Jdinoto

(Updated on September 8 and 9. Go to the bottom of the post)

Data visualization isn't just about visualizing data, but also about writing headlines, intros, captions, explainers, and footnotes. I'm right now closely following the news about hurricane Irma —I live in Miami!— and feeling both amazed and terrified by the many great graphics news organizations and independent designers are publishing. As I've just tweeted, beauty is sometimes correlated with terror.

Anyway, I've just read a very good graphics-driven story in The Washington Post. This is its first map:



This is its caption:


I'm no expert in weather forecasting, but I believe that this is inaccurate. To learn why, go to minute 14:30 in my keynote at Microsoft's Data Insights summit. Here's some of what I said there:

Maps based on cones of uncertainty are quite problematic, as this article by Jen Christiansen, and this other by Robert Kosara explain. Among other reasons, some people don't see in that cone the possible range of paths the center of the hurricane can take, but the size of the hurricane itself.

This happens event to those who, like me, do know how to read this kind of map. I need to consciously struggle with my brain's inclination to see a physical object, and not a probability range. Why? I don't know for sure, but I'll make a conjecture: it's because the representation looks pictorial. The rounded shape of the tip of the cone roughly resembles the shape of a hurricane.

This map is made even more confusing if a black line is placed in the middle of the cone. Just read tweets like this. People may see that line not as a visual aid to emphasize the center of the cone (right), but as the most probable path (wrong).

Going back to the caption, the reason why it sounds wrong to me is related to something most of you probably aren't aware of: the cone of uncertainty doesn't represent the range of all possible paths the hurricane could follow, based on simulations. This excellent paper explains that the most common cone, the one by NHC, “accurately predicts the ultimate path of the tropical cyclone’s center about 2/3 of the time (J. Franklin 2005, personal communication). In other words, one out of three storm centers directly impact areas outside of the cone.” That's a 66%-33% chance.

Therefore, the caption could say something like this: “Based on predictive simulations of past hurricanes, there are 2 out of 3 chances that the path of the center of the hurricane could be anywhere within this cone, and a 1 out of 3 chance it will be outside of it.” This is longer and clunkier —I'm sure that any copy editor in the audience can improve it!— but truer to reality.

This other map shows the actual uncertainty of predictive simulations quite well; notice the faded lines, corresponding to less probable (but still possible) paths:


UPDATE: It seems that NOAA is listening. See the explanation that they have been tweeting. It ought to be published next to every single cone of uncertainty map out there:


UPDATE 2: The map below, by meteorologist Ryan Maue, is far better than any cone of uncertainty map if your goal is to inform the general public about the risks posed by wind. See it animated. The scale is predicted maximum wind speed in mph.

‘seinfeld’ script

written by 10 people using predictive text keyboards

source: transcripts of episodes from season 3 of seinfeld. source texts specific to character with separate source for stage directions. 18 word options at each step. finished script also includes one or two stage directions generated by a recurrent neural network.

writers: carl s, corey g, elle o, erin m, gabe m, jonah c, joseph p, kai c, rob d

editors me and rob d

transcript:

