CS-GY 6313: Information Visualization
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  • Introduction
  • Defining Information Visualization
  • Why Use Visualization?
  • Popular Visualization Sources and Tools
  • Why Use a Graphical Representation?
  • The Problem with Statistics
  • Why Use a Computer to Visualize Data?
  • Why Use Interaction?
  • Assessing the Quality of a Visualization
  • Data Abstraction
    • Types of Datasets
    • Types of Attributes
    • Attribute Semantics
    • Data Abstraction to Visualization
    • Data Profiling
  • Fundamental Graphs
    • Alternate Representations
    • Visualizing More Than 2 Attributes
    • Faceting
  • Data Transformation
  • Graphical Components and Mapping Strategies
    • Marks
    • Channels
    • Graphical Decoding
    • Evaluating the Quality of a Visual Encoding
    • Contextual Components
  • Color
    • Color Perception
    • Color Specification
    • Color Use
      • Quantitative Color Scales
      • Categorical Color Scales
      • Diverging Color Scales
      • Highlighting
    • Perceptual Issues with Color
  • Geo Visualization
    • When to Use Maps
    • Geo Visualization Techniques
      • Dot Maps
      • Heat Maps
      • Hexbin Maps
      • Choropleth Maps
      • Graduated Symbol Maps
      • Summary of Map Types
    • Issues with Maps
    • Visualizing Geo Data with Time
  • Visualizing Temporal Data
    • Time Structures
    • Visualization Methods
    • Increasing Visual Scalability
    • Beyond Using Position
  • Networks and Trees
    • Visualizing Network Data
      • Node-Link Diagrams
        • Clutter Reduction
      • Matrices
    • Visualizing Trees
      • Node-Link Diagrams
      • Special Kinds of Trees
      • Space-Partitioning and Containment
        • Sunburst and Icicle Plots
  • Interaction and Multiple Views
    • Single and Multiple View Methods
      • Single View Methods
      • Multiple (Linked) Views Methods
    • Common Scenarios
  • Exploring Data
  • Animation, Pacing and Exposition
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On this page
  • Base Rate Bias
  • Insensitivity to Sample Size
  • Skewed Spatial Distributions
  • Perceptual Issues
  • Map Projections
  • Interference from Map Features

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  1. Geo Visualization

Issues with Maps

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Last updated 5 years ago

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Base Rate Bias

Human-related events are correlated with population density.

For example, the map above may make the viewer assume that high density regions are more literate/culturally advanced. However, this is not true! They are simply more populated!

Insensitivity to Sample Size

When we have a large sample size, values tend to be stable around the mean. However, when we have a small sample size, values tend to oscillate and be either very high or very low. Smaller sample are, therefore, very unstable, when compared to larger samples.

Skewed Spatial Distributions

We can have very small regions and very large regions at the same time. This problem is particularly common while using the world map for a visualization: for example, Europe has very small countries while other regions have larger countries.

This is a problem because size has an impact on perception. Smaller areas are more difficult to visualize.

One solution to this problem is to use a Dorling Cartogram.

Perceptual Issues

  • It is difficult to estimate/compare values using color intensity

  • Color perception is affected by area size

  • Background color affects the perception of the color of foreground objects

  • The perception of the size of an object is also affected by the sizes of the objects that surround it

Map Projections

Every map is a result of projecting points on the surface of a sphere (the Earth) onto locations on a plane (the 1D map). This results in certain unavoidable distortions.

The following are some commonly used projection types:

We must keep in mind that:

  • larger areas are more prone to distortion

  • distortion increases as we move away from the point of contact (ex. for the cylindrical projection shown above, distortion is least at the equator and highest at the poles)

Some rules of thumb:

  • for equatorial regions, use the cylindrical projection

  • for mid-latitude regions, use the conic projection

  • for the polar regions, use the azimuthal projection

Tissot's Indicatrix is a measure of distortion. The idea behind it is to start by placing circles on the original surface and then measure how the circles get distorted when they are transformed to a plane.

The Mercator Projection is the most widely used projection. It has no angular distortion.

However, there is area distortion from North to South. The areas in the North and South are much larger than the areas in between.

The image below shows the Gall-Peters Projection. It tries to preserve area but there is visible shape distortion.

Finally, we have the Robinson Projection. It doesn't completely preserve any specific feature, but is a good compromise among all the features.

  • Equivalent: preserve equivalent areas, useful for world maps and small scale in general

  • Conformal: preserve angular relationships, useful for large-scale maps

Interference from Map Features

Sometimes, the features of the map in the background (such as region boundaries) may interfere with the actual objects that we want to visualize.

We must make sure to keep the focus on the spatial distribution of the values contained in the data. To do so, it is important to decide how many and which details from the map we want to retain.

If we use too many map features, they will interfere with our data. However, if we use too few map features, we may lose important contextual information. There is always a trade off.

A few rules of thumb:

  • try to use a few colors (preferably with low saturation); greys are great; don't use too many colors

  • do not use string lines or borders

  • include only necessary spatial features

A Dorling cartogram uses bubbles instead of the actual region shapes. Positions of these bubbles are determined using an algorithm that keeps the topological relations between regions intact, but may vary the sizes in a manner that is not proportionate to the actual region sizes.

Projections are classified based on the kinds of distortions they cause, which may include distortions to one or more of the following: angles, areas, shapes, distances, directions. No projection can preserve all of them at once.

We must choose between two types of projection categories: