When I was in high school, Robert Coles came to speak. Robert Coles is the child psychologist who worked with six-year-old Ruby Bridges in 1960, the year that she, by herself, integrated William Frantz Elementary School in New Orleans. The details of Ruby Bridges’ story are extraordinary.

Coles talked to a [...]]]> Kindness

When I was in high school, Robert Coles came to speak. Robert Coles is the child psychologist who worked with six-year-old Ruby Bridges in 1960, the year that she, by herself, integrated William Frantz Elementary School in New Orleans. The details of Ruby Bridges’ story are extraordinary.

The Problem We All Live With, Normal Rockwell

Coles talked to a packed gym – standing room only. He spoke with a singular and memorable refrain:

Be kind, be kind, be kind.

Afterwards, in a more intimate discussion, a teacher challenged him. Life’s rough, he said. We’re trying to prepare these kids for that. You aren’t actually suggesting we be kind all the time? They parried; the teacher was angry and would not back down. The conversation, and the room, was heated and uncomfortable.

Congress

Lately I’ve been spending a lot of time with the U.S. congressional record. It is a surprising and strange text, sometimes infuriating, sometimes touching, often boring.

Sunlight Labs’ Capitol Words API is a great tool for searching phrases in the congressional record; it’s based on the full text of the congressional record since 1996.

I searched “kindness” and this is what I found.

The word kindness in the congressional record is used for recognition, remembrance, protocol, and prayer. Recognition of the kindness of individual people, communities, and events. Remembrance of kindness in a eulogy. Protocol appears as “I thank the Senator for his kindness in yielding me the floor.” Prayer is kindness appearing in the daily opening prayer.

Rarer uses of kindness include the political use of “the kindness of strangers.” Representatives generally feel that the U.S. has become, or is at risk of becoming, overly dependent on the kindness of strangers. Kindness is also sometimes used to describe people’s actions during natural disasters or terrorist attacks.

Congressional Kindness is a serendipity tool, and an experiment in small data, in showing the countability of kindness in the congressional record.

One of the most recent mentions of kindness comes in an April 23 eulogy of the AI researcher David Waltz.

The lessons that teachers, researchers, supervisors–in fact any of us here in this House or elsewhere–can draw from David Waltz are not primarily about computer science and artificial intelligence. They are that the greatest creativity comes from inspiring others; the greatest technique of team-building is listening; the greatest innovation comes from devoting time to others’ ideas; and the greatest wisdom is kindness.

What was Robert Coles’ final answer to my questioning teacher?

Of course, kindness is hard. Kindness is complicated. But yes, it can be done.

We took all our whimsy and some code to San Francisco last weekend to participate in Hypothesis: An Art/Science Fair at The Lab. The event was fantastic, with a huge turnout despite a downpour, and interesting projects from all artist/scientist participants. Our project was mobbed most of the night, and we took [...]]]>

We took all our whimsy and some code to San Francisco last weekend to participate in Hypothesis: An Art/Science Fair at The Lab. The event was fantastic, with a huge turnout despite a downpour, and interesting projects from all artist/scientist participants. Our project was mobbed most of the night, and we took home the Audience Engagement ribbon!

The Idea: Make something interesting and engaging by combining face tracking with some randomness and math to create a personality prediction. This was inspired in part by the idea that eigenvalues are the ”characteristic” values of a matrix.

We aim to discover what we can create by intentionally making mistakes.

The Process: The project creates an adjacency matrix (0s and 1s) using facial parameter output from Kyle McDonald‘s extraordinary and open-source FaceOSC. The adjacency matrix is translated into a graph based on Daniel Shiffman’s force directed graph example for Processing. Eigenvalues for the matrix are used to compute the number of nodes and edges in the graph, as well as the maximum number of triangles and the maximum degree. (Maximum degree is the greatest number of edges to any node.) Nodes, edges, triangles, and max degree are each mapped to a personality prediction. From face, through math, and back to character.

Caution: Science in Progress. By reading this you give your consent as a participant.

The Performance: In order to perform science in real-time, we asked participants to fill out a survey based on their experience with the tool. Number of nodes, edges, triangles and max degree was collected for each participant, and everyone answered two questions:

1. How well does this describe you? (5-pt Likert scale)
2. Is this something or nothing?

Results were tabulated and reported at the end of the night.

The Results: There existed a weak positive correlation between number of triangles and self-reported accuracy of the personality prediction, which supported our hypothesis:

The performance of science can create correlations between unrelated parameters. We expect to find relationships that should not exist.

Of the 55 participants, 78.2% found the project to be “something” and 10.9% found it to be “nothing.” 4 participants made no choice, 1 wrote “meow” and 1 wrote “middle.”

