Making the most detailed map of auto emissions in America

Using estimates from the Database of Road Transportation Emissions, Nadja Popovich and Denise Lu for The New York Times mapped auto emissions at high granularity. Popovich described their process on Storybench:

I want to make graphics that really resonate with people. If that is your goal as a visual journalist, something to think through is just how you can tie data back to a more human experience. To kind of go past the dataset as a dataset and reveal the humanity of it. I think one way that you can do that is by zooming into it in this way. You suddenly don’t just see, “Oh, this line of emissions has gone up.” We set out for a more personal view that says, “You know, you can actually see the roads that you might be driving on every day. That’s where the emissions are coming from.” It ties it back to a much more human experience and makes the data less abstract. Thinking a lot more through how to tie (the data) back to human-lived experiences is something that is really important and really we found resonates with readership.

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Weaponised design

When the web was relatively new, things were more of a free-for-all. Everything was an experiment, and it always felt like there were fewer consequences online, because not that many people really used the internet. Now a large portion of people’s lives are online. There is more at stake.

Tactical Tech focuses in on the (careless) design of systems that allows bad actors to thrive:

Design can also be weaponised through team apathy or inertia, where user feedback is ignored or invalidated by an arrogant, culturally homogenous or inexperienced team designing a platform. This is a notable criticism of Twitter’s product team, whose perceived lack of design-led response is seen as a core factor for enabling targeted, serious harassment of women by #Gamergate, from at least 2014 to present day.

Finally, design can be directly weaponised by the design team itself. Examples of this include Facebook’s designers conducting secret and non-consensual experiments on voter behaviour in 2012–2016, and emotional states of users in 2012, and Target, who in 2014 through surveillance ad tech and careful communications design, informed a father of his daughter’s unannounced pregnancy. In these examples, designers collaborate with other teams within an organisation, facilitating problematic outcomes whose impact scale exponentially in correlation with the quality of the design input.

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People relationships in data analysis

Roger Peng discusses the importance of managing the relationships between people — analyst, patron, subject matter expert, and audience — for a successful analysis:

Human relationships are unstable, unpredictable, and inconsistent. Algorithms and statistical tools are predictable and in some cases, optimal. But for whatever reason, we have not yet been able to completely characterize all of the elements that make a successful data analysis in a “machine readable” format. We haven’t developed the “institutions” of data analysis that can operate without needing the involvement of specific individuals. Therefore, because we have not yet figured out a perfect model for human behavior, data analysis will have to be done by humans for just a bit longer.

Whenever someone touts a tool for “automatic insights”, whether it be in analysis or chart generation, something like this comes to mind.

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Bruises

Musician Kaki King’s daughter suffers from a condition (Idiopathic Thrombocytopenic Purpura) where her body attacks her own platelets, which leads to spontaneous bruising and burst blood vessels. In coping with the stress as a parent who can only do so much for her suffering child, King collaborated with information designer Giorgia Lupi.

The result: a mix of personal data collection, reflection, music, and data art entitled Bruises — The Data We Don’t See.

Watch the full piece below:

Love Lupi’s continuous path towards less sterile data.

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Mass exodus at human scale

Big numbers are too abstract in our minds to fully understand the scale of things. So, to show the full gravity of the hundreds of thousands of Muslims fleeing Myanmar’s Rakhine state, Reuters starts with the individuals and builds your intuition towards the true scale.

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Complement data with emotion for full effect

Data is a great vehicle for arguments, but the (not just visual) perception can change completely depending how a reader feels. Cognitive neuroscientist Tali Sharot talks facts and emotions on Hidden Brain.

The example at the end is interesting. Tell a person a joke when they’re sad, and they probably won’t think the joke is funny. Make the person happy first, and it’s more likely they’ll see the joke from your point of view. How does this transfer to the communication of data? [via Kim Rees]

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Sharing the same traits and qualities

My son used to watch Daniel Tiger’s Neighborhood (a modern take on Mister Rogers’ Neighborhood) a lot, and one song’s chorus goes like, “In some ways we are different, but in so many ways we are the same.” This commercial from TV2 in Denmark is the grown-up, categorical version of that message.

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Skittle disconnect

This is what happens when there is a disconnect between data and what it represents. So much wrong.

I need to avoid social media for the next month. There is something upsetting every single time these days.

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Would you try to vaginally "seed" your baby’s microbiome after a C-section

Well, sadly I just accidentally deleted a whole post about this and cannot find it. So this is going to be way shorter than I would have liked.

