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data visualization + design
Visit the Poster Hospital to see redesigns of real-world posters and learn practical design guidelines for scientific posters and layouts on a large canvas.
Visit the Graphical Abstract Hospital to see redesigns of real-world abstracts and learn practical design guidelines for graphical abstracts and small figures.

Obesity — a Data Story

Rescuing nuanced pattterns from the clutches of a bad graphic

“This figure may give you a migrane”

Sometimes, I get emails that look like this

   Sent: Monday, July 29, 2019 at 07:59
   From: Jasleen Grewal
Subject: This figure may give you a migrane

As you can see, 100% of the graphs are ineffective.
BMI and prevalence for 185 countries by Martin Krzywinski / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Each ring plot shows the fraction of population with a BMI ≥ 25 in a country. A page from Atlas der Globaliesierung: Welt in Bewegung, by Stefan Mahike (2019)

Here, I wanted to take you through my reaction to the figure, which was quick, and the redesign, which wasn't quick.

text labels — it's a hard life

I'm always on the lookout for abused text. So here I cried. A lot.

BMI and prevalence for 185 countries by Martin Krzywinski / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A large fraction of labels either barely fit or don't fit. Some are hyphenated and some of those still don't fit. A page from Atlas der Globaliesierung: Welt in Bewegung, by Stefan Mahike (2019)

strangely structured legend

Do we really need a footnote inside the legend? The globe? The hyphenated "Body-Mass-Index". By this point, I really could feel that migrane.

BMI and prevalence for 185 countries by Martin Krzywinski / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
For a simple encoding, the legend is quite complex. From a page from Atlas der Globaliesierung: Welt in Bewegung, by Stefan Mahike (2019)

here's the graphic — now what?

What question's does this figure answer? Here's my list, with answers.

1. How many countries are there in the world? A lot.

2. What is the range of BMI ≥ 25 prevalence? 18—89.

3. Who has the lowest and highest prevalence? Vietnam and Nauru.

4. What is the median prevalence? Probably 55 and answering this is only made easy by the fact that the book's spine splits the plot into largely two equal halves

5. What is the prevalence where I live (e.g. Canada)? I gave up trying to find "Kanada".

Essentially, the two-page figure of ring charts is equivalent to the summary

BMI and prevalence for 185 countries by Martin Krzywinski / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
This figure answers the same questions as the two-page spread for all but the most patient.

critique by redesign

It's obvious what's wrong with the figure. How do you fix it?

Using the list of countries by body mass index, I created a poster that tells interesting stories about how high BMI and obesity vary across countries and genders.

I describe the design and stories in the poster in the design section.

BMI and prevalence for 185 countries by Martin Krzywinski / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
My redesign of the original figure showing preBMI ≥ 25 and obesity prevalence in 185 countries.
news + thoughts

How Analyzing Cosmic Nothing Might Explain Everything

Thu 18-01-2024

Huge empty areas of the universe called voids could help solve the greatest mysteries in the cosmos.

My graphic accompanying How Analyzing Cosmic Nothing Might Explain Everything in the January 2024 issue of Scientific American depicts the entire Universe in a two-page spread — full of nothing.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
How Analyzing Cosmic Nothing Might Explain Everything. Text by Michael Lemonick (editor), art direction by Jen Christiansen (Senior Graphics Editor), source: SDSS

The graphic uses the latest data from SDSS 12 and is an update to my Superclusters and Voids poster.

Michael Lemonick (editor) explains on the graphic:

“Regions of relatively empty space called cosmic voids are everywhere in the universe, and scientists believe studying their size, shape and spread across the cosmos could help them understand dark matter, dark energy and other big mysteries.

To use voids in this way, astronomers must map these regions in detail—a project that is just beginning.

Shown here are voids discovered by the Sloan Digital Sky Survey (SDSS), along with a selection of 16 previously named voids. Scientists expect voids to be evenly distributed throughout space—the lack of voids in some regions on the globe simply reflects SDSS’s sky coverage.”

voids

Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli Lauro Moscardini & Marco Baldi (2023) Cosmological Constraints from the BOSS DR12 Void Size Function Astrophysical Journal 953:46.

