federica bianco

@fedhere

a few historical plots and why they made history

H-R diagram:

the life of a star

https://en.wikipedia.org/wiki/Hertzsprung%E2%80%93Russell_diagram

we visualize to

communicate (Tufte)

and to

explore

(Thorp)

increased data volume

Big data:

One of Thorp’s projects is a visualization of the number of times the terms “communism” (bottom) and “terrorism” (top) appeared in The New York Times, from 1981 until 2009. The spike for “terrorism” is the reflection of 9/11. As the word “terrorism” is used more and more, the use of the word “communism” decreases. (Image courtesy Jer Thorp; flickr.com/photos/blprnt/)

Ambiguity  |  distortion  |   distraction.

An example of ambiguity in visualizations that is common in peer review physics

different stretch

Ambiguity  |  distortion  |   distraction.

# how would you improve these plots?

I would say this plot is at the limit of confusion (information saturation)

Ambiguity  |  distortion  |   distraction.

obstruction

clutter

deformation

No Unjustified 3D

from private communication...

No Unjustified 3D

marginalized posteriors

in MCMC

# how would you improve these plots?

Mollweide projection

equirectangular projection

necessary distortions

An example of ambiguity in visualizations that is common in peer reviewed physics

duplication of data: commonly planet transit and eclipsing binary dataset are repeated twice (consecutively along the x axis)

A highly unequal-mass eclipsing M-dwarf binary in the WFCAM Transit Survey

Nefs, S.V. et al. MNRAS. 431 (2013) 3240 arXiv:1303.0945 [astro-ph.SR]

sometimes we use distortion

sometimes we use distortion

# how would you improve these plots?

Sometime the distraction is a consequence of the complexity of the data.

# what     makes     a    good       visualization?

Tufte's rules

Edward Tufte

Tufte’s rules:

Lie factor =

size of the effect in the graphic
size of the effect in the data

Tufte’s rules:

Lie factor =

size of the effect in the graphic
size of the effect in the data

## Astronomical Surveys Data Rates

SKA

(2025)

(original graphics: Leanne Guy)

Necessary lie factor:

log scale plots!

Tufte’s rules:

1. The representation of numbers, as physically measured on the surface of the graph itself, should be directly proportional to the numerical quantities represented   ("lie factor")
2. Clear, detailed and thorough labeling should be used to defeat graphical distortion and ambiguity.  Write out explanations of the data on the graph itself.  Label important events in the data
3. Show data variation, not design variation
4. In time-series displays of money, deflated and standardized units of monetary measurement are nearly always better than nominal units.
5. The number of information carrying (variable) dimensions depicted should not exceed the number of dimensions in the data. Graphics must not quote data out of context.

effect size ~ 1

data/ink -> large

no chart junk

use small-multiples

avoid redundancy in communication

# Data to Ink Ratio

Tufte’s rules:

1. The representation of numbers, as physically measured on the surface of the graph itself, should be directly proportional to the numerical quantities represented   ("lie factor")
2. Clear, detailed and thorough labeling should be used to defeat graphical distortion and ambiguity.  Write out explanations of the data on the graph itself.  Label important events in the data
3. Show data variation, not design variation
4. In time-series displays of money, deflated and standardized units of monetary measurement are nearly always better than nominal units.
5. The number of information carrying (variable) dimensions depicted should not exceed the number of dimensions in the data. Graphics must not quote data out of context.

effect size ~ 1

data/ink -> large

no chart junk

use small-multiples

avoid redundancy in communication

Tufte’s rules:

Chart Junk

the excessive and unnecessary

use of graphical effects

Tufte’s rules:

1. The representation of numbers, as physically measured on the surface of the graph itself, should be directly proportional to the numerical quantities represented   ("lie factor")
2. Clear, detailed and thorough labeling should be used to defeat graphical distortion and ambiguity.  Write out explanations of the data on the graph itself.  Label important events in the data
3. Show data variation, not design variation
4. In time-series displays of money, deflated and standardized units of monetary measurement are nearly always better than nominal units.
5. The number of information carrying (variable) dimensions depicted should not exceed the number of dimensions in the data. Graphics must not quote data out of context.

effect size ~ 1

data/ink -> large

no chart junk

use small-multiples

avoid redundancy in communication

Tufte’s rules:

