs
• 2.5.4 Period 4: Integration with GIScience
• 2.6 European Thematic Cartography
• 2.6.1 The Swiss School
• 2.6.2 The British Experimental Cartographic Unit
• 2.6.3 Bertin and French Thematic Cartography
• 2.7 The Paradigms of American Cartography
• 2.7.1 Analytical Cartography
• 2.7.2 Maps and Society
• 2.7.2.1 Privacy
• 2.7.2.2 Power and Access
• 2.7.2.3 Ethics
• 2.7.2.4 Public Participation GIS/Mapping
• 2.8 Summary
• 2.9 Study Questions
• References
• Chapter 3 Statistical and Graphical Foundation
• 3.1 Introduction
• 3.2 Learning Objectives
• 3.3 Population and Sample
• 3.4 Descriptive versus Inferential Statistics
• 3.5 Analyzing the Distribution of Individual Attributes
• 3.5.1 Tables
• 3.5.1.1 Raw Table
• 3.5.1.2 Grouped-Frequency Table
• 3.5.2 Graphs
• 3.5.2.1 Point and Dispersion Graphs
• 3.5.2.2 Histogram
• 3.5.3 Numerical Summaries
• 3.5.3.1 Measures of Central Tendency
• 3.5.3.2 Measures of Dispersion
• 3.6 Analyzing the Relationship between Two or More Attributes
• 3.6.1 Tables
• 3.6.2 Graphs
• 3.6.3 Numerical Summaries
• 3.6.3.1 Bivariate Correlation
• 3.6.3.2 Bivariate Regression
• 3.6.3.3 Reduced Major-Axis Approach
• 3.6.3.4 Multiple Regression and Other Multivariate Techniques
• 3.6.3.5 Considerations in Using Correlation-Regression
• 3.7 Exploratory Data Analysis
• 3.8 Numerical Summaries for Geographic Data
• 3.8.1 Geographic Center
• 3.8.2 Spatial Autocorrelation and Measuring Spatial Pattern
• 3.8.3 Measuring Map Complexity
• 3.9 Summary
• 3.10 Study Questions
• References
• Chapter 4 Principles of Symbolization
• 4.1 Introduction
• 4.2 Learning Objectives
• 4.3 Nature of Geographic Phenomena
• 4.3.1 Spatial Dimension
• 4.3.2 Models of Geographic Phenomena
• 4.3.3 Phenomena versus Data
• 4.4 Levels of Measurement
• 4.5 Visual Variables
• 4.5.1 Visual Variables for Quantitative Phenomena
• 4.5.1.1 Spacing
• 4.5.1.2 Size
• 4.5.1.3 Perspective Height
• 4.5.1.4 Hue, Lightness, and Saturation
• 4.5.2 Visual Variables for Qualitative Phenomena
• 4.5.2.1 Orientation and Shape
• 4.5.2.2 Arrangement
• 4.5.2.3 Hue
• 4.5.3 Some Considerations in Working with Visual Variables
• 4.6 Comparison of Four Common Thematic Mapping Techniques
• 4.6.1 Choropleth Map
• 4.6.2 Proportional Symbol Map
• 4.6.3 Isopleth Map
• 4.6.4 Dot Map
• 4.6.5 Discussion
• 4.7 Selecting Visual Variables for Choropleth Maps
• 4.8 Using Senses Other than Vision to Interpret Spatial Patterns
• 4.8.1 Sound
• 4.8.2 Touch (or Haptics)
• 4.8.3 Smell
• 4.9 Summary
• 4.10 Study Questions
• References
• Chapter 5 Data Classification
• 5.1 Introduction
• 5.2 Learning Objectives
• 5.3 Data to Be Classified
• 5.4 Equal Intervals Method
• 5.5 Quantiles Method
• 5.6 Mean-Standard Deviation Method
• 5.7 Natural Breaks
• 5.8 Optimal
• 5.8.1 The Jenks–Caspall Algorithm
• 5.8.2 The Fisher–Jenks Algorithm
• 5.8.3 Advantages and Disadvantages of Optimal Classification
• 5.9 Head/Tail Breaks: A Novel Classification Method
• 5.10 Criteria for Selecting a Classification Method
• 5.11 Considering the Spatial Distribution of the Data
• 5.12 Summary
• 5.13 Study Questions
• References
• Chapter 6 Scale and Generalization
• 6.1 Introduction
• 6.2 Learning Objectives
• 6.3 Geographic and Cartographic Scale