The Conclusion: Success! (Or at least “something.”) Thanks to everyone at The Lab, especially Rachel Beth Egenhoefer, for putting on the show.

The Title: Night’s Starred Face: The Magic Hand of Chance

The Source: John Keats

When I have fears that I may cease to be
Before my pen has glean’d my teeming brain,
Before high pil`d books, in charact’ry,
Hold like rich garners the full-ripen’d grain;
When I behold, upon the night’s starr’d face,
Huge cloudy symbols of a high romance,
And feel that I may never live to trace
Their shadows, with the magic hand of chance;
And when I feel, fair creature of an hour!
That I shall never look upon thee more,
Never have relish in the faery power
Of unreflecting love;—then on the shore
Of the wide world I stand alone, and think,
Till Love and Fame to nothingness do sink.

A presentation is about the context of a particular moment, the people, pacing, and tone, and being available afterwards for [...]]]>

What follows are the slides and transcript from my WikiSym talk for the WikiViz 2011 award. Parts of the talk are showing up around the web, and I wanted to have the full context somewhere.

A presentation is about the context of a particular moment, the people, pacing, and tone, and being available afterwards for comments, questions, criticism. So please understand that reading won’t be the same as being.

The talk seemed to mean something to some people; I want to give it the chance to mean something to more people. The good stuff starts in the middle.

I’ll be talking a little about my WikiViz project and also about my personal obsession with missing values and visualizing emptiness.

WikiViz 2011 was about visualizing the impact of Wikipedia. The goal of the competition was to improve our understanding of how Wikipedia is affecting the world beyond the scope of its own community.

The first question when creating a visualization is always: what data to use? I spent a lot of time looking for outward-facing data about Wikipedia. Then on Wikipedia itself, I found data about the global usage of Wikipedia, by country and language. When I finally found data about Wikipedia traffic by country, I knew I had the connections I needed between the world and the world of Wikipedia.

Huge thanks to Erik Zachte, the data curator on whose work this whole visualization rests.

I cleaned data with R and visualized it with Processing, both open source tools. The top represents countries, colored by region and more broadly by global north (blue) and south (red). The bottom represents languages. Connections represent over 100,000 page requests in the year from April 2010 to March 2011. It’s interactive, countries and regions can be highlighted, and sorted by population, pageviews, pageviews per person, and internet access. All data is transparently available on rollover.

In this image we’re sorting by access and can immediately see the difference between global north and south in terms of access.

Europe has generally high access and lots of connections.

Africa has generally low access and few connections.

Oceania also has generally low access, and has no connections.

(None of which is particularly surprising.)

The call for participation asked to see surprises: specifically, where Wikipedia has less reach than expected. This depends on the population and internet access of a country, both of which are considered here. These are the 25 countries* with fewer than 2 pageviews per person per month and greater than 25% access. For comparison: the highest pageviews per person per month is Iceland, with 15.7.

I think that visualization is amazing for its ability to force us to see what’s missing; to see the missing values in a collection of data. Anyone who has experience with data analysis, especially with analyzing other people’s data, knows the feeling of being totally preoccupied with missing values: how are they represented in the dataset? How should we deal with them – bootstrap to fill them in, or throw out the associated data completely?

I find that visualization trains my mind to notice what’s missing.

When I sort by region, I can force you to see the emptiness -

the missed connections in the global south.

The more I do visualization work, the more I notice who’s missing, not just globally, but personally. It’s going to get a little personal here….

First, I think everyone here should know about Data No Borders (update: Data No Borders is now DataKind). DataKind is the creation of Jake Porway and Drew Conway; it hosts “data dives” that connect data scientists with nonprofits and NGOs to collaborate and solve data problems. I love DataKind, and was up at 6am streaming their presentation at the Strata Conference a couple of weeks ago. Later that day, these tweets came through, and I was excited to see this spontaneous meetup, so I clicked through to the picture, and saw……..

Photo Credit: Alistair Croll; http://campl.us/fdUp

…….the table. And I think this spontaneous meetup is so awesome, but right away I notice who’s missing. I always wonder if other people notice.

I want to harness the power of whitespace in real life – I imagine diving into that picture and putting empty chairs around the table for all the people who are missing from the conversation. I want to create physical empty spaces to visualize the missing values. To force us to see who is missing.

So I won this WikiViz contest, and I’m so excited; I’ve been checking out the WikiSym page, and see that there are talks about the lack of female Wikipedia editors, and I come across these tweets….

…the joke of course is that there are no female authors on these papers. But all I can think is how happy I am that someone else is noticing who is missing.