INT. COMEDY CLUB
JERRY
What’s the the problem with the
minimalism? You know, if you enjoy
being around alcoholics, I’m gonna
go ahead and never return that
dog.
CUT TO:
INT. JERRY’S APARTMENT - DAY
JERRY and ELAINE are carrying leaves in their hands. He tries
to grab a door and she imitates the door.
JERRY
Well the elevator opens and wrong
side of the door… I thought
maybe the door’s not waiting, but
it said “going down” and Kramer
couldn’t help me move it.  I just
wanted to get out of it, just get
out.
He slams his hand on the door. KRAMER enters dancing with
garbage.
KRAMER
Hey hey hey, great idea for a big
sponge: Make it so large you think
it’s got a fat clock in the
middle.
JERRY
(takes off his bones)
Kramer, do you have a fun
flashback to do?
CUT TO:
INT. MONK’S CAFE - DAY
George is wearing a $20 hat that says “Hello to Horse.”
GEORGE
I’m kinda like the captain of
hygiene.
JERRY
Mind the fish, George! Elaine, say
something to George. Look for
anything wrong with him.
2.
ELAINE
Right now I want to find someone
who doesn’t believe George is back
from the bathroom.
GEORGE
(takes the garbage and moves
back)
Elaine isn’t exhausting but I’ll
bet three days of straight fear
I’m not bitter.
ELAINE
First dog to get George will be my
friend.
George walks at her seriously.
ELAINE
I’ll pay attention to George when
he talks louder. He never says
anything wrong but his intellect
is dying with him.
George walks back to George’s parents.
INT. JERRY’S APARTMENT - NIGHT
Kramer enters quickly, sliding around the floor on his knees.
Jerry is watching TV. Jerry notices something in the
refrigerator. Elaine smoking at the crowd.
KRAMER
Hey I’m thinking Jerry, I’m really
sweating Jerry. Jerry, I have a
woman comin and she does it mean,
you know?
JERRY
(relaxously handily talking)
Well, the couch is not going to be
washed by me you know, I’ve got
Knick tickets this Wednesday and
George gets soup on the road.
Snacks are piled high in the guest room. Jerry spots them and
takes a photograph.
KRAMER
(cynical voice)
Jerry can you be a big salad and
give me a meal?
George enters acting very strange. He picks up the remote
control and tries to run with it.
3.
JERRY
Hey George, what’s wrong with you?
GEORGE
Oh Jerry, Jerry just sit there and
try to hear my machine. Jerry I
really have a hot dog in there and
I’m not going to lie to it.
JERRY
What’s the difference? You became
a legend, I got a big picnic.
George, Elaine and Jerry lean back and forth. Elaine, laughing
hypocritically, sports a couple of muscle relaxers. Jerry
looks at the psychic’s apartment, the phone and George. Helen
walks toward Elaine and George is a double look at the
bathroom. George is touching the cowbanes. Kramer is barely
laughing. Elaine making the newspaper. Jerry looks at Jerry.
Jerry unlocks the hot ends. He like it. Jerry is not heard
with his fingers. Jerry walks out of the blade, with a music
pause.
CUT TO:
INT. COMEDY CLUB
JERRY
No no no no no no no no god no no
god no you should get out of here
right now. You’re real turkey club
without any crackers, aren’t you?
What’s the deal with this ping
pong guy? He mumbles something
like ya ya huh ha ha oh no no go.
I’ve never been called an egg but
you could do it.
ROLL CREDITS

This is part of a series of posts about the ‘little of visualisation design’, respecting the small decisions that make a big difference towards the good and bad of this discipline. In each post I’m going to focus on just one small matter – a singular good or bad design choice – as demonstrated by a sample project. Each project may have many effective and ineffective aspects, but I’m just commenting on one.

---

The ‘little’ of this next design concerns z-sorting and how you manage the depth-arrangement of overlapping value series. The chart in question comes from an article by FiveThirtyEight titled ‘The National Parks Have Never Been More Popular‘.

The bump chart plots the relative popularity, in ranking terms, of the US national parks going back over 100 years. With an increasing range of recognised parks and, one must assume, an ever-expanding register of visitor data, there is a lot to plot in one chart and a risk of creating lots of noise and limited signal.

Three key things happen here. Firstly, only 11 of the parks are coloured, creating editorial emphasis versus all the other categorical series, which are included but presented in grey and without labels. Secondly, you will see there is a white outer-stroke wrapped around the coloured lines to help them pop-out a little further. Thirdly, and the main thrust of this post, is the decision to apply z-sorting on the coloured lines whereby the most recent rankings influences the overlapping order when lines meet back through the timeline. So, for example, The Great Smoky Mountains line is always on top, the Grand Canyon will then be on top of all other lines apart from the Great Smoky Mountains etc.

The post The little of visualisation design: Part 43 appeared first on Visualising Data.

Visual editor Xaquín G.V. recently used the distracted boyfriend meme to represent our attraction to novel visualization methods when a simple and visually sound method is right there at our disposal.

Then he ran with it to illustrate his professional sins as an editor for a news desk.

Tags: meme, sins

submitted by /u/VaporizerWizard [link] [comments]

submitted by /u/waltc97 [link] [comments]

submitted by /u/savagedata [link] [comments]

Bubbles. 

Twitter, Facebook.

staff:

@thedailytask.tumblr.com 

@beesandbombs.tumblr.com

@vhspositive.tumblr.com

@the-wolf-and-moon.tumblr.com (see also this 1-bit conversion by lucichrist.tumblr.com)

“The eclipse is beautiful,” raves @retrogamingblog.tumblr.com

hope you enjoyed the eclipse if you were lucky enough to be in its path :)

submitted by /u/scizormytimbers [link] [comments]

submitted by /u/Poutchika [link] [comments]

submitted by /u/jitney86 [link] [comments]

We’ve seen faces as map projections before, but this is 63 projections on one page. Plus, you can click and drag to change the center points to see how different parts of the face change.

Tags: projections

submitted by /u/Jholder112233 [link] [comments]

szimmetria-airtemmizs:

If you look closely you will see that I only used one type of quadrilateral to make this. 

