Project 3, Communication Design Studio, Fall 2016
We kicked off our last project which is about gathering relevant data and visualizing in a useful way. We’ll be spending a lot of time searching for data we need, sorting out relevant ones, finding out patterns among them, and eventually visualizing. My teammates and I have chosen creation as our topic based on Max-Neef’s Hierarchy of Needs below.
11.3 Examples from class
In today’s class, Stacie showed us a bunch of examples of data visualization.
- How layers of newspapers from different regions look like?
How they are formed, and what kind of topics they are mainly dealing with?
NYTimes: Inaugural Words: 1789 to the Present
- Visualized archive of U.S. presidents’ most used words in their speeches.
- The Trustworthiness of Beards: designed by Matt Mcinerney
Note: based on absolutely no scientific evidence.
Time lapse movie of the rhythms of weather, the lengthening and shortening of days, and other atmospheric events on an immediate aesthetic level: the clouds, fog, wind, and rain form a rich visual texture, and sunrises and sunsets cascade across the screen. (In San Francisco Bay)
Produced by Gabriel Reilich
11.10 Exploring & Framing the Topic
In today’s class, we shared data we’ve collected respectively and questions we had in order to find out interesting areas and make a clear direction. All of us were interested in startups, which generally represent creativity, youthfulness, originality and so on. Also, to me, startup seems closely related to our topic, ‘creation’, because startup is at the center of emerging industries, it pursues innovative and creative thinking, in its research method, design thinking or related disciplines are placed at the center of its operation, to name a few. Also, I thought it might be interesting to visualize ramifications that startups brought to our world such as changes in natural environment like air/land quality, in people’s physical/mental conditions ( like their perceptions or abilities) and so on.
More specific questions regarding startup include ‘what factors affect start-up and what factors are affected by it?’, ‘what does the relationship between those factors look like?’. These questions led us to think about several factors that might have something to do with startups. Depending on the location and the characteristics of startup, we assumed that, new groups of people with similar social/cultural/even economic backgrounds could gather and create community. Then, new formations of gatherings and communities could be leading to housing price fluctuation, newly emerging transportations or traffic growth which could lead to air/land quality, etc.
We also talked about failure and the tolerance toward it, because startup always entails risks. Thus, we might find some interesting patterns which have something to do with failure and the tolerance of failure.
11.15 Discussion & Experiments
We defined our topic and set the question. So the questions we are dealing with at this point include:
- What is the relationship between paid leave (vacation) and creativity?
- Do higher levels of creativity lead to higher happiness?
We looked for data sets such as:
- Creativity index
- Happiness index
- Paid time off
- Maternity leave
- Public holiday
- Work hours
During the class, we talked about Shedroff’s experience design as a reference for our project. We were encouraged to think carefully about ‘Attraction, Engagement, Conclusion’ when creating interactive visualization. We need not only to discover useful patterns from raw and often overabundant data, but to create engaging story-telling piece of work that might easily attract and involve viewers. So, we have to consider, with each piece of data we have, how should we order it? How should we present these overwhelming amount of data? We were encouraged to jot down ‘what our story might be’.
We also talked about, since people’s cognitive models might be different and we, as designers, have our own hypothesis, user testing is sometimes necessary.
Stacie also encouraged us to think about ‘how we might visualize actors’, ‘visual variables by using different color, size, shape, sound, movement, location/proximity, line, weight, value/saturation, etc’.
There should be close cognitive connection in visuals. For example, when we are trying to show demographic, we should consider effective ways of showing gender.
- this or that (categories)
- scale( <> )
11.17 Discussion & Experiments
In today’s class, we created some simple visual cues that might work for the topic by roughly drawing shapes, using different colors, utilizing graphs, and so on. Also, we explored ‘Scales’ from Nathan Yau’s Data Points which include, Linear, Logarithmic, Categorical, Ordinal, Percent, and Time, then applied these scales into each divided subtopics. This exploration gave us some hints of what kinds of shape we might use or how might we shade objects.
11.22 Exploring visual cues
During the class, I kept exploring the topic of creativity with the post-its of different scales of each subtopic. And I threw questions to myself which include:
- How might I map different countries — based on geographical feature? Or I might create a new abstract map based on creativity index rankings or population or size of countries
- How might I convince viewers with creativity index? — obviously, the researchers who announced the creativity index have concrete standards with which they measured creativity index (which are each technology, tolerance, and talent), but should I deal with those subdata of three-Ts in order to convince viewers?
- Do I need to consider priority of each factor according to importance in terms of contribution to creativity and map/shade them differently?
