Week 1
Kick off and refining (01/02/21 - 08/02/21)
Last updated
Kick off and refining (01/02/21 - 08/02/21)
Last updated
Cairo starts off with stating how influential data visualizations can be. A great example of this is the 'Climat issue' published by The Economist. The article below dives deeper into this example.
He compares making data visualizations to writing: There are some rules you have to keep in mind but you can deviate from standards to give body to the story you want to tell with your data visualization.
It is important to devise why your visualization should exist. Alberto states that it is very important to realize why it is you are making what you are making.
Do the potential benefits of designing my visualization outweigh the possible harm it may cause?
Questions to ask yourself
Why should this data be made public
Why should it be made public through a map?
Why should it be this type of map?
Even if we decided that this data is worth publishing wouldn't a different map be better?
The power of visualization lies in when it helps us see things that we can’t normally see. Don't make a visualization when the purpose of the object you're designing is to let the reader see each specific value. In this case a table is great.
The question you have to ask yourself is do I care more about the specifics or do I care more about the bigger picture.
The next step is to determine what to visualize. Do I understand my data including its limitations, uncertainty, and or glitches? What or who is being measured and why?
The numbers of this example can be shocking of you don't understand the definition of homeless they used creating this project. Being in this context also means not being sure you can live in the house you live in for more than a year or so.
Tip| When you start a project always partner up with someone who knows much more about the data then you do.
Interesting article about the importance of adding context and definitions to your data visualizations (about COVID-19)
Who are visualizing for? Think about how the audience will access your graphic. can they read it? Wil they understand it? It is not the same thing to design a visualization that is going to be published in a scientific paper as designing that same visualization to be published in a newspaper or a magazine. Those are two completely different types of audience.
“I and my colleagues here at the FT, we really do think one of the most valuable things we can do as data visualization practitioners is add this expert annotation layer.” - John Burn-Murdoch (Financial Time)
How much should you visualize? Find the balance between showing too much and too little.
What is the best visual form for your data? There are a lot of sources that can help you find the best option for your data. A couple of handy resources are:
My favorite
These links provide a lot of information about what kind of graphs you should use keeping in mind what you want to show. If we use writing as a metaphor you can explain the table above as the 'grammar' of data visualization.
The last subject Cairo discusses is the style you use. The style should be in line with the subject you're discussing. For example the visualization about pets he continuously uses throughout the presentation is a very light subject. The style emphasizes this by using happy pastel colors and a cute illustration style.
Cairo ends his talk by giving a lot of other useful resources. A couple of them are listed below
00:00 Caroline Criado-Perez introduces herself as a writer and activist while being a guest on this podcast. The design flaw of designing things based on the need for men is everywhere. She first illustrates this by telling a story of clearing snow in a city in Sweden. At first you'd think this won't have anything to do with gender. But when you zoom in, you see it does.
"They operated under the assumption that what would have worked for them would have worked for society"
"We're so used to picking men as the default"
07:37 How does this affect medicine? The majority of medical research and therefore knowledge is based on the male body. An example heart attack symptoms.
Women have higher rate of diagnoses because it their symptoms are not 'typical'.
Another example are car crashes. Car crash dummies are based on a male body while women's bodies are very different. They also don't pay attention to:
Women's breast
Pregnancy
Length
17:14 Another example where we take men as the default is that women in the workplace are often labelled as insecure. However research has shown that women are pretty good at assessing their qualities. Man often have too much confidence but because we take them as the default we call women insecure.
Interesting terms
Data feminism
Data bias
World designed for men
in this youtube video on of the writers of the book 'Data Feminism' gives a presentation about the book. The information is not necessarily interesting for my subject but it provides a good back story. I will explain this using some quotes i copied from her talk.