I made a print based on this one. Its on my door now:)  

submitted by /u/DLC204 [link] [comments]

The Moon will totally eclipse the Sun for the first time as seen from the continental United States in more than 40 years on August 21, 2017. What are eclipses, and what's special about this one?

chequered waves

submitted by /u/smith2017 [link] [comments]

bigblueboo:

ἤḕɡᾅȽḯⅴἱʈγ

pr1nceshawn:

Stop Clickbait.

How to animate cube in Houdini

A friend of my friend (XAPKOHHEH) just made this.
I’m totally into Houdini the last Months - I had to share 

EDIT
while this is extremely funny, still, it’s showing perfectly what is Houdini capable of. Made in 1 day :o
Note: he shared source file in the description - if interested 

curiosamathematica:

Today I learned about a rather remarkable open problem in mathematics, which looks tantalizingly easy. The question was posed by Ron Graham.

Consider the following recursively defined sequence:

Innocent question: is this sequence unbounded? Surprisingly, the answer to this is unknown—at least according to the source article dating from 2000, Unbounded orbits and binary digits by M. Chamberland and M. Martelli.

(Source: http://www.math.grinnell.edu/~chamberl/papers/mario_digits.pdf)

Here is a very similar problem where we know the answer.

The question is the same, is this sequence bounded or not? In other words, is it possible that the sequence never goes above a certain number?

Try to solve it! It is not easy but you don’t need any kind of advanced tools. 

submitted by /u/hikemix [link] [comments]

Welcome to the summer American Astronomical Society (AAS) meeting in Austin, Texas! Astrobites is attending the conference as usual, and we will report highlights from each day here. If you’d like to see more timely updates during the day, we encourage you to search the #aas230 hashtag on twitter. We’ll be posting once a day during the meeting, so be sure the visit the site often to catch all the news!

---

Plenary Session: George Ellery Hale Prize, The Solar Magnetic Field: From Complexity to Simplicity (and Back) (by Benny Tsang)

The morning plenary session started with the George Ellery Hale Prize presentation of our speaker Manfred Schüssler (Max Planck Institute for Solar System Research) for his “outstanding contributions over an extended period of time to the field of solar astronomy”. Eugene Parker, who first discovered the magnetism and polarity of sunspots and who we named NASA’s new Sun-touching spacecraft after, was the first scientist to have received this honor. Today Schüssler led us on a journey to disentangle the Sun’s complex magnetic field with simple models — can this really be done?

 

To get a sense of the level of complexity in the magnetic structures of the Sun, let’s first take a look at some images. On the seemingly simple and boring surface, we see tiny features around sunspots (middle panel) and granules (hot, rising pockets of gas; right panel). In addition, all these are highly turbulent and dynamical, so we are faced with the challenge of explaining a hierarchy of time-varying complexities on a wide range of scales.

Numerical simulations have tried to reproduce the observed features by including physics at different scales — from the near-surface layer, to the deeper layer where the magnetic field is believed to be created, to the whole convection zone. Although simulations are not perfect in reproducing all features, Schüssler stressed that they offer an otherwise unavailable 3D view of the Sun, which allows new questions to be asked. Among all, the small-scale dynamo model shows the most promising prospects for explaining most of the observed small-scale structures. This dynamo process is so fundamental that it is believed to prevail even when the first generation of stars were born.

The Sun can be quite predictable in its own way. The highly regular, 11-year cycle of sunspot activity and the 22-year field direction reversal are two examples. Such regularities can be understood by the Babcock and Leighton (BL) model pretty well, which describes the activities as driven by the twisting of magnetic field lines in the Sun by its rotation. That said, the full picture of Solar magnetism is still far from being complete. As an example, Schüssler noted that the emergence of magnetic field deeper in the Sun (flux emergence) assumed in the BL model seems to be extremely complex in and of itself. Future scientists, I think we could really use some help here.

---

 

Press Conference: Bending & Blending (by Benny Tsang)

The last press conference of this AAS meeting featured two speakers and had a rather enigmatic title: Bending and Blending. To summarize in one sentence, it was about the bending of light by a white dwarf, and the blending of a suite of versatile tools for better data visualization.

 

Kailash Sahu (Space Telescope Science Institute) led the discussion of a truly exceptional microlensing event. One of the crucial tests of Einstein’s theory of general relativity is the bending of light around massive objects. Unlike typical gravitational lensing by clusters of galaxies, microlensing events are caused by objects with stellar or planetary masses. Sahu’s team observed a foreground white dwarf (Stein 2051 B) deflecting light of a background star. By analysing the images formed by this “white dwarf lens”, they estimated its mass to be 0.675 times the mass of the Sun (with ~7% error). Until this discovery, all mass estimates of white dwarfs have relied on binary systems. Sahu’s discovery opened up a new way to measure white dwarfs’ masses, which could empower many new discoveries in astronomy. [Full press release]

Aside: If you wish to do your personal gravitational lensing observation, there’s a chance during the upcoming total Solar eclipse event on Aug 21. We can all be part of it!

