10a-1: Manipulate, facet, reduce

Ideas to improve visualizations

Recap: How to design viz idioms

Visualization Interactivity

A lot of the viz design techniques discussed below requires interactivity to be baked into the visualization idiom.

  • View Manipulation — Change / Selection / Navigation
  • Faceting — Juxtapose / Partition / Layering
  • Reduction — Filter / Aggregate / Embed

View manipulation

Why do view manipulation?

To help navigate the dataset.

To highlight specific data, draw attention to insights / trends, etc.

Change over time

Basically animation tweening / transitions over time.

  • Change the chart idiom entirely (e.g. bar chart to line chart)
  • Change specific channel encodings (e.g. color schemes, symbol marks)
  • Change the layout arrangement
  • Change dataset
  • Reorder the data

Adjacency matrix, Mike Bostock, Les Miserables dataset

Projection transitions, Mike Bostock

Sunburst to icicle, Noah Veltman

Bivariate animation chart (in R, video), Nathan Yao

Selection

Selecting a datapoint, or a subset / superset of data

  • Highlighting
  • Tooltipping
  • Brushing

Blo.cks.org: Complex tooltip (SVG), d3noob

Blo.cks.org: Scatterplot + brush, Michael Feyder

D3 Graph Gallery: Interactivity, Brushing

ObservableHQ: Brushing Collection

Navigation (item reduction)

Navigating levels (subsets and supersets of the current view), i.e. going up and down the ladder of abstraction. Techniques:

  • Zooming
  • Panning
  • Guided (constrained) navigation

We've gone through examples in the geospatial space, but this can also be applied to other types of charts.

Zoomable scatterplot Obeservable HQ, Mike Bostock

Lifemap (Leaflet + raster tiles), University of Lyon

Scale of the Universe 2

Navigation (dimension reduction)

An intuitive common understanding is taking a 3D dataset and turning it into a a 2D visualization via the below techniques. Of course these can be applied to higher dimentsions.

  • Slicing
  • Cutting
  • Projecting

3D slicer, free open source compute platform for slicing 3D images.

Visualizing quaternions, 3Blue1Brown (Ben Eater) - Amazing!

View faceting

Why facet?

Multiple views juxtaposed side by side (instead of presented one after another over time!) allow us to compare two views simultaneously.

Drawback is of course display space and working memory constraints.

Multiform Visualizations

Showing the data (all or subset) through different chart encodings.

Usage of linked highlighting shows how one view is linked to another.

Population.io, World Data Bank

Covid19 Surveillance Dashboard, University of Virginia

Small Multiples

Show entirely different partitions of the data through the same viz encoding.

Thank Edward Tuft for popularizing sparklines and small multiples.

How Pew Research Center uses small multiple charts, Peter Bell

Global Technology Tracker, Bloomberg (Sparklines!)

Scottish politics, The Guardian

Visualizing the History of Pandemics, VisualCapitalist

Layering

Superimposing layers of data on top of one another.

MarineTraffic

View reduction

Why reduce?

Often to reduce visual clutter in your visualizations and make it easier to understand.

Filtering (item or attribute)

Use an interface option to show/hide a data items or attributes.

Shipmap.org

Onemap.gov.sg traffic query

Aggregate

Group data elements to create new derived attributes.

E.g. Histogram, density plots, beewswam, hexbin, ridgelines, etc.

Age and occupation, Nathan Yau (boxplot)

Income in Each State, Adjusted for Cost of Living, Nathan Yau (histogram, faceted)

How many humans have ever lived, VisualCapitalist

Mapped: All the world's military personnel, VisualCapitalist

Dimension reduction

Dimension reduction is often necessary for dealing with extremely large datasets, breaking the data down into feature vectors.

11 Dimensionality reduction techniques you should know in 2021

Dimensionality Reduction for Data Visualization

Embed (focus + context)

Focus–plus–context enables viewers to see the object of primary interest presented in full detail (focus) while at the same time getting a overview–impression of all the surrounding information (context).

Elide - ObservableHQ: Focus + context, Mike Bostock

Superimpose - Reading: Top 5 learnings for visualizing data in Augmented Reality (AR), Sebastian Sadowski

Distort - Fisheye distortion, Mike Bostock

Distort - Worldmapper.org, (geospatial distortions)

Questions?

Chi-Loong | V/R