Data is the new oil
With this she means that data can be used as the new tool to change the world
Data is the same old oppression
Data and especially the absence of it plays a big role in the ongoing discrimination against women
In today's world data is power
With facts and visualisations you can really make your point and win people over in modern society
Later in her presentation she mentions the 7 principles of 'Data Feminism'
Examine power
Challenge power
Rethink binaries and hierarchies
Elevate emotion and embodiment
Embrace pluralism
Consider context
Make labor visible
She illustrates these points by giving example. I think i could really substantiate my research if i include these principles.
Data feminism requires an expanded definition of data science
Interesting terms
Invisible data
Oppression
Data science
Interesting sources
Design Justice Network https://guns.periscopic.com/ https://dataplusfeminism.mit.edu/ https://dhlab.lmc.gatech.edu/ https://databasic.io/en/
Short summary In this article the Guardian writes about the book ‘Invisible Women’. They discuss the cornucopia of statistics Criado Perez assembled – from how blind auditions have increased the proportion of female players hired by orchestras to nearly 50%, to the good reasons why women take up to 2.3 times as long as men to use the toilet. They continue to list impactful examples described in the book. The continue to say that the sheer abundance of examples in this book militates somewhat against its argument, which is that there is a lack of gender-specific data: a “gender data gap”. The neat thing about data is that it avoids thorny questions of intention. People interpret data as facts. Data not only describes the world, it is increasingly being used to shape it. Interesting terms
Gender data gap
Data as a tool to shape the world
Interesting terms
Gender equality
Disruptive bias
Epistemological perspective
Inclusion and exclusion
To gain a better perspective on what it is i am researching i made an online whiteboard. Here i collected all the 'interesting terms' i noted above.
I decided that in order to refine my research i could choose one of the examples (green). My preference goes out to the car crashes because i think this subject will allow me to combine hard data with a humanistic view.
Belted female auto occupants have 73% greater odds of being seriously injured in frontal car crashes compared to belted males (after controlling for collision severity, occupant age, stature, body mass index and vehicle model year).
Important factors
collision severity
occupant age
stature
body mass index
vehicle model year
Female drivers and right front passengers are approximately 17% more likely to be killed in a car crash than a male occupant of the same age.
Any seatbelt-wearing female vehicle occupant has 73% greater odds of being seriously injured in a frontal car crash than the odds of a seatbelt-wearing male occupant being injured in the same kind and severity of crash.
Data from the National Highway Traffic Safety Administration and the Federal Highway Administration (FHWA) shows that males drive more miles than females, and are more likely to engage in risky behavior, such as speeding, driving under the influence of alcohol, and not wearing a seat belt.
the vast majority of automotive safety policy and research is still designed to address the body of the so-called 50th percentile male—currently represented in crash tests by a 171-pound, 5-foot-9-inch dummy that was first standardized in the 1970s (today, the average American man is about 26 pounds heavier).
No dummy takes into account the biological differences between male and female bodies.
(The female dummy sits in the driver’s seat for some side-impact tests.) This, despite the fact that women now represent almost 50% of drivers in the U.S., according to the FHWA.
The first stage is the vehicle crash—the impact of a car or truck into a foreign object. Stage two is the human crash, when the bodies of the vehicle’s occupants come into contact with seat belts and airbags—or worse, the dashboard, windows, or some other object. The third stage is the internal crash, which refers to the collisions of organs, bones, and soft tissue that happen within the human body.
Seatbelts
Air bags
But differences aren’t just about shape, size, and position. For example, the female pelvis has a geometry that’s different from the male pelvis, and the male neck is stronger when it comes to forces that bend it.
The agency’s use of 5th percentile female and 50th percentile male dummies represents “a broad spectrum of occupant crash protection rather than merely focusing on median body types,” its statement said. “Currently, NHTSA is focusing its research in new advancements in both sizes of crash test dummies, including the use of advanced instrumentation and criteria designed to better mitigate respective injury risks."
After doing a little bit of research i decided to focus on a specific example of the problem i want to adres: unsafe cars for women. In week 2 i will do even more research and come up with a more specific project plan.
Women
Men
Breathlessness
Chest pain
nausea
Pain in the left arm
Fatigue
