The podcast makes me realize the data visualizer’s responsibility for gathering data on their own. I had a misconception about the role of data visualizers before taking the class. I thought the responsibility of the data visualizer is to reinterpret existing data in the form of visual design, in order to make the data more understandable for wide audiences. However, as it turns out, the role of data visualizers is proven to be more extensive and significant in terms of social responsibility because we are not only in charge of visualizing the data but also gathering it. In the podcast, Gebeloff mentions that many coronavirus cases were not recorded in a comprehensive way, lacking other relevant information about the patients. Namely, data collectors do not know what kinds of data will be useful to data visualizer. In other words, data visualizers actively pick and combine different data, to tell a meaningful message. Having realized that, I believe data visualizer bears more social responsibility, like a film director who interprets the scripts in the form of a visual medium. Both are storytellers, with the responsibility of bridging individual meaningless data to a wider range of audiences.
Climate change is a complex topic since it evokes people to think about the future.
cases we saw during class, most of them are either timeless or tied to specific moments in history.
For example, the self-driving car is a timeless case. Specifically speaking, it provides choices,
like switching to another lane to kill one person or go straight to kill three, for the users to
make based on their moral values. The moral values are more of the result of culture rather than
time. Another case is Japan before and after the Tsunami. The essence of the project is to compare
the satellite photos before and after the Tsunami, two distinct moments, without further implication
about the future. However, in the case of climate change, it is more complicated. Since it is still
a debatable topic, some believe in it, while others do not. Yet, whether one believes it does not
matter. What matters is that both believers and disbelievers will look into the future, because the
future will show who is right or wrong or both. In that case, designers tend to use trends rather
than specific moments to tell the story. However, a trend is vague and intangible to us. We do not
experience trends. We constantly experience moments which made up of a trend.
The essence of this project is that it personalizes the data with moments at first and then trend. Popovich wisely points out the difference between weather and climate. The former is a moment; the latter is a trend. I agree that comparing the number of hot days in certain locations at your birth year and 2018 is pretty effective and innovative. It personalizes the data using specific moments to tell a trend. However, I still do not think it is the best way. Admittedly, we can vividly recall the experience of a hot day, a moment that is tangible to us. However, we still cannot vividly experience the number of hot days in a year, an abstract concept.
What does 1 Trillion scale like? How much does Apple’s value grow since its foundation? How do the values of other companies compare to Apple?
My favorite one is “Apple’s Value Hits 1 Trillion” because it successfully gives the audience a real sense of how valuable tech companies like Apple are. In comparison to “Drowning in plastic,” the visualization of cubes being poured into the container is more effective and accurate since the audience has a real sense of the maximum value, indicated by the volume of the box. Although the three-dimensional reference to Effie tower or New York City can help, the problem of “drowning in plastic” is that its maximum value is elusive. As a result, the audience can only know it is a lot of plastic bottles.
How complex is the decision-making process of self-driving cars?
My second favorite one is the “Self-driving Cars” because I am fascinated by its narratives. Since the ethical and technological discussions are based on making decisions, the designer wisely incorporates the decision-making process on the website, letting the audience make the decision. Thus, the audience can personally experience the complexity of the discussion. There is no perfect answer, and everyone has a stand.
What are the types of gun deaths? How much does each contribute to the total number? How to bring gun deaths down?
My third favorite one is “Gun Deaths in America” because it follows the criteria I outlined in the first one: it visually indicates the maximum value. That is important, since having the full amount in mind, the audience will get a sense of each type of gun deaths’ proportion. For example, terrorism and mass shooting do not make up a lot of total gun deaths. Besides, it uses both colors and saturations to codify the categories, which is very clear.
I am a passionate filmmaker and movie-goer, so I decide to use RAWGraphs to visualize the same dataset about movies to answer and reflect specific questions.
The contour plot answers the relationship between the production budget and the box office. As we can learn from the graph, 1200 million box office seems to be a watershed: film with a lower budget such as below 100 million dollars and film with a colossal budget above 200 million dollars both can reach the watershed, regardless of the inflation.
The beeswarm plot answers the relationship between genre and production budget. Ideally, the dataset should include as many films as possible, to be more representative. However, we still can see some general trends that action and thriller genre tend to be more expensive to make in comparison to romcom and drama.
The scatter plot shows the relationship between IMDb rating (public rating) and the Metascore rating (critics rating), which I think is the most interesting one. if y = x is the divider. Films that are below the line are the ones with underrated critic ratings. For example, the Dark Knight features an IMDb rating 9.0 (No. 4), while its Metascore is a decent 84 points.