Two Interesting Visualizations Assignment – Visualization 1
Every morning my wife and I go to the local CrossFit gym. Depending on the coach, the music selection for each day varies greatly. This morning, I was asked by the coach if I was a Weezer fan. I said I wasn’t a huge fan, but they are alright. I told him I enjoy when another coach chooses music because she selects 1990-2000 pop punk, which were formative years for me. He offered to change the station. As a Smashing Pumpkins song was playing, I declined the change and told the coach how the current song was one of the first songs I learned on the guitar. After I talked with him, I was thinking about how my musical tastes have changed, and thought about how my experience is different from the others in my class. As I looked for visualizations, this visualization provided by Google interested me (Music Timeline, n.d.).
View the visualization here: http://research.google.com/bigpicture/music/
Message(s) the visualization is intending to convey.
I believe a primary purpose of this visualization is to connect customers and potential customers to music available in Google’s music service, however it serves purposes more interesting to this assignment as well. First, it provides an interactive library for music discovery based on the popularity of recordings organized by genre and recording dates as represented by representation in Google Music users’ libraries. Second, this should roughly estimate the most popular genres and albums throughout the visualization’s timeline, again for the purposes of musical discovery. Third, it is a robust library of music that allows for a search of any artist or album to see them within their historical context and popularity with the ability to explore artis and album information. For those interested in the trends of music since 1950, this is an extremely interesting, interactive experience that is educational and provides a walk down memory lane as I remember my favorite artists and compare it with others who, I imagine, enjoyed the same tracks as I did through the years.
A critique of this visualization
The visualization is beautiful and fun. In particular, the scope of the data is robust to find even some of the most obscure albums of my teen years. For example, I had a brief “goth metal” phase in the mid 90’s and really enjoyed this obscure band called “Klank.” I had a favorite album that I listened to nonstop for about a month, then lost interest. I never got back into them. Searching for that album, I can see that I wasn’t the only person favoring that album and missing out on any later releases. Of course, like any other music serve, there is a later album mistakenly associated with the initial artist, but for the most part, exactly what I was looking for is all here.
The visualization is visually interesting and shows blips where genres increased and decreased in popularity, spurring me to investigate further. Additionally, the integration with Google Music’s library makes it easy to transition from information to experience as I listen to the albums I discover.
There are some difficulties with the visualization. First, it is difficult to understand exactly what data is represented. At first glance, I would assume it is a chart of genre popularity over time. The Y axis, while first utilizing a label turned 90 degrees, increasing reading difficulty, is unclear. The label reads “popularity” but does little to understand what that means. I had to scroll to the bottom of the page to view an external page explaining the visualization before gaining a useful understanding of the data.
Second, genres expand and contract through the timeline based on the popularity in a fluid mix of color. As a result, the presentation is unnecessarily confusing and seems purposeless and difficult to read. In the same way, the scale does not allow a great deal of visualization for less-popular genres. There is simply no way to manipulate and investigate obscure data outside of a manual search. It is easiest to discover the most popular genres and artists, which could be argued, becomes a self-predictive popularity/discovery tool.
Third, I find the reference albums for genre timelines to be lacking in areas where critical data points are needed. As an example, if I click on the jazz genre, I can see a timeline of “popularity.” The largest region of popularity is in 1950. If that is the most popular time for records released in libraries, it is reasonable to expect to find some example albums, however, the closest reference album isn’t released until 1955, after the peak area of the graph. The true data is simply not discoverable to gain particularly meaningful knowledge.
Finally, because of the restrictions of the data to measure presence of genres and albums within Google Music users, it provides only a restricted audience that may not provide an accurate representation of the music listened to by the general public. This graph only represents the music in libraries of users who choose to utilize Google’s music service and would not be considered a reliable source of true historic information of popularity. There is nothing wrong with that reality, but it is important to understand.
This visualization is exciting and fun, but as far as a meaningful resource for true historic information about music popularity through time, that is simply not the purpose of the graph. Unfortunately, the graph is not clearly defined and axis are not labeled well. Additionally, the presentation restricts the availability to find data that may be more obscure. It would be best to consider the following adjustments. First, clean up and relabel the visualization. Second, reconsider the format of the visualization to make comparisons clearer. I would suggest a line graph to represent change over time. The same information can be presented in this format in a more comparative format. Finally, due to the complexity of the graph, more information would be valuable to more quickly understand the limitations of the available data.
