Monthly Archives: September 2026

Black Super Bowl = Genre Streams???

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Many artists and people see the Super Bowl as the pinnacle of music popularity. So much so that the Super Bowl doesn’t even pay the artists for their performances; it is almost purely for “exposure.” Now that all forms of media are monetized, artists are very likely to gain some revenue from their Super Bowl performance anyway, but the main goal is increased media presence. In this day and age, there are two main standards to meet in order to be regarded as a popular or successful artist in the US: Grammy awards/nominations, and streams. Streaming apps such as YouTube, Spotify, Apple Music, etc, have almost completely rendered physical media useless. It has become the way that major music corporations across the world now use to gauge success.

With the Super Bowl being one of, if not the largest, stages for an artist, I wanted to see how much of an impact a Super Bowl performance has on the number of streams of that artist’s particular genre within that year, and if there are any notable changes from the year prior or after. And then I further made the decision to only go off of the black Super Bowl artists from 2010 – 2023, as there has been a higher frequency of black artists being chosen to perform for the Super Bowl. Then, after identifying each black artist or group that was predominately black, I then classified them into only one genre. This was met with some difficulty because the data and Spotify itself limit one artist to one genre, even though their music career may span over many different genres. So I just classified them into a genre that fit their music the most at that time, so example, even though now most will say that Bruno Mars makes RnB and Soul music, the majority of his music circa 2014 was pop. Although I still chose to have Bruno Mars and Beyonce cross listed with pop and RnB. Another hiccup in this was the year that many different artists that were under Death Row Records headlined in 2022. The artists were: Dr. Dre, Snoop Dogg, Mary J Blige, Eminem, and Kendrick Lamar. While Mary J Blige is infamous for her RnB records