• 6.3.1 Multiple-Scale Databases
• 6.4 Definitions of Generalization
• 6.4.1 Definitions of Generalization in the Manual Domain
• 6.4.2 Definitions of Generalization in the Digital Domain
• 6.5 Models of Generalization
• 6.5.1 Robinson et al.’s Model
• 6.5.2 McMaster and Shea’s Model
• 6.5.2.1 Why Generalization Is Needed: The Conceptual Objectives of Generalization
• 6.5.2.2 When Generalization Is Required
• 6.6 The Fundamental Operations of Generalization
• 6.6.1 A Framework for the Fundamental Operations
• 6.6.2 Vector-Based Operations
• 6.6.2.1 Simplification
• 6.6.2.2 Smoothing
• 6.6.2.3 Aggregation
• 6.6.2.4 Amalgamation
• 6.6.2.5 Collapse
• 6.6.2.6 Merging
• 6.6.2.7 Refinement
• 6.6.2.8 Exaggeration
• 6.6.2.9 Enhancement
• 6.6.2.10 Displacement
• 6.6.3 The Simplification Process
• 6.7 An Example of Generalization
• 6.8 New Developments in Cartographic Generalization
• 6.8.1 Measurement of Scale Change
• 6.8.2 Fully Automated Generalization
• 6.8.3 Data Models for Generalization
• 6.8.4 New Forms of Cartographic Data
• 6.9 Summary
• 6.10 Study Questions
• References
• Chapter 7 The Earth and Its Coordinate System
• 7.1 Introduction
• 7.2 Learning Objectives
• 7.3 Basic Characteristics of Earth’s Graticule
• 7.3.1 Latitude
• 7.3.2 Longitude
• 7.3.3 Distance and Directions on Earth’s Spherical Surface
• 7.4 Determining Earth’s Size and Shape
• 7.4.1 Earth’s Size
• 7.4.2 Earth’s Shape
• 7.4.2.1 The Prolate versus Oblate Spheroid Controversy
• 7.4.2.2 Reference Ellipsoid and the Graticule
• 7.4.2.3 The Geoid
• 7.4.2.4 Geodetic Datum
• 7.4.2.5 Geodetic Datums and Thematic Cartography
• 7.5 Summary
• 7.6 Study Questions
• References
• Chapter 8 Elements of Map Projections
• 8.1 Introduction
• 8.2 Learning Objectives
• 8.3 The Map Projection Concept
• 8.4 The Reference Globe and Developable Surfaces
• 8.5 The Mathematics of Map Projections
• 8.6 Map Projection Characteristics
• 8.6.1 Class
• 8.6.2 Case
• 8.6.3 Aspect
• 8.7 Distortion on Map Projections
• 8.7.1 A Visual Look at Distortion
• 8.7.2 Scale Factor
• 8.7.3 Tissot’s Indicatrix
• 8.7.4 Distortion Patterns
• 8.7.5 Using Geocart to Visualize Distortion Patterns
• 8.8 Projection Properties
• 8.8.1 Preserving Areas
• 8.8.2 Preserving Angles
• 8.8.3 Preserving Distances
• 8.8.4 Preserving Directions
• 8.8.5 Compromise Projections
• 8.9 Summary
• 8.10 Study Questions
• References
• Chapter 9 Selecting an Appropriate Map Projection
• 9.1 Introduction
• 9.2 Learning Objectives
• 9.3 Potential Selection Guidelines
• 9.3.1 Snyder’s Hierarchical Selection Guideline
• 9.3.1.1 World Map Projections
• 9.3.1.2 Map Projections for a Hemisphere
• 9.3.1.3 Map Projections for a Continent, Ocean, or Smaller Region
• 9.3.1.4 Map Projections for Special Properties
• 9.4 Examples of Selecting Projections
• 9.4.1 Mapping World Literacy Rates
• 9.4.2 Mapping Russian Population Distribution
• 9.4.3 Mapping Migration to the United States
• 9.4.4 Mapping Tornado Paths across Kansas
• 9.4.5 Mapping a Flight Path from Fairbanks, AK to Seoul, South Korea
• 9.4.5.1 Mapping the Flight Path from Space
• 9.4.5.2 Mapping the Flight Path’s Direction
• 9.4.5.3 Mapping the Flight Path Distance
• 9.4.5.4 Mapping the Great Circle Flight Path