Music Timeline. (n.d.). Research.Google.Com. Retrieved September 18, 2020, from http://research.google.com/bigpicture/music/
Two Interesting Visualizations Assignment – Visualization 2
The future of labor in light of the development of AI is a particularly interesting topic. There are significant data and conversations available to demonstrate a real impact on available jobs as tasks historically accomplished by people are completed by technological automation. I am interested in a visualization as part of an article released by The National Academy of Sciences (Frank et al., 2019).
Message(s) the visualization is intending to convey.
The first point demonstrated by this visualization is the decrease in employment share from ’07-’16 from ’99-’16 for low wage jobs compared with an increase in high wage jobs within the same period. Of course, the content of the report discusses the situation in detail, but the assumption is this demonstrated change is experienced as AI and automation increases during this time frame. The second charge complements the first by filling in additional information. Prior to 1999, a critical developmental point for automation, median income wage growth tracked almost directly with output per hour. As technology has made labor more efficient, these two outcomes have diverged with outputs dramatically increasing while medium incomes have remained relatively stagnant through the following fifteen years. Combined, the two charts attempt to demonstrate the decreased availability of lower paying jobs due to the efficiency gains provided by technology. The bottom portion of the visualization demonstrates a shift of available industries, vocational options, and markable skills as the job market is transformed through technological efficiency gains. Available jobs are now located in careers demanding narrower and educated fields yielding workers who can facilitate the development and implementation of technological developments rather than simply accomplishing tasks through manual labor. The result is experienced through increased skill and wealth disparity (2019).
A critique of this visualization
The intention of the two series within the visualization is admirable. The top two charts lay a foundation for the concluding statement made by the bottom portion. Because the data on the top portion of the visualization is true, the result is the shifts represented in the bottom portions of the visualization. It provides a helpful summary of the content of the related section of the released article.
There are some visual difficulties with the two charts in the top of this visualization. First, more care could be given to provide for alignment of the two charts for greater visual congruence and balance. Additionally, the scale of the data is inconsistent creating some difficulty to adjust and gain a helpful understanding of what is being represented. Y-axis labels could be rotated to be more readable, and cleanup of markers could motivate understanding of the scale of data provide.
Overall, care could be given to make these two charts look more appealing. However, the charts themselves do not include significant enough data to accurately communicate the intended conclusion. For example, automation utilization is not present in the graph. The assumption that automation began influencing productivity around 1999 must be asserted, along with an increasing trajectory to at least demonstrate a correlation with output. There are other data points which would be helpful. For example, the number of available jobs over time could greatly impact the interpretation of the first chart. Suppose many more jobs were created during the second time period, which we cannot conclude either way from the chart, if that were true, you could conclude the possibility that the number of low wage wage jobs remained the same and many more high wage jobs were created during the second period of time. Although the second chart seems to speak against that possibility, because the actual wage gap is not known in either graph, one must still assume an outcome. For example, if the wage gap were significantly greater within the first period of time, but dramatically decreased during the second, even with the increase of high wage jobs presented as a possibility, you could possibly still expect to see a stagnation of median income, The provide information is simply not enough to clearly articulate the intended point. It is recommended that information should be added to complete the picture.
These two charts could be creatively combined to demonstrate the same reality in a more impactful, cohesively visible way. One way could be to provide an overlay, perhaps a visualization such as a heat map, demonstrating the wage disparities over time as medium income stagnates while productivity increases.
The icons of the bottom section are nice. It could be beneficial to supplement the representation with data to create a strong infographic to numerically demonstrate the shifts in career and skill environments and opportunities.
To be fair to the contributors of this report, this visualization is one within a much broader context addressing an important topic that is worth consideration. Within context, a complete and compelling argument is presented. This visualization tells a story that aides the reader; however, some improvements would help to increase the strength of the visual argument.
Frank, M. R., Autor, D., Bessen, J. E., Brynjolfsson, E., Cebrian, M., Deming, D. J., Feldman, M., Groh, M., Lobo, J., Moro, E., Wang, D., Youn, H., & Rahwan, I. (2019). Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences, 116(14), 6531–6539. https://doi.org/10.1073/pnas.1900949116