 

Next, Brian Kent (National Radio Astronomy Observatory) illustrated the multi-purpose, well-documented, scientific data visualization tool he built, known as Blender. Data from multi-wavelength observations and advanced supercomputer simulations have been growing in both size and complexity. Not only is visualization required to help communicate new discoveries to the general public, but scientists themselves rely heavily on efficient visualization tools to make discoveries in the first place. Recently Kent has even combined Blender with Google Spatial Media to “put data in the hands of the audience” — data visualization on users’ mobile devices. We can start making our own scientific art pieces now by following the tutorials and reading the new Blender book! [Full press release]

---

 

Plenary Session: CANDELS: A Cosmic Quest for Distant Galaxies Offering Live Views of Galaxy Evolution (by Benny Tsang) 

Inventor of photometric redshift measurement David Koo (University of California, Santa Cruz) told the story of the cosmic quest to understand galaxy formation. Having recently retired to “finally do research full-time”, Koo started by clarifying a common question about the CANDELS program — the name ‘CANDELS’ is indeed an intentional misspelling to avoid generic results on search engines. CANDELS is a Hubble Space Telescope legacy survey with an unprecedented amount of data, providing both wide and deep coverage of galaxies. The entire image database consists of 250,000 galaxies from redshift of 1.5 to 8.

 

Why do we want to get yet more data on distant galaxies? It is obvious from a quick glance at the Hubble Deep Field that cosmic environments vary a lot — we see galaxies of different shapes and colors! Moreover, galaxies cannot be neatly divided into discrete types; they interact with each other and evolve. A large amount of data is therefore needed to cover a representative volume of the Universe in order for a galaxy evolution study to make sense.

With complementary coverage by Herschel and Spitzer (infrared), Chandra (X-ray), and GALEX (ultraviolet), we earn the bread and butter for galaxy evolution, e.g. stellar mass, size, star formation rate, and morphology. In particular, the addition of the X-ray band provides important hints about galaxies’ central supermassive black holes. An important component of the CANDELS program is the inclusion of theorists working with N-body and hydrodynamical simulations. By reproducing observed galaxies from first principles, simulations allow us to track them back in time (like rewinding a movie) to see the processes of their evolution.

 

Throughout the talk Koo filled the entire hall with his warmth, and he didn’t hesitate to give thanks to his team. Besides the principal investigators Sandra Faber (who has won the Bruce Gold Medal, the “cosmology Nobel prize”) and Henry Ferguson, he also thanked astronaut Andrew Feustel for installing the camera that made CANDELS possible! With the prospects of new telescopes such as JWST, ALMA, SKA, and those of decades to come, Koo echoed Casey on Day 1, envisioning that detailed mapping of gas and dust is the future of astronomical observations.

Title: Jupiter’s interior and deep atmosphere: The initial pole-to-pole passes with the Juno spacecraft

Authors: S. J. Bolton, A. Adriani, V. Adumitroaie, M. Allison, J. Anderson, S. Atreya et al.

First Author’s Institution: Southwest Research Institute, San Antonio, USA

Status: Published in Science, [open access]

When we gaze upon the night sky, we can often spot Jupiter. We have also launched several missions to explore Jupiter, like Pioneer, Voyager, and Galileo. Yet all we have “seen” stops at the cloud tops. Now our understanding is going to change by NASA’s Juno mission, which provides us the privilege to have a glimpse through the clouds. The primary science goal of Juno is to measure the deep composition and internal structure, so that we can better understand the formation and evolution of Jupiter and planetary formation in general. Juno takes elliptical orbits around Jupiter to minimize the damage from the radiation belt. Once every 53 days, Juno accomplishes a close flyby and takes as many photos as possible. Today’s paper brings some exciting new results from first few close passes of Juno probe.

 

Jupiter’s poles

Before Juno, we never had a close look at Jupiter’s polar regions. Figure 1 shows the snapshots of north and south poles taken by the visible-light camera, JunoCam, with a resolution of 50 km. From this amazingly detailed image, we see that the familiar band and belt zonal structure vanishes at about 60° latitude. It is replaced by ovals and spiral-like features, which are revealed to be cyclones and waves in the temporal sequence of shots. It is now very interesting and challenging to explain the dynamical transition from bands to cyclones and the differences from Saturn’s poles (e.g. Saturn has north polar hexagon).