• 9.4.5.5 Mapping the Rhumb Line
• 9.4.5.6 Mapping the Flight Path Using Google Maps
• 9.4.6 Discussion
• 9.5 Web-Based Interactive Map Projection Selection
• 9.6 Summary
• 9.7 Study Questions
• References
• Chapter 10 Principles of Color
• 10.1 Introduction
• 10.2 Learning Objectives
• 10.3 How Color Is Processed by the Human Visual System
• 10.3.1 Visible Light and the Electromagnetic Spectrum
• 10.3.2 Structure of the Eye
• 10.3.3 Theories of Color Perception
• 10.3.4 Simultaneous Contrast
• 10.3.5 Color Vision Impairment
• 10.3.6 Beyond the Eye
• 10.4 Models for Specifying Color
• 10.4.1 The RGB Model
• 10.4.2 The CMYK Model
• 10.4.3 The HSV Model
• 10.4.4 The Munsell Model
• 10.4.5 The CIE Model
• 10.4.6 Discussion
• 10.5 Terminology and Principles in the Practical Use of Color
• 10.5.1 Color Wheels
• 10.5.2 Tints, Shades, and Tones
• 10.5.3 Qualitative Color Conventions
• 10.5.4 Quantitative Color Conventions
• 10.5.5 Theme-Oriented Color Schemes
• 10.6 Summary
• 10.7 Study Questions
• References
• Chapter 11 Map Elements
• 11.1 Introduction
• 11.2 Learning Objectives
• 11.3 Alignment and Centering
• 11.4 Common Map Elements
• 11.4.1 Frame Line and Neat Line
• 11.4.2 Mapped Area
• 11.4.3 Inset
• 11.4.4 Title and Subtitle
• 11.4.5 Legend
• 11.4.6 Data Source
• 11.4.7 Scale
• 11.4.8 Orientation
• 11.4.9 Relative Type Sizes for Certain Map Elements
• 11.5 Summary
• 11.6 Study Questions
• References
• Chapter 12 Typography
• 12.1 Introduction
• 12.2 Learning Objectives
• 12.3 What Is Typography?
• 12.3.1 Characteristics of Type
• 12.4 General Typographic Guidelines
• 12.5 Specific Typographic Guidelines
• 12.5.1 All Features (Point, Linear, and Areal)
• 12.5.2 Point Features
• 12.5.3 Linear Features
• 12.5.4 Areal Features
• 12.6 Automated Type Placement
• 12.7 Summary
• 12.8 Study Questions
• References
• Chapter 13 Cartographic Design
• 13.1 Introduction
• 13.2 Learning Objectives
• 13.3 Elements of Cartographic Design
• 13.3.1 The Design Process
• 13.3.2 Visual Hierarchy
• 13.3.3 Contrast
• 13.3.4 Figure-Ground
• 13.3.5 Balance
• 13.4 Case Study: Real Estate Site Suitability Map
• 13.4.1 Steps 1–3 of the Map Communication Model
• 13.4.2 Step 4 of the Map Communication Model: Design and Construct the Map
• 13.4.3 Return to Procedure 4: Implementation of Map Elements and Typography
• 13.4.3.1 Frame Line and Neat Line
• 13.4.3.2 Mapped Area
• 13.4.3.3 Inset
• 13.4.3.4 Title and Subtitle
• 13.4.3.5 Legend
• 13.4.3.6 Data Source
• 13.4.3.7 Scale
• 13.4.3.8 Orientation
• 13.4.4 Final Procedures
• 13.5 Summary
• 13.6 Study Questions
• References
• Chapter 14 Map Reproduction
• 14.1 Introduction
• 14.2 Learning Objectives
• 14.3 Planning Ahead
• 14.4 Map Editing
• 14.5 Raster Image Processing for Print Reproduction
• 14.5.1 Printing the Digital Map
• 14.6 Screening for Print Reproduction
• 14.6.1 Halftone and Stochastic Screening
• 14.6.2 Halftone Screening Parameters
• 14.6.3 Stochastic Screening Parameters
• 14.7 Aspects of Color Printing
• 14.7.1 Process Colors
• 14.7.2 Spot Colors
• 14.7.3 High-Fidelity Process Colors
• 14.7.4 Color Management Systems
• 14.8 High-Volume Print Reproduction
• 14.8.1 The Prepress Phase
• 14.8.2 File Formats for Prepress
