ISIS-3710 Web Development


John Alexis Guerra Gómez


Slides:http://johnguerra.co/lectures/webDevelopment_fall2019/12_DataViz_Intro/

Class page:http://johnguerra.co/classes/webDevelopment_fall2019/

Information Visualization

Defining Visualization (vis)

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

Why?

Have the human in the loop

Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods.

Why show data in detail?

  • Summaries lose information
    • Confirm expected and find unexpected patterns
    • Assess validity of statistical model

Anscombe's quartet

I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89
Property Value
Mean of x 9
Variance of x 11
Mean of y 7.50
Variance of y 4.125
Correlation between x and y 0.816
Linear regression y = 3.00 + 0.500x
Coefficient of determination of the linear regression 0.67

Anscombe's visualized

More examples same stats

https://dabblingwithdata.wordpress.com/2017/05/03/the-datasaurus-a-monstrous-anscombe-for-the-21st-century/

Datasaurus!

https://dabblingwithdata.wordpress.com/2017/05/03/the-datasaurus-a-monstrous-anscombe-for-the-21st-century/

Visual Analytics

How to do data analytics?

  • Statistical Analysis
  • Machine Learning and Artificial Intelligence
  • Visual Analytics (and data analytics)

Data Mining/Machine Learning

Information Visualization

Visual Analytics

Traditional

  • Query for known patterns
  • Display results using traditional techniques

Pros:
  • Many solutions
  • Easier to implement

Cons:
  • Can’t search for the unexpected

Data Mining/ML

  • Based on statistics
  • Black box approach
  • Output outliers and correlations
  • Human out of the loop

Pros:
  • Scalable

Cons:
  • Analysts have to make sense of the results
  • Makes assumptions on the data

InfoVis

  • Visual Interactive Interfaces
  • Human in the loop

Pros:
  • Visual bandwidth is enormous
  • Experts decided what to search for
  • Identify unknown patterns and errors in the data

Cons
  • Scalability can be an issue

In Infovis we look for insights

  • Deep understanding
  • Meaningful
  • Non obvious
  • Actionable

Insights

Health insurance claims

Task: Detect fraud network clusters

User: Undisclosed Analysts

Overview

Ego distance

Tweetometro

Task: Twitter behavior during Presidential Elections

User: Me

http://old.tweetometro.co

Normal tweets

Weird tweets?

Creation dates

Number of followers

What car to buy?

Task: What's the best car to buy?

User: Me

Normal procedure

Ask friends and family

Problem

That's inferring statistics from a sample n=1

Better approach

Data based decisions

http://tucarro.com

Visualization Basics

Types of Visualization

  • Infographics
  • Scientific Visualization (sciviz)
  • Information Visualization (infovis, datavis)

Infographics

Scientific Visualization

  • Inherently spatial
  • 2D and 3D

Information Visualization

Infovis Basics

Visualization Mantra

  • Overview first
  • Zoom and Filter
  • Details on Demand

Data Types

1-D Linear Document Lens, SeeSoft, Info Mural
2-D Map GIS, ArcView, PageMaker, Medical imagery
3-D World CAD, Medical, Molecules, Architecture
Multi-Var Spotfire, Tableau, GGobi, TableLens, ParCoords,
Temporal LifeLines, TimeSearcher, Palantir, DataMontage, LifeFlow
Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap, Treeversity
Network Gephi, NodeXL, Sigmajs

Perception Preference

Adapted from from:Tamara Munzner Book Chapter

Intro to D3

https://beta.observablehq.com/@john-guerra/intro-to-d3