Besides, we also briefly discussed on the shape of creativity with different forms. Manya thinks pointed blades represent creativity while flat/even form like circle represents something boring. But I personally disagree with that. In my view, keen blades seems like they would not embrace variety thus not really being creative. Rather, it seems to represent isolation, rigid, unadaptable, inflexible, etc. I’d rather use some cloudy blades which more seem to embrace diversity.
11.28 Collecting More Datasets — higher education enrollment / patents / investment in R&D
My teammates and I decided to collect more concrete datasets for our visualization instead of the creativity index.
I’ve got some inspiration from an article of CNN, which shows World’s most cultured cities. And according to it, each city was studied based on six themes: 1)cultural heritage, 2)literary culture, 3)film and games, 4)performing arts, 5)people and talent, and 6)cultural diversity.
So I decided to compare existing creativity index (or rank) and some datasets that might have something to do with creativity such as:
- Highest number of national museums (Cultural heritage)
- Highest number of UNESCO World Heritage Sites (Cultural heritage)
- Highest percentage of public green spaces (Cultural heritage)
- Highest number of library book loans (Literary culture)
- Highest number of film festivals (Film and games)
- Highest number of video games arcades (Film and games)
- Highest number of theaters (Performing arts)
- Highest number of international tourists (Cultural diversity)
- Highest number of specialist public/private cultural higher education establishments (People and talent)
- Highest number of students at specialist public art and design institutions (People and talent)
12.06 + 12.08
In our class, Manya, Jesse (my teammates) and I got some feedback from Stacie. We talked a lot about visual styles that might be strong and relevant to our topic. Since our subquestions and representatives are different, we’ve got to show each other different visual forms:
Manya is going to deal with technology aspect among creativity index, especially money investment on technology. Stacie’s advice on her visualization mostly focused on abstract form of ‘money’. From the specific form to very abstract form of money would be, from stacks of money to square or circle (bill or coin).
Jesse is going to dig deeply on educational aspect and he brought a lot of square forms, each of which represents one country.
And I am going to use several dimensions especially focus on cultural aspects. I’ve decided to compare original rank from GCI (Global Creativity Index) 2015 and three aspects including ‘Policy’, ‘Culture’, and ‘Gender Equality’. Each of three dimensions contain two datasets, so I’m going to use 6 datasets including:
- Policy — 1) Paid time off, 2) Maternity leave
- Culture — 3) International tourism, number of arrivals, 4)Heritage sites (nature+culture)
- Gender equality — 5) Labor participation rate, female, 6) Female share of seats in national parliaments
Originally, I thought it might be very interesting if I show relationship between creativity and ‘equality’ (both on gender and homosexuality) but I was unable to find enough datasets for homosexuality. And I excluded business aspect for the same reason. But I feel confident about what I’m going to do.
In terms of visual styles, I was inspired by an animation which is called ‘UP (2009)’, where a bunch of balloons appear.
I explored various forms of balloons. Which might be effective to show the rank of countries. My main goal is to compare the rank of countries with three aspects (policy, culture, and gender equality). I will allow users to switch on and off each dimension so that they can not only see new creativity rank according their selection but also compare original creativity rank and new rank.
I’ve got diverse feedbacks from MDes/MPS/MA classmates (from Jesse, Bori, Adrian etc) and all of them agreed that the visual style and animation of floating balloons is interesting and relevant enough to creativity. At first, I was not sure whether to represent the creativity rank of countries with the number of balloons or with the size of one balloon, but I thought that the size itself doesn’t reveal exact difference, because some of datasets I’m using show subtle difference gap between countries. But some show dramatic difference. This is where I got stuck actually.
If you see above, the numeric gap is very huge among countries on International Tourism data, yet it is subtle in the others.
So what I did is,
writing down all the numbers from my data sources, which is the smallest and which is the largest, then set the range of scales so that I can allocate countries evenly into categories.
12.14, Final version of Data Visualization
This screen, you can see when all switches are on like this:
<First scene of this piece>
At the first scene, you will see only original creativity index which you can find stroked dots in white area above countries. You can see different rank according to your interaction — switch on/off keys in the box area.
- Global creativity rank — Martin Prosperity Institute (http://martinprosperity.org/content/the-global-creativity-index-2015/)
- Maternity leave — UNdata (http://data.un.org/DocumentData.aspx?id=344)
- Paid time off—Wikipedia (https://en.wikipedia.org/wiki/List_of_minimum_annual_leave_by_country)
- International tourism — he World Bank (http://data.worldbank.org/indicator/ST.INT.ARVL)
- Heritage sites — UNESCO (http://whc.unesco.org/en/list/stat/)
- Labor participation rate — The World Bank (http://data.worldbank.org/indicator/SL.TLF.CACT.FE.ZS)
- Female share of seats in national parliament — The World Bank (http://data.worldbank.org/indicator/SG.GEN.PARL.ZS)