• 14.8.3 Proofing Methods
• 14.8.4 Offset Lithographic Printing
• 14.9 Summary
• 14.10 Study Questions
• References
• Part II Mapping Techniques
• Chapter 15 Choropleth Mapping
• 15.1 Introduction
• 15.2 Learning Objectives
• 15.3 Selecting Appropriate Data
• 15.4 Factors for Selecting a Color Scheme
• 15.4.1 Kind of Data
• 15.4.2 Color Naming
• 15.4.3 Color Vision Impairment
• 15.4.4 Simultaneous Contrast
• 15.4.5 Map Use Tasks
• 15.4.6 Color Associations
• 15.4.7 Aesthetics
• 15.4.8 Age of the Intended Audience
• 15.4.9 Presentation vs. Data Exploration
• 15.4.10 Economic Limitations and Client Requirements
• 15.5 Systems for Specifying Color Schemes
• 15.5.1 Approaches for Classed Maps
• 15.5.1.1 Color Ramping and HSV Systems
• 15.5.1.2 The Munsell Curve
• 15.5.1.3 ColorBrewer
• 15.5.2 Approaches for Unclassed Maps
• 15.5.2.1 Applying the Munsell Curve
• 15.5.2.2 Kovesi’s Approach
• 15.6 Classed vs. Unclassed Mapping
• 15.6.1 Maintaining Numerical Data Relations
• 15.6.2 Presentation vs. Data Exploration
• 15.6.3 Summarizing the Results of Experimental Studies
• 15.6.3.1 Specific Information
• 15.6.3.2 General Information
• 15.6.3.3 Discussion
• 15.7 Legend Design
• 15.8 Illuminated Choropleth Mapping
• 15.9 Summary
• 15.10 Study Questions
• References
• Chapter 16 Dasymetric Mapping
• 16.1 Introduction
• 16.2 Learning Objectives
• 16.3 Selecting Appropriate Data and Ancillary Information
• 16.4 Some Basic Approaches for Dasymetric Mapping
• 16.5 Eicher and Brewer’s Study
• 16.6 Mennis and Hultgren’s Intelligent Dasymetric Mapping (IDM)
• 16.7 Two Approaches for Producing Dasymetric Maps of Population Density
• 16.7.1 Approach One: Using Land Cover and Limiting Ancillary Data Sets
• 16.7.2 Approach Two: Use Zoning Polygons and Limiting Ancillary Data Sets
• 16.7.3 Discussion
• 16.8 Socscape: A Web App for Visualizing Racial Diversity
• 16.9 Mapping the Global Population Distribution
• 16.9.1 Gridded Population of the World
• 16.9.2 LandScan
• 16.9.3 Global Human Settlement Layer
• 16.10 Summary
• 16.11 Study Questions
• References
• Chapter 17 Isarithmic Mapping
• 17.1 Introduction
• 17.2 Learning Objectives
• 17.3 Selecting Appropriate Data
• 17.4 Manual Interpolation
• 17.5 Automated Interpolation for True Point Data
• 17.5.1 Triangulation
• 17.5.2 Inverse-Distance Weighting
• 17.5.3 Ordinary Kriging
• 17.5.3.1 Semivariance and the Semivariogram
• 17.5.3.2 Kriging Computations
• 17.5.4 Thin-Plate Splines
• 17.5.5 Choosing among the Interpolation Methods
• 17.6 Tobler’s Pycnophylactic Interpolation
• 17.7 Symbolization
• 17.7.1 Some Basic Symbolization Approaches
• 17.7.2 Color Stereoscopic Effect
• 17.8 Summary
• 17.9 Study Questions
• References
• Chapter 18 Proportional Symbol Mapping
• 18.1 Introduction
• 18.2 Learning Objectives
• 18.3 Selecting Appropriate Data
• 18.4 Kinds of Proportional Symbols
• 18.5 Scaling Proportional Symbols
• 18.5.1 Mathematical Scaling
• 18.5.2 Perceptual Scaling
• 18.5.2.1 Formulas for Perceptual Scaling
• 18.5.2.2 Problems in Applying the Formulas
• 18.5.3 Range-Graded Scaling
• 18.6 Legend Design
• 18.6.1 Arranging Symbols
• 18.6.2 Which Symbols to Include
• 18.7 Handling Overlap of Symbols
• 18.7.1 How Much Overlap?
