Well-being in the San Francisco Bay Area

At the University of Pennsylvania’s Positive Psychology Center, Martin Seligman and more than 20 psychologists, physicians, and computer scientists in the World Well-Being Project used machine learning and natural language processing to sift through Twitter. They have been able to rank each of the 3235 U.S. counties according to well-being, depression, trust, and five personality traits.

For the Bay Area, the rankings are:

Rank	County	Percentile
8	Marin	99.8
17	Sonoma	99.5
26	San Francisco	99.2
36	Napa	98.9
37	San Mateo	98.9
61	Santa Clara	98.1
120	Santa Cruz	96.3
341	Alameda	89.5
455	Sacramento	86.0
505	Contra Costa	84.4
1429	Solano	55.9

If you live in the U.S., you can check your county in their online map. For example, Kings County in New York ranks 448, while the District of Columbia ranks 49.

How is your well-being?

Unique hues

Color does not exist in nature, it is an illusion happening in our mind. Therefore, we can not solve color appearance problems with physics alone. The situation gets even hairier when we converse about color, because color names are just an ephemeral convention. For example, it is easy to "synonimize" blue and cyan or red and magenta.

Especially the latter is not out of the blue. Back in the days when I was doing research in color science, I had an OSA-UCS atlas. Periodically, I was getting updates of color tiles. One tile was standing out: the one that Boynton and Olson had determined to be the best red, with coordinates (L, j, g) = (–3.6, 1.5, –6.9), representing the consensus for the basic color term "red." Not only was it changing, but is was changing frequently. When I lined the updates up in the light-booth, the best red was shifting towards magenta.

I was so puzzled about the OSA changing the tile for the best red, that I called them up. I found out that they periodically where getting new tiles from their supplier Dupont when they developed new pigments. The OSA would then scale them visually and emit updates to the atlas. Therefore, the shift of basic red was a sign that industry was getting better at creating what humans consider to be the best red, and the hue was shifting from yellow towards magenta.

This indicates that the above synonymity is not arbitrary.

Art teachers are the ones most victimized by the non physicality of color, especially when they have to teach the difference between additive and subtractive color, as I wrote in "Is it turquoise + fuchsia = purple or is it turquoise + fuchsia = blue?" a few years ago. Recently I received the following question: do you have any ideas as to how I can prove that red, green, yellow and blue are the unique hues?

It is not that Goethe's yellow-red-blue (today we would write yellow-magenta-cyan) primary theory is wrong. His 1810 Farbenlehre and his battle against Newton with the theory of red-green-blue primaries was a little weird, especially considering Newton (1642–1727) had long been dead when Goethe started fighting against him.

Go back to the 1470s in Florence. The 1453 collapse of the Roman Empire of the Orient (Byzantium) had generated an influx of scientists fleeing from Constantinople to Venice, who took along their over 1500 year old libraries. Aldo Manuzio, exploited this trove of works and cheap scientists to create the new profession of "publisher" and popularized this knowledge that had gone lost in the west. This lead to the Renaissance in Italy, which essentially consisted in taking a field, studying all books about it, and formulating a theory about this field, which can be used to apply it over and over. An example was Niccolò Machiavelli studying politics and creating the field of political science.

At that time, somebody we would now call "engineer" did not go to study at a university but to a place called a "bottega" or "workshop" in English. One of the most famous ones in Florence at that time was the Bottega del Verrocchio, run by Andrea di Michele di Francesco de' Cioni. Verrocchio was his nickname and can be translated as "true eye."

In the pictorial arts, at that time the hot trend was to use very vivid colors and to try to reproduce reality as faithfully as possible. In Verrocchio's bottega, students would learn about pigments and binders and research the creation of new paints to realize highly vivid colors.

One of these students was a lad from the village of Vinci, called Leonardo. Leonardo was passionate about this quest for realism and would dissect cadavers at the hospital to learn how muscles and tendons worked anatomically. For color, he invented a new method to analyze a scene consisting in viewing it through a series of stained glasses (a common novelty in the street markets of the 1470s). Today we would call this a spectroradiometer.

From his studies, Leonardo da Vinci formed a new theory that color is not a physical quality mastered by studying pigment admixtures, rather it is a new phenomenon he called "perception." Perception is the capacity to have a sensate or sentient experience, what philosophers call quale (the plural is qualia). Quale is what something feels like; for example, the smell of a rose or the taste of wine as specific sensations or feelings. We experience the world through qualia, but qualia are not patterns of bits in memory, far from it.