1. The representation of numbers, as physically measured on the surface of the graph itself, should be directly proportional to the numerical quantities represented   ("lie factor")
2. Clear, detailed and thorough labeling should be used to defeat graphical distortion and ambiguity.  Write out explanations of the data on the graph itself.  Label important events in the data
3. Show data variation, not design variation
4. (In time-series displays of money), deflated and standardized units of monetary measurement are nearly always better than nominal units.
5. The number of information carrying (variable) dimensions depicted should not exceed the number of dimensions in the data. Graphics must not quote data out of context.

effect size ~ 1

data/ink -> large

no chart junk

use small-multiples

avoid redundancy in communication

Tufte’s rules:

Small multiples

encourage comparison

sparkline graph

Tufte’s rules:

Small multiples

encourage comparison

sparkline graph

Tufte’s rules:

Small multiples

work really well with maps!

https://mahb.stanford.edu/whats-happening/167-tiny-maps-tell-major-story-climate-change/

Galileo Galilei, Jupiter moons, 1610

Tufte’s rules:

Small multiples

Keiran Healy

(Data Viz A Practical Intro)

Galileo Galilei, Jupiter moons, 1610

Tufte’s rules:

every feature should be associated with only 1 graphical element

(here color is redundant with length)

Tufte’s rules:

1. The representation of numbers, as physically measured on the surface of the graph itself, should be directly proportional to the numerical quantities represented   ("lie factor")
2. Clear, detailed and thorough labeling should be used to defeat graphical distortion and ambiguity.  Write out explanations of the data on the graph itself.  Label important events in the data
3. Show data variation, not design variation
4. (In time-series displays of money), deflated and standardized units of monetary measurement are nearly always better than nominal units.
5. The number of information carrying (variable) dimensions depicted should not exceed the number of dimensions in the data. Graphics must not quote data out of context.

effect size ~ 1

data/ink -> large

no chart junk

use small-multiples

avoid redundancy in communication

Tufte’s rules:

Tufte’s rules:

chart junk

2 graphical elements for frequency

(color and position)

low data/ink ratio

no comparison

Tufte’s rules:

chart junk

2 graphical elements for frequency

(color and position)

no comparison

Tufte’s rules:

chart junk

2 graphical elements for frequency

(color and position)

low data/ink ratio

no comparison

comparison but scale out of context

high effect-size due to the choice of color map (more on this later)

# Graphic Vocabulary

• Continuous:    distance to the closest star (can take any value)

Continuous data may be:

• Continuous Ordinal:    Earthquakes (notlinear scale)
• Interval:          F temperature - interval size preserved
• Ratio:              Car speed - 0 is naturally defined

• Discrete:         any countable, e.g. number of brain synapses

Discrete data may be:

• Counts:          number of bacteria at time t in section A
• Ordinal:         survey response Good/Fair/Poor

• Categorical:     fermion - bosons: any  object by class
•

Data may also be:

• Censored:       star mass >30 Msun
• Missing:          “Prefer not to answer” (NA / NaN)

# data types

graphical elements work differently on different data types

Stevens 1975

response to length:

when shown something 4x as long we perceive it as being 4x as long

response to brightness:

when shown something 4x as bright we perceive it as being 2x as bright

I=S
I=\sqrt{S}

response to saturation:

when shown something 4x as saturated we perceive it as being 11x as saturated

I=S^{1.7}

Heer and Bostock 2010

modern version gets uncertainties to these quantities by crowdsourcing the tests

Stevens 1975

Heer and Bostock 2010

# Common Problems

too many time series

too many time series

Tufte's small multiples and

spakrlines

1.
1. In time-series displays of <money>, deflated and standardized units of monetary measurement are nearly always better than nominal units.

enable comparison  by giving the data center stage

too many time series

Time series heatmaps

enable comparison  by giving the data center stage

A common problem: too many points

plt.plot(Teff, logg, 'k.')

A common problem: too many points

solution: subsample

plt.plot(Teff[::10], logg[::10], 'k.')
plt.plot(Teff, logg, 'k.')

A common problem: too many points

plt.plot(Teff, logg, 'k.')
plt.plot(Teff, logg, 'k.', alpha=0.1)

solution: alpha

solution: subsample

plt.plot(Teff[::10], logg[::10], 'k.')

