A lot of the viz design techniques discussed below requires interactivity to be baked into the visualization idiom.
To help navigate the dataset.
To highlight specific data, draw attention to insights / trends, etc.
Basically animation tweening / transitions over time.
Adjacency matrix, Mike Bostock, Les Miserables dataset
Projection transitions, Mike Bostock
Sunburst to icicle, Noah Veltman
Bivariate animation chart (in R, video), Nathan Yao
Selecting a datapoint, or a subset / superset of data
Blo.cks.org: Complex tooltip (SVG), d3noob
Blo.cks.org: Scatterplot + brush, Michael Feyder
D3 Graph Gallery: Interactivity, Brushing
ObservableHQ: Brushing Collection
Navigating levels (subsets and supersets of the current view), i.e. going up and down the ladder of abstraction. Techniques:
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
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.
3D slicer, free open source compute platform for slicing 3D images.
Visualizing quaternions, 3Blue1Brown (Ben Eater) - Amazing!
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.
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
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
Superimposing layers of data on top of one another.
Often to reduce visual clutter in your visualizations and make it easier to understand.
Use an interface option to show/hide a data items or attributes.
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 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
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)
Chi-Loong | V/R