From this he developed a methodology to paint the light illuminating a scene instead of the scene itself. The technique he developed was to paint in many layers with very little pigment. He called this technique "sfumato" and the result "sfumatura."

Working with the spectroradiometer, he realized that colors are not a discrete set with the names of pigments. Rather it was a set of opponents. Doing his drawings he realized, that white and black are actually colors (in 1475 they were not considered to be colors) and they are opposites he called "chiaro" and "scuro", resulting into a technique he called "chiaroscuro."

Once you realize colors are not related to pigments, you have to come up with a representation based on perception. This is where language comes in. When describing a color in 1475, you would say for example "this color is a vermilion with a hint of lapislazzuli" to describe a certain pink. So in 1475 the primary colors were the pure pigments, like oltremare (lapislazzuli), azzurro (azzurrite), indaco, tornasole, vermilion, gold, etc.

Some pigments like lapislazzuli were very expensive and others like azzurrite were more affordable. Admixture would allow you to obtain a given color with a cheaper paint that elicits the same perception. Therefore, Leonardo concluded that the old naming scheme is not useful. To find a representation, he then set out to find if there are any perceived colors that you would never describe as an admixture of two or more colors. This lead him to red, green, yellow, blue, black and white, which are his unique hues.

Further, he discovered that you would never use descriptions like reddish green, bluish yellow, or dark white. This is what he called the "color opponent system" and allowed him to describe perceived color as points in a 3-dimensional space. This is the basis for CIELAB and for the NCS color atlas. For the Munsell color tree there is an extra step for purple.

With this you have the answer to the above question: you have to distinguish between the creation of color with colorants and the perception of color.

Traps in big data analysis

When I was a student, I had chosen mathematical statistics as one of my majors. At the time, the hot topics were robust statistics, non-parametric methods and optimal stopping times. Descriptive statistics was not part of the curriculum (PowerPoint did not yet exist and there was no need for meaningless 3-D pie charts).

In the student houses I lived, there were always medical students at the end of their studies who had to get a doctorate. Residencies were grueling and at that time the least effort thesis was to punch in some historical medical data. On their way home from the clinic, these students would spend part of the night in the empty punch card rooms, for about 6 months.

Thereafter, they would bring the punch cards to the data center and get 10 to 20 centimeters of SAS printout—and the desperation of not knowing how to get from hundreds of cryptic tables to a one hundred page thesis.

Many of them ended up knocking on my door with the printout and scratching their head. Because in the data center the students could not tell what analyses they needed—after all, there never was an experimental design—the data center people just ran all and every function available in SAS. Classical garbage-in garbage-out.

So, I had to tell the students to stare at the data and come up with a few hypotheses, then use the ANOVA routines to confirm them and the regression routines to do a few nice graphs.

Unfortunately, after all these years we are not much better off. Indeed, now we have to deal also with "big data hubris," the often implicit assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis. Now we have tools like Google Correlate that allow us to correlate tons of apples with megatons of oranges.

A recent interesting paper by David Lazer et al. is a nice summary of how big data analysis allows us to create more statistical garbage: Lazer D, Kennedy R, King G, Vespignani A. Big data. The parable of Google Flu: traps in big data analysis. Science. 2014 Mar 14;343(6176):1203-5. doi: 10.1126/science.1248506. PubMed PMID: 24626916.

The authors conclude: "Big data offer enormous possibilities for understanding human interactions at a societal scale, with rich spatial and temporal dynamics, and for detecting complex interactions and nonlinearities among variables. We contend that these are the most exciting frontiers in studying human behavior. However, traditional 'small data' often offer information that is not contained (or containable) in big data, and the very factors that have enabled big data are enabling more traditional data collection. The Internet has opened the way for improving standard surveys, experiments, and health reporting. Instead of focusing on a 'big data revolution,' perhaps it is time we were focused on an 'all data revolution,' where we recognize that the critical change in the world has been innovative analytics, using data from all traditional and new sources, and providing a deeper, clearer understanding of our world."

AIC Study Group on the Language of Color (LC)

During the AIC meeting in Zurich, the AIC Study Group on the Language of Color (LC) held a meeting, moderated by the Chair Prof. Jinsook Lee. During the meeting, the delegates discussed two project proposals by Co-Chair Prof. Paul Green-Armytage.