• 18.7.2 Symbolizing Overlap
• 18.8 Necklace Maps
• 18.9 Summary
• 18.10 Study Questions
• References
• Chapter 19 Dot Mapping
• 19.1 Introduction
• 19.2 Learning Objectives
• 19.3 Key Issues Involved in Dot Mapping
• 19.3.1 Determining Regions within Which Dots Should Be Placed
• 19.3.2 Selecting Dot Size and Unit Value
• 19.3.3 Placing Dots within Regions
• 19.3.3.1 Placing Dots Manually
• 19.3.3.2 Placing Dots Digitally
• 19.3.4 Designing a Legend
• 19.4 Graduated Dot Mapping
• 19.5 Interactive Dot Mapping on the Web
• 19.6 Summary
• 19.7 Study Questions
• References
• Chapter 20 Cartograms
• 20.1 Introduction
• 20.2 Learning Objectives
• 20.3 Methods that Attempt to Preserve the Shape of Enumeration Units
• 20.3.1 Noncontiguous Cartograms
• 20.3.2 Contiguous Cartograms
• 20.3.2.1 Gridded Cartograms
• 20.3.3 Mosaic Cartograms
• 20.4 Methods that Do Not Preserve the Shape of Enumeration Units
• 20.4.1 Rectangular Cartograms
• 20.4.1.1 Rectilinear Cartograms
• 20.4.2 Dorling Cartograms
• 20.4.3 Demers Cartograms
• 20.5 Contrasting Various Cartogram Methods
• 20.5.1 Contrasting Cartogram Methods in Terms of Aspects of Accuracy
• 20.5.2 A User Study of Major Cartogram Methods
• 20.6 Alternatives to Conventional Cartograms
• 20.6.1 Combined Choropleth/Proportional Symbol Maps
• 20.6.2 Value-by-Alpha Maps
• 20.6.3 Balanced Cartograms
• 20.7 Summary
• 20.8 Study Questions
• References
• Chapter 21 Flow Mapping
• 21.1 Introduction
• 21.2 Learning Objectives
• 21.3 Basic Types of Flow Maps and Associated Data for Flow Mapping
• 21.4 Issues in Designing Flow Maps
• 21.5 Flow Mapping Prior to Automation
• 21.6 Early Digital Flow Mapping Efforts by Waldo Tobler
• 21.7 Examples of Recent Digital Flow Mapping
• 21.7.1 Stephen and Jenny’s Interactive Web-Based Origin-Destination Flow Map
• 21.7.2 Koylu et al.’s Web-Based Software for Designing Origin-Destination Flow Maps
• 21.7.2.1 Koylu and Guo’s User Study
• 21.7.2.2 Koylu et al.’s FlowMapper Software
• 21.7.3 Flow Mapping in Virtual Environments
• 21.8 Geovisual Analytics and Flow Mapping
• 21.9 Summary
• 21.10 Study Questions
• References
• Chapter 22 Multivariate Mapping
• 22.1 Introduction
• 22.2 Learning Objectives
• 22.3 Bivariate Mapping
• 22.3.1 Comparing Maps
• 22.3.1.1 Comparing Choropleth Maps
• 22.3.1.2 Comparing Miscellaneous Thematic Maps
• 22.3.1.3 Comparing Maps for Two Points in Time
• 22.3.2 Combining Two Attributes on the Same Map
• 22.3.2.1 Bivariate Choropleth Maps
• 22.3.2.2 Additional Bivariate Mapping Techniques
• 22.4 Multivariate Mapping Involving Three or More Attributes
• 22.4.1 Comparing Maps
• 22.4.2 Combining Attributes on the Same Map
• 22.4.2.1 Trivariate Choropleth Maps
• 22.4.2.2 Multivariate Dot Maps
• 22.4.2.3 Multivariate Point Symbol Maps
• 22.4.2.4 Acquiring Specific and General Information from Multivariate Maps
• 22.4.2.5 Ring Maps: An Alternative to Conventional Symbolization Approaches
• 22.5 Cluster Analysis
• 22.5.1 Basic Steps in Hierarchical Cluster Analysis
• 22.5.2 Adding a Contiguity Constraint to a Hierarchical Cluster Analysis
• 22.6 Summary