A common problem: too many points

plt.plot(Teff, logg, 'k.')

solution: scatter contours

solution: subsample

plt.plot(Teff[::10], logg[::10], 'k.')
astroml

A common problem: too many points

# Color

theory

(and good practice)

# very real consequences of bad color choices

Borkin et al. 2011

Borkin et al. 2011

Eye Physiology and color perception deficiencies

Rods   |  Cones

80M

Rods   |  Cones

80M

Brightness |  Color

Rods   |  Cones

80M

Brightness |  Color

80M   |  5M

Rods   |  Cones

80M

Brightness |  Color

80M   |  5M

RODS

+

CONES

RODS

Rods   |  Cones

80M

Brightness |  Color

R

G

B

# color blindness

Color blindness (color vision deficiency, CVD) affects approximately

1 in 12 men (8%) and 1 in 200 women

in the world.

Worldwide, there are approximately 300 million people with colour blindness, almost the same number of people as the entire population of the USA!

Protanopia

# color blindness

Protanopia (red-blind)

# color blindness

Protanopia (green-blind)

# color blindness

Tritanopia (blue-blind)

use the http://colororacle.org/ app to test your plots for color-blindness

Kelly 1965 designed a list of 22 maximally contrasting colors for colorblind compliance (the “Kelly colors”):

"#023fa5", "#7d87b9", "#bec1d4", "#d6bcc0", "#bb7784", "#8e063b", "#4a6fe3", "#8595e1", "#b5bbe3", "#e6afb9", "#e07b91", "#d33f6a", "#11c638", "#8dd593", "#c6dec7", "#ead3c6", "#f0b98d", "#ef9708", "#0fcfc0", "#9cded6", "#d5eae7", "#f3e1eb", "#f6c4e1", "#f79cd4"

“Du Bois was aware that while unmoving prose and dry presentations of charts and graphs might catch attention from specialists, this approach would not garner notice beyond narrow circles of academics,” Aldon Morris writes in the essay “American Negro at Paris, 1900.” “Such social science was useless to the liberation of oppressed peoples. Breaking from tradition, Du Bois was among the first great American public intellectuals whose reach extended beyond the academy to the masses.”

https://hyperallergic.com/476334/how-w-e-b-du-bois-meticulously-visualized-20th-century-black-america/

“The colorful charts, graphs, and maps presented at the 1900 Paris Exposition by famed sociologist and black rights activist W. E. B. Du Bois offered a view into the lives of black Americans, conveying a literal and figurative representation of 'the color line'."

After graduating with a Ph.D. in history from Harvard University, W.E.B. Du Bois, the prominent African-American intellectual, sought a way to process all this information showing why the African disapora in America was being held back in a tangible, contextualized form.

https://www.smithsonianmag.com/history/first-time-together-and-color-book-displays-web-du-bois-visionary-infographics-180970826/

W.E.B. Du Bois 1868-1963, sociologist, black right activist, graphic designer ante litteram

a few historical plots and why they made history

W.E.B. Du Bois

February 23, 1868 – August 27, 1963

American sociologist, socialist, historian, civil rights activist, Pan-Africanist, author, writer and editor

https://inspirehep.net/record/1082448/plots

a few historical plots and why they made history

W.E.B. Du Bois

Smithsonian Magazine

# Alternatives to visualization

The theory that the planets and stars in their (circular) motion (around the earth) would produce a sound (and that that sound would be pleasant and harmonious) originate in ancient Greece with Pythagora (that guy must have never slept cause so many things "originate" from him.... (I'm suspicious)), and later formalized with notes by Kepler (him too.... must have never slept!)

"Kepler did not believe this "music" to be audible, but felt that it could nevertheless be heard by the soul" https://en.wikipedia.org/wiki/Musica_universalis​

musica universalis

(perhaps related philosophy parenthesis)

Research Inclusion: sonification of astrophysical time series from the Rubin LSST

Sid Patel, UD undergrad summer research project

Sonification: Data → Sound

New way of understanding data

• Can be complementary to visualizations
interpret data visually

• Sounds cool! Good for public outreach

while eyesight is the most developed sense for humanity in general consider perceptual differences to assure accessibility and equality!

sonification, tactile data 3D printed, and accessible colors and visual properties

#### VIZ metals23

By federica bianco

# VIZ metals23

some notes on visualizations

• 281