This week, I looked at the provided “4D” animation formats data may be presented in and analyzed its strengths and weaknesses. 4D data visualization can be distinguished from other forms of data visualization by incorporating time into the display of information. Each animation video I watched depicted several data sets that experienced visual change over an elapsed time.
The first data visualization project I watched was Isao Hashimoto’s “2053.” The artist wanted to represent the daunting 2,053 nuclear explosions that occurred between 1945 and 1998. To pass time quickly, Hashimoto shortened the duration of a month into one second and indicated the turn of the month with a high-pitched beep. Additionally, each year is marked by a louder beep. The nuclear explosions are represented in space on the map, with the color indicating the country responsible and a beep. The fifteen-minute video presents subtextual information, such as global conflict trends and specific countries’ military presences.
The following data visualization project I analyzed was the February 2006 Ted Talk by Hans Rosling featuring the software Gapminder. His talk focused on the misrepresentation and misinterpretation of data about world development and developing nations. He provided a nuanced discussion of data analysis utilizing his data visualization tools which still prove helpful in contextualizing data currently.
Though Rosling’s Ted Talk occurred nearly twenty years ago, his impact on data visualization has influenced how data humanists can analyze statistical information and present it in meaningful manners. Gapminder still provides tools by which users can present their data on the website, exactly as Rosling did in his talk.
During our class discussion on data visualization, we considered the strengths and weaknesses of various graph formats with different independent and dependent variables frequently found in graph making. In considering weaknesses, I found recalling previous information difficult to follow while watching the 4D graphs. As time passed, in most of these graphs presented, the information given from a previous time frame was immediately lost once the unit of time had passed. For example, Hashimoto’s project was meaningful when lots of information was being transmitted at once. Having a count of total country nuclear explosions was helpful, but the fairly quick blips representing one occurrence made it tedious to remember where and when explosions transpired. Additionally, the Rosling graphs frequently utilized multiple measurements of data at the same time to represent how various variables changed the animations. Specifically, when the diameter of circles was used to represent the population in some countries, it became difficult actually to differentiate places on a country basis.
Ultimately, I believe 4D data visualization provides an extremely information-packed medium by which statisticians may present their data. The possibility to draw conclusions across multiple variables seems to benefit all areas of study. Some practical limitations will still present as aesthetics and the project’s message. Still, overall the capacity to represent change over time is intrinsically valuable to data visualization which is fundamentally more achievable when time becomes its own variable on a graph.
5 thoughts on “Data Viz Reflection”
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From your explanations, I imagine 4D data visualization is a good medium for handling massive data sets. I feel like it helps eliminate one of the problems of data visualizations discussed during Lin’s class. It avoids cluttering of data, presenting it in an order that makes people view data in a way that doesn’t make them confused. Thus, the rate of change aspect of events in the graph addresses a huge concern in data visualization despite the problem it also poses.
I’m curious to know what other people think about 4D data visualization.
It’s interesting to see the extent to which history can be preserved through digital humanities projects. From your examples on misinformation and global conflict, it seems that 4D data visualization helps with the component of establishing a narrative when representing data. With such detailed work, it’s important to recognize what parts add or take away from the overall message that the data is meant to show viewers.
In addition to a being a good medium for large data sets like Bennet mentioned, I also agree with you that it is a great medium to show change over time. It allows for lots of data to be presented without being super cluttered across a time scale. The aesthetic limitations that you pose are definitely true for the examples explored here but I think the technological advances made in animation in recent years could be applied to solve some of these limitations. It could be really cool to see how some of this advanced video design could be applied to examples of 4D data visualization.
I am very interested to see how sound affects our understanding of data. In this case, I guess the word is not just data visualization but data sensation. As technology improves, we might have more interactive data perception ways in addition to the classical visionary perception. With that in mind, I’m also wondering whether the diversity of data sensation methods is helpful since sometimes the technology can get overwhelming.