• 22.7 Study Questions
• References
• Part III Geovisualization
• Chapter 23 Visualizing Terrain
• 23.1 Introduction
• 23.2 Learning Objectives
• 23.3 Nature of the Data
• 23.4 Vertical Views
• 23.4.1 Hachures
• 23.4.2 Contour-Based Methods
• 23.4.2.1 Eynard and Jenny’s Work
• 23.4.3 Raisz’s Physiographic Method
• 23.4.4 Shaded Relief
• 23.4.5 Morphometric Techniques
• 23.4.5.1 Symbolizing Aspect and Slope: Brewer and Marlow’s Approach
• 23.4.5.2 Symbolizing Other Morphometric Parameters
• 23.5 Oblique Views
• 23.5.1 Block Diagrams
• 23.5.2 Panoramas and Related Oblique Views
• 23.5.3 Plan Oblique Relief
• 23.6 Physical Models
• 23.7 Issues in Creating Shaded Relief
• 23.7.1 Generalizing the Terrain
• 23.7.2 Selecting an Azimuth and Sun Elevation for Illumination
• 23.7.3 Other Lighting Model Issues
• 23.7.4 Representation of Swiss-Style Rock Drawing
• 23.7.5 Color Considerations
• 23.8 Summary
• 23.9 Study Questions
• References
• Chapter 24 Map Animation
• 24.1 Introduction
• 24.2 Learning Objectives
• 24.3 Early Developments
• 24.4 Visual Variables for Animation
• 24.5 Examples of Temporal Animations
• 24.5.1 Animating Movement and Flows
• 24.5.2 Animating Choropleth Maps
• 24.5.2.1 Some Basic Examples of Choropleth Animation
• 24.5.2.2 Should We Generalize Choropleth Animations?
• 24.5.2.3 Should We Utilize Classed or Unclassed Maps?
• 24.5.3 Animating Proportional Symbol Maps
• 24.5.4 Animating Isarithmic Maps
• 24.5.5 Other Temporal Animations
• 24.6 Examples of Nontemporal Animations
• 24.6.1 Peterson’s Early Work
• 24.6.2 Gershon’s Early Work
• 24.6.3 Fly-Overs
• 24.6.4 Viégas and Wattenberg’s Wind Map
• 24.7 Enhancing the Interactivity in Animations
• 24.7.1 Harrower’s Work
• 24.7.2 CoronaViz
• 24.8 Does Animation Work?
• 24.9 Guidelines for Designing Your Own Animations
• 24.10 Using 3-D Space to Display Temporal Data
• 24.11 Summary
• 24.12 Study Questions
• References
• Chapter 25 Data Exploration
• 25.1 Introduction
• 25.2 Learning Objectives
• 25.3 Goals of Data Exploration
• 25.4 Methods of Data Exploration
• 25.4.1 Manipulating Data
• 25.4.2 Varying the Symbolization
• 25.4.3 Manipulating the User’s Viewpoint
• 25.4.4 Multiple Map Views
• 25.4.5 Linking Maps with Other Forms of Display
• 25.4.6 Highlighting Portions of a Data Set
• 25.4.7 Probing the Display
• 25.4.8 Toggling Individual Themes On and Off
• 25.4.9 Animation
• 25.4.10 Access to Miscellaneous Resources
• 25.4.11 How Symbols Are Assigned to Attributes
• 25.4.12 Automatic Map Interpretation
• 25.5 Examples of Data Exploration
• 25.5.1 Moellering’s 3-D Mapping Software
• 25.5.2 ExploreMap and Map Sequencing
• 25.5.3 Project Argus
• 25.5.4 MapTime
• 25.5.5 CommonGIS
• 25.5.6 GeoDa
• 25.5.7 Micromaps
• 25.5.7.1 Linked Micromaps Plot
• 25.5.7.2 Conditioned Micromaps
• 25.5.8 ViewExposed
• 25.5.9 Using Tableau to Create Interactive Data Visualizations
• 25.6 Summary
• 25.7 Study Questions
• References
• Chapter 26 Geovisual Analytics
• 26.1 Introduction
• 26.2 Learning Objectives
• 26.3 Characteristics and Limitations of Big Data
• 26.4 What Is Geovisual Analytics?