I’m going to be honest with you: I don’t think Wikipedia needs more female editors to improve its coverage of Sex and the City. I think Wikipedia needs a diversity of editors because of Feodor Vassilyev:

I met Feodor Vassilyev almost 3 years ago, when the octomom insanity prompted me to look into who had given birth to the most children, and Wikipedia had my answer. Feodor’s first wife gave birth to 69 children: 16 pairs of twins, 7 sets of triplets, and 4 sets of quadruplets. So I think we can all agree that it is Mrs. Feodor Vassilyev, and her extroardinary capacity for birth, who is the true subject of this page, and Feodor’s accomplishment was really just being lucky (or unlucky) enough to be married to such a prolific woman.

But Mrs. Feodor Vassilyev has no page on Wikipedia. She gave birth to 69 children, but Feodor gets the title.

In this new WikiTrip tool, built by Paulo Massa, you can see that no female registered users have ever edited this page.

I love this example of bias in Wikipedia, and I’ve saved it for three years, because it is so obvious and so subtle. There’s no way to identify this with text analysis. A particular human mind is required to notice the bias, and give the title to Mrs. Feodor.

Now I don’t think this example is “capital-I” Important, but I think there must be examples like this all over Wikipedia, and it makes me happy to think that every Feodor has its editor – they just need to be connected.

The full quote from the title of my vis:

I want to say thank you to “11 guys” for noticing who was missing, and for taking the first steps to investigate not just who was missing, but why it might matter.

Thank you to anyone who walks away from here and continues to notice who is missing from events and conversations, and seeks out and invites a diversity of voices to the party.

This is both a global and a personal endeavor. It doesn’t require a grand gesture – just noticing when someone, who wanted to be in a conversation, walks away without saying anything, and taking the time to stop and say: I noticed you were there. What was it you wanted to say?

The generosity of these kinds of micro-actions invites individuals to a conversation and helps to develop a field. A micro-moment of connection can change a person’s life.

Very much related to that: Thank you to the judges for taking the time to really see this work. I know it demands a lot of the viewer.

Thank you to WikiSym for inviting me.

From the personal back to the global, just like there are women like me who want to be sitting at the Data No Borders table, there are people in those empty spaces of my visualization who want to be Wikipedia editors, who want to contribute, but don’t know it exists, or don’t see a way in.

In the panel just before this, we discussed openness vs. accessibility. Heather Ford said, “Openness is easy – you just put a license on something and say it’s open.” Accessibility is hard – someone has to take responsibility, and commit sustained  effort.

So – the goal is: we meet back in 10 years and see the circle FILLED. No more missing values, no more missed connections, no more empty spaces.

And because of the amount of Wikipedia data being collected, we will be able to see, rather than speculate on, exactly how a diversity of voices has changed patterns of edits, the content, and the connections of Wikipedia.

We will all have a Wikipedia for everyone, that reflects the collaborative contributions of everyone.

Thank you for listening (reading!). I’d love to continue the conversation with anyone who’s interested.

*Low Views | High Access countries, from greatest to least internet access: South Korea, UAE, St. Lucia, Jamaica, Bahrain, Iran, Tuvalu, Seychelles, Turkey, Brazil, Belarus, Morocco, Dominican Republic, Saudi Arabia, US Virgin Islands, Palau, Tunisia, Guyana, China, Venezuela, Peru, Phillipines, Vietnam, Cook Islands, Thailand

For context, if you need it: Andrew Gelman’s critique of Robert Kosara’s Infovis example

Above: Kosara’s example from Statistical Computing & Graphics (p. 5)

So I went to the epic Eyeo Festival, learned some Processing from Jer [...]]]> I spend massive amounts of time trying to make beautiful, meaningful vector fields that represent wind flow. The problem is this: no matter how lovely they are, static images do a poor job of representing dynamic flow through time.

So I went to the epic Eyeo Festival, learned some Processing from Jer Thorp, Wes Grubbs, and Moritz Stefaner, and decided to animate the vector fields (now with neon!):

The vectors are derived from remotely sensed raw lidar data. The particles create pathlines as they follow the changing vector fields. The input to this sketch is vector locations, direction, magnitude, and time. (Am I the first person to write a Julian day converter for Processing?)  The core of the code is Daniel Shiffman’s flow fields.

This novel visualization gives us the opportunity to investigate and share the flow through time as we’ve never been able to before – Processing helped me make these static vectors compelling.

Thanks to everyone I met at Eyeo for inspiring me to have fun making something beautiful from the data I work with everyday.

Data shown was created through NSF Award 0924407

Pictures that have to be gone over with a reading glass to see the main point are wasteful of time and inadequate of effect.

The greatest value of a picture is when it [...]]]>

Pictures that emphasize what we already know – “security blankets” to reassure us – are frequently not worth the space they take.

Pictures that have to be gone over with a reading glass to see the main point are wasteful of time and inadequate of effect.

The greatest value of a picture is when it forces us to notice what we never expected to see.

John W. Tukey, Exploratory Data Analysis, p. iv