Ammonia

MWR (the microwave radiometer) measures the thermal emission coming from below the cloud deck, which provides very powerful eyesights through the clouds with its large antenna. Figure 2 displays the brightness temperatures measured by MRW at different wavelengths, corresponding to emission flux from different depths. The team argues that the ~50 K variations of brightness temperature are not due to physical temperature since the equatorial wind would have been much greater in that case. Instead, they are caused by the variations of microwave opacity, and ammonia is the dominant microwave opacity source. Therefore, the authors work out a global ammonia abundance that best matches the observed brightness temperatures, as shown in Figure 3. Again, surprisingly, ammonia is not well-mixed, as predicted by the equilibrium chemistry. There is an equatorial plume lifting ammonia up and descending at higher latitude, resembling a Hadley cell on Earth. If the ammonia distribution is indeed driven by the circulation below the cloud layer, we definitely need some new ideas and models to understand what drives the circulation!

Gravity field

By measuring the Doppler shift of radio signals, Juno can also set constraints on Jupiter’s gravity field. What they found now is the current interior models do not agree precisely with the Juno data. These can be contributed to several uncertainties such as the equation of state for hydrogen-helium mixtures, the presence of a core, the assumption of an adiabatic interior, and the differential rotation (different part of the body at different latitude/depth rotates with different angular velocity). We would get a better handle on this with further Juno measurements of smaller components of the gravity.

It is usually more exciting to see unexpected things. What Juno has found so far hints us that our models of giant planets may be a little too oversimplified. Juno will continue more close passes around Jupiter until 2018, so we can expect more surprises and puzzles to come!

 

Someone sent me this chart via Twitter, as an example of yet another terrible pie chart. (I couldn't find that tweet anymore but thank you to the reader for submitting this.)

At first glance, this looks like a pie chart with the radius as a second dimension. But that is the wrong interpretation.

In a pie chart, we typically encode the data in the angles of the pie sectors, or equivalently, the areas of the sectors. In this special case, the angle is invariant across the slices, and the data are encoded in the radius.

Since the data are found in the radii, let's deconstruct this chart by reducing each sector to its left-side edge.

This leads to a different interpretation of the chart: it’s actually a simple bar chart, manipulated.

The process of the manipulation runs against what data visualization should be. It takes the bar chart (bottom right) that is easy to read, introduces slants so it becomes harder to digest (top right), and finally absorbs a distortion to go from inefficient to incompetent (left).

What is this distortion I just mentioned? When readers look at the original chart, they are not focusing on the left-side edge of each sector but they are seeing the area of each sector. The ratio of areas is not the same as the ratio of lengths. Adding purple areas to the chart seems harmless but in fact, despite applying the same angles, the designer added disproportionately more area to the larger data points compared to the smaller ones.

 

In order to remedy this situation, the designer has to take the square root of the lengths of the edges. But of course, the simple bar chart is more effective.

 

 

Ice in Antartica is in constant (very slow) motion, and as ocean waters warm, the flow of ice accelerates. The New York Times mapped the flows, showing where the ice is headed.

And, if you’re interested in how they did this, NYT graphics editor Derek Watkins provides the rundown.

Tags: environment, glaciers, New York Times

2016 brought us the world’s blackest black, Vantablack, while 2017 has already introduced us to Harvard’s collection of the world’s rarest colours. We now have the first group of colours created by AI. Research Scientist, Janelle Shane, who took a neural network (for non-sciencey brains, that’s an artificial network made up of a number of computer systems), and tasked it with creating a unique set of colours with accompanying names. Janelle states, “for this experiment, I gave the neural network a list of about 7,700 paint colours along with their RGB values. (RGB = red, green, and blue colour values). Could the neural network learn to invent new paint colours and give them attractive names?”.

The answer was yes (well, almost!) Janelle created an algorithm for the network that completed two tasks: the creation of the RGB value of the colour and the selection of letters to form the colour name. The first results were promising, and the AI had managed to produce valid RGB values, however, the punchy and eye-catching names were lacking a little, and it seemed to be favouring brown, blue and grey hues.

The network developed further and could soon spell green and grey and was expanding its palette of colours, however it was failing to place the green and grey terms alongside the relevant colour.

Then the more creative (we’re not sure we’d be able to pronounce them!) names began.

Finally, the network reached a level of intelligence where colours matched names (almost!). You could see some of the below being right at home alongside the likes of Farrow & Ball’s Elephant’s Breath!

The post World First: Paint colours generated by AI appeared first on The Chromologist.