Honeysuckle

Honeysuckle is not a color term in Nathan Moroney's color thesaurus, meaning none of the contributors to the online color naming experiment ever mentioned this term. It is a reddish pink, which is a color term in his dictionary. Unfortunately Google's Book Ngram Viewer is not of much help, because it cannot distinguish the color term from the widely distributed climbing shrub with tubular flowers that are typically fragrant and of two colors or shades, opening in the evening for pollination by moths. • Genera Lonicera and Diervilla, family Caprifoliaceae (the honeysuckle family).

Indeed, the graph (click on it for a larger view) shows a much higher frequency for honeysuckle than for rose pink, which is just a color term and would be more frequent than honeysuckle.

Nevertheless, this year you will hear it often used as a color term. Indeed, Pantone has declared it the color for 2011. Its formal specification is PANTONE 18-2120 Honeysuckle.

"In times of stress, we need something to lift our spirits. Honeysuckle is a captivating, stimulating color that gets the adrenaline going — perfect to ward off the blues," explains Leatrice Eiseman, executive director of the Pantone Color Institute. "Honeysuckle derives its positive qualities from a powerful bond to its mother color red, the most physical, viscerally alive hue in the spectrum."

Eiseman continues, "The intensity of this festive reddish pink allures and engages. In fact, this color, not the sweet fragrance of the flower blossoms for which it was named, is what attracts hummingbirds to nectar. Honeysuckle may also bring a wave of nostalgia for its associated delicious scent reminiscent of the carefree days of spring and summer."

Now we have to see how long it will take for honeysuckle to show up in Nathan's color dictionary and thesaurus.

Is it turquoise + fuchsia = purple or is it turquoise + fuchsia = blue?

In five minutes we are presenting our paper on arithmetic with color terms in the Harbour Room A here at EI. In case you did not make it, here are our slides:

Is it turquoise + fuchsia = purple or is it turquoise + fuchsia = blue? from Giordano Beretta

Here are the speaker's notes:

{Color diagrams by scientists}

When color scientists draw chromaticity diagrams to illustrate their publications, they do it in black and white. Color, at most, is used for annotation purposes, not as self-reference.

{Color diagrams by designers}

This frustrates color designers, who make ample use of colors and expect more guidance from color scientists using the designer's language. Therefore, they prefer colorized chromaticity diagrams and they like how they can pencil in color theories because chromaticity diagrams can be used to predict the mixture of aperture color.

However, when they explain color theories using color terms, they get confused, because they have samples for the basic color terms and use them to label the colorized chromaticity diagram.

{sRGB color diagram}

In fact, the colorization of chromaticity diagrams is just that: a colorization. It bears little relation to the color's self-reference, because there is not device gamut covering the entire chromaticity diagram.

This figure shows how a chromaticity diagram can be more accurately colorized on an sRGB device.

{It is 3-dimensional!}

In the yellow range, the colorization fails and sticks out of the chromaticity diagram. This is because the gamut is tridimensional and the gamut rendering algorithm is somewhat imprecise. On the right side we move the viewpoint.

{Printer gamut}

You are seeing the projected image from a beamer, which in reality is not an sRGB device, so right now you are not seeing the correct self-referenced colors. In the case of a printer the error is even larger, but if for a moment you imagine viewing the diagram at right on a SWOP printer, you realized how off the colorized chromaticity diagram we saw earlier is.

{Color theories — an abstraction}

From a color science point of view, the various color theories have their own problems. Compare for example Leonardo's color circle with that proposed by Itten. The color are quite different. What happened?

{Itten revealed}

Of Leonardo we know that he considered color to be a perceived entity and his theory is based on color opponency. We discover Itten's method by looking at the black and white version of the color circle published in the student version of his book: He simply started with monolexemic color names and then interpolated with bilexemic color names to obtain a richer circle.

{Color by measurement}

We conclude, that we cannot predict arbitrary color mixtures by doing arithmetic with color terms obtained from colorized chromaticity diagrams. The general way to predict color mixtures is to reproduce actual samples and then measure them.

{Naming color}

How then can we determine the proper color terms? The World Color Survey has shown how to do it for the colors on the surface of the Munsell color gamut, and Boynton and Olson have shown how to do it for the full OSA color gamut.

Both experiment classes are limited to the basic color terms. For a large number of color terms, we need data from thousands of observers, which we can hardly do using controlled psychophysics techniques. Possible approaches include Amazon's Mechanical Turk and open Web surveys based on crowd-sourcing.