• 26.5 The Self-Organizing Map (SOM)
• 26.6 Examples of Geovisual Analytics
• 26.6.1 TaxiVis: A System for Visualizing Taxi Trips in NYC
• 26.6.2 Mosaic Diagrams: A Technique for Visualizing Spatiotemporal Data
• 26.6.3 CarSenToGram: An Approach for Visualizing Twitter Data
• 26.6.4 Crowd Lens: A Tool for Visualizing OpenStreetMap Contributions
• 26.6.5 Use of a SOM for Sense-of-Place Analysis
• 26.7 Summary
• 26.8 Study Questions
• References
• Chapter 27 Visualizing Uncertainty
• 27.1 Introduction
• 27.2 Learning Objectives
• 27.3 Basic Elements of Uncertainty
• 27.4 General Methods for Depicting Uncertainty
• 27.5 Visual Variables for Depicting Uncertainty
• 27.5.1 Some Examples of Intrinsic Visual Variables
• 27.5.2 Some Examples of Extrinsic Visual Variables
• 27.6 Applications of Visualizing Uncertainty
• 27.6.1 Handling the Uncertainty in Choropleth Maps
• 27.6.1.1 Using Confidence Levels (CLs) to Create Class Breaks
• 27.6.1.2 Using Maximum Likelihood Estimation to Create Class Breaks
• 27.6.1.3 Using the SAAR Software to Visualize Uncertainty
• 27.6.2 Visualizing Climate Change Uncertainty
• 27.6.3 Visualizing Uncertainty in Decision-Making
• 27.6.3.1 Visualizing the Uncertainty of Water Balance Models
• 27.6.3.2 Visualizing the Uncertainty of Forecasted Hurricane Paths
• 27.6.4 Examples of Interactivity and Animation
• 27.7 Using Sound to Represent Data Uncertainty
• 27.8 Summary
• 27.9 Study Questions
• References
• Chapter 28 Virtual Environments and Augmented Reality
• 28.1 Introduction
• 28.2 Learning Objectives
• 28.3 Defining VEs and AR
• 28.4 Technologies for Creating VEs
• 28.4.1 Personalized Displays
• 28.4.2 Wall-Size Displays
• 28.4.3 Head-Mounted Displays
• 28.4.4 Room-Format and Drafting-Table Format Displays
• 28.5 The Four “I” Factors of VEs
• 28.5.1 Immersion
• 28.5.2 Interactivity
• 28.5.3 Information Intensity
• 28.5.4 Intelligence of Objects
• 28.6 Some Key Questions Regarding VEs
• 28.6.1 Are Specialized Symbols Necessary for Thematic Maps Created in VEs?
• 28.6.2 Are Stereoscopic Maps More Effective than Non-Stereoscopic Maps?
• 28.6.3 What Are Some Examples of VEs That Make Use of Caves and Wall-Size Displays?
• 28.6.3.1 Using a CAVE to Create Soils Maps
• 28.6.3.2 Using a Wall-Size Display to Obtain Public Input on Climate Change Scenarios
• 28.6.3.3 HMDs as a Potential Cost-Effective Solution for Collaborative Efforts
• 28.6.4 What Progress Has Been Made Toward Developing a Digital Earth?
• 28.7 Some Recent Examples of the Utilization of AR
• 28.7.1 The Augmented Reality Sandbox
• 28.7.2 Using AR to Enhance an Understanding of Topographic Maps
• 28.7.3 Developing Novel Methods for Interacting with AR Environments
• 28.7.4 Holograms
• 28.8 Health, Safety, and Social Issues
• 28.9 Summary
• 28.10 Study Questions
• References
• Glossary
• Index