I found this opinion piece to be very interesting and a really good case study for discussing the microbiome: Opinion: A Mother’s Microbes | The Scientist Magazine

About the piece
  • Authors: Jack Gilbert and Rob Knight
  • Topic: VMS. Vaginal Microbiome Seeding. Attempting to "seed" the microbiome of an infant born by C-section by collecting microbes from the mother's vagina and applying them to the baby. 
  • Subtopic: A response to a BMJ editorial that seems to have said they do not at this time recommend VMS (I say seems to have said because the people at BMJ decided this was something to put behind a paywall). 
Why do I find it interesting?  Because I think the authors do a good job of showing how hard it is to make some decisions when the evidence is inconclusive.

Quibbles? Sure.  I like much of the approach of Gilbert and Knight but still find parts unconvincing and incomplete.  Some specifics are below:

The paragraph on the conditions associated with C-sections does not in my mind, go into enough detail on how such associations can be spurious and that those conditions could have no causal connection to C-sections.  Instead they simply say we do not know if these associations are due to lack of exposure to the vaginal microbiome of the mother.  That just is not sufficient in my mind.  For example, they mention the association between autism and C-sections.  However, they do not mention that some recent studies suggest that there may not be a causal association (e.g., see this). I think it is imperative when discussing such associations that one also mentions how such associations may have no causal relationship. 

It is unclear what the part near the end is that appears to be a quote from Rob Knight.  Not sure if this is a online formatting issue or what.  But in that section Knight writes "I seeded my newborn child with the vaginal microbes the baby would have received naturally had everything gone according to plan."  I have some quibbles with this as it seems possible that just doing an attempted seeding would not necessarily get the microbes that would have been received naturally

Finally, I have some more major quibbles with the last section.  On the one hand it it is good that they do discuss a risk that could come from VMS (exposure of the baby to pathogens from the mother).  However I think it would have been good in this discussion to also discuss other possible risks.  For example, it seems plausible that exposure to non pathogens from the mother could in fact do some harm (e.g., if one exposure the baby to the wrong non pathogenic organisms perhaps things could go wrong in terms of colonization).  In addition, it seems also plausible that by doing the sending, some parents and medical caregivers and others might be less vigilant at watching the infant for health issues assuming that the seeding made them healthy.  And I am sure there are others.  

It would in fact be good to lay out the possible risks somewhere in more detail.  Why?  Because I think Gilbert and Knight are actually spot on with their final recommendation:
But based on the evidence to date that your child’s microbiome at birth is important and modifiable, we think that parents should make up their own minds how much evidence is enough given the evolutionarily sound logic and clear health advantages of vaginal birth over C-section. 
And I am not just per se talking in the hypothetical.  If I could go back in time to when my daughter was born by a last minute C-section, I think I would have wanted to have done VMS.  And if I could go back in time to when our second child was born by a planned C-section, I definitely would have wanted to have been more aware of this issue and I think we would have seriously considered it.  I probably would have looked in more detail at the possible risks if I had the time but regardless, from what I know now, I think VMS is definitely worth considering in cases of C-sections if the mother's vaginal microbiome does not contain any known dangerous pathogens and if other risks can be considered and balanced.  




Bonus: BMJ did make available an audio commentary about their Editorial. See below:

Made a Storify of some of the discussion around the topic


Some other questions of interest and or papers to look at

Twitter bot generates biographies via Census data

We usually see Census data in aggregate. It comes in choropleth maps or as statistics about various subpopulations and geographies. Is there value in seeing the numbers as individuals? What about the people behind the numbers? FiveThirtyEight intern Jia Zhang experiments on Twitter.

[I] built a Twitter bot that mines for details in the data. Called censusAmericans, it tweets short biographies of Americans based on data they provided to the U.S. Census Bureau between 2009 and 2013. Using a small Python program, the bot reconstitutes numbers and codes from the data into mini-narratives. Once an hour, it turns a row of data into a real person.

Here are a couple of examples:

Fairly straightforward but an interesting exercise. I have a hunch someone is going to expand on this idea soon enough.

In case you're interested, I'm guessing Zhang used the Public Use Microdata Sample (PUMS) from the Census Bureau, which is a granular dataset based on responses to the American Community Survey. Or maybe I'm thinking about it too hard. It would also be possible to simply create "estimated" individuals with the aggregate data. Either way, this is fun. I want to see more things like this, please.

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