Nico Hamaus, Alice Pisani, Jin-Ah Choi, Guilhem Lavaux, Benjamin D. Wandelt & Jochen Weller (2020) Journal of Cosmology and Astroparticle Physics 2020:023.

Sloan Digital Sky Survey Data Release 12

constellation figures

Alan MacRobert (Sky & Telescope), Paulina Rowicka/Martin Krzywinski (revisions & Microscopium)

stars

Hoffleit & Warren Jr. (1991) The Bright Star Catalog, 5th Revised Edition (Preliminary Version).

cosmology

H0 = 67.4 km/(Mpc·s), Ωm = 0.315, Ωv = 0.685. Planck collaboration Planck 2018 results. VI. Cosmological parameters (2018).

Error in predictor variables

Tue 02-01-2024

It is the mark of an educated mind to rest satisfied with the degree of precision that the nature of the subject admits and not to seek exactness where only an approximation is possible. —Aristotle

In regression, the predictors are (typically) assumed to have known values that are measured without error.

Practically, however, predictors are often measured with error. This has a profound (but predictable) effect on the estimates of relationships among variables – the so-called “error in variables” problem.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Error in predictor variables. (read)

Error in measuring the predictors is often ignored. In this column, we discuss when ignoring this error is harmless and when it can lead to large bias that can leads us to miss important effects.

Altman, N. & Krzywinski, M. (2024) Points of significance: Error in predictor variables. Nat. Methods 20.

Background reading

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nat. Methods 12:999–1000.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nat. Methods 13:541–542 (2016).

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nat. Methods 16:451–452.

Convolutional neural networks

Tue 02-01-2024

Nature uses only the longest threads to weave her patterns, so that each small piece of her fabric reveals the organization of the entire tapestry. – Richard Feynman

Following up on our Neural network primer column, this month we explore a different kind of network architecture: a convolutional network.

The convolutional network replaces the hidden layer of a fully connected network (FCN) with one or more filters (a kind of neuron that looks at the input within a narrow window).

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Convolutional neural networks. (read)

Even through convolutional networks have far fewer neurons that an FCN, they can perform substantially better for certain kinds of problems, such as sequence motif detection.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Convolutional neural networks. Nature Methods 20:1269–1270.

Background reading

Derry, A., Krzywinski, M. & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.

Neural network primer

Tue 10-01-2023

Nature is often hidden, sometimes overcome, seldom extinguished. —Francis Bacon

In the first of a series of columns about neural networks, we introduce them with an intuitive approach that draws from our discussion about logistic regression.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Neural network primer. (read)

Simple neural networks are just a chain of linear regressions. And, although neural network models can get very complicated, their essence can be understood in terms of relatively basic principles.

We show how neural network components (neurons) can be arranged in the network and discuss the ideas of hidden layers. Using a simple data set we show how even a 3-neuron neural network can already model relatively complicated data patterns.

Derry, A., Krzywinski, M & Altman, N. (2023) Points of significance: Neural network primer. Nature Methods 20:165–167.

Background reading

Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nature Methods 13:541–542.

Cell Genomics cover

Mon 16-01-2023

Our cover on the 11 January 2023 Cell Genomics issue depicts the process of determining the parent-of-origin using differential methylation of alleles at imprinted regions (iDMRs) is imagined as a circuit.

Designed in collaboration with with Carlos Urzua.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Our Cell Genomics cover depicts parent-of-origin assignment as a circuit (volume 3, issue 1, 11 January 2023). (more)

Akbari, V. et al. Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq (2023) Cell Genomics 3(1).

Browse my gallery of cover designs.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A catalogue of my journal and magazine cover designs. (more)
Martin Krzywinski | contact | Canada's Michael Smith Genome Sciences CentreBC Cancer Research CenterBC CancerPHSA
Google whack “vicissitudinal corporealization”
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