{Validating crowd-sourcing}

Unfortunately, non-withstanding Boynton's positive assessment of the uncontrolled protocol used in the World Color Survey, crowd-sourcing is still considered very controversial in the color science community. Therefore, we decided to perform a controlled experiment with an unusually large number of observers.

{Color chips given and color seating arrangement}

We printed a number of color chips and gave them to the researchers having lunch at the cafeteria and asked them to sit at the labelled tables with the term closest to their color chip.

{Calibrated lunch vs. Web color centroids}

As you can see from this diagram, we achieved excellent correlation between the controlled lunch room experiment and the uncontrolled crowd-sourced experiment on the Web. Thus, our data from the Web is scientifically valid.

{Beyond crowd-sourcing}

One question arising when teaching color design is which synonym to use for a given color. To answer this question we need more data than even crowd-sourcing can provide. Also, we have repeatedly written how color terms are ephemeral and change with time.

In this diagram we used Google's English book corpus and tracked the number times fuchsia and magenta appear in book each year from 1800 to 2000. The appearance of the term magenta coincides with the historical facts and the numbers suggest your should prefer the term magenta over fuchsia.

{What is the best term for RGB = (0, 1, 1)?}

This diagram illustrates how cyan is not a good choice and you should use the term turquoise instead.

{Is the term for RGB = (1, 1, 0) monolexemic?}

We can use this approach also to answer very difficult questions, like the number of basic color terms. This diagram illustrates that olive green might be becoming a basic term also in English, because chartreuse is monolexemic.

Our data shows that few people ever learned to correctly spell fuchsia, but the data shows this is not critical. However, you should definitely learn how to spell chartreuse.

which color synonym should I use?

Recently we wrote about magenta and fuchsia being synonyms, and on Water Day we wrote about the difference between cyan, aqua, and (for the horizontal readers of our blog using the etymology tag) turquoise, (see also here, since we are still in 2010). The question we left open was how to choose between color synonyms. As color scientists, we do not want to come across as being too geeky or おたく…

From today, there is a tool to answer this question.

In the old days of color science, in psychophysics we dealt with about 15 subjects (logarithmically: 1). Then with the multilingual color naming experiment, HP Labs ratched this up to thousands (logarithmically: 3). Today, Google Labs is taking this to the next level: millions (logarithmically: 6), or 5,195,769 observers if you need an exact number.

When the color naming experiment allowed you to find color names and their synonyms, the new corpus allows you to decide which synonyms to use. Let us get back to magenta and fuchsia. You are at Silicon Valley cocktail party, want to talk about your daughter painting her room in hot lips color, but do not want to come across as a geek. Should you call it magenta or fuchsia?

First learn our latest tag: culturomics. While our technological advantage was crowd-sourcing, the next level is culturomics, where instead of harvesting an audience, all books are interrogated. Moreover, while crowd-sourcing was ephemeral, culturomics introduces time as a new parameter. The details are in the upcoming Science magazine paper Quantitative Analysis of Culture Using Millions of Digitized Books, by Jean-Baptiste Michel1, Yuan Kui Shen, Aviva P. Aiden, Adrian Veres, Matthew K. Gray, The Google Books Team, Joseph P. Pickett, Dale Hoiberg, Dan Clancy, Peter Norvig, Jon Orwant, Steven Pinker, Martin A. Nowak and Erez Lieberman Aiden.

Essentially, Google Labs today is making 5,195,769 of the 15 million books they have digitized available for research. To put the number in perspective, these are about 4% of all books ever published.

So, is it magenta or fuchsia? [click on the graph to view it at full size]

In harmony with what we wrote, fuchsia has been around since 1730, while magenta emerged 1851 with the battle in Magenta. While the first use in books was probably regarding Magenta, uses after London's 1860 red magenta definitively refer to the color. The above graph teaches us, that when talking to non-scientists we should use magenta rather than fuchsia, even when the former is currently dipping.

This is good. After all, the color naming experiment teaches us that nobody knows how to spell fuchsia anyway.

Now, let us look at cyan. [Click on the graph to view it at full size]

The corpus queried was English books, specifically, Italian books were excluded. Although the Latin spelling aqua has always dominated, up to 1870 the Italian spelling acqua was sometimes used. At the cocktail party, however, you should call it turquoise.

Although this is a quantum leap, it does not yet solve the hot lips problem, because there is not enough data to isolate the context of color. For that we will have to progress beyond books, but Google does not yet know how to structure its corpus, as we wrote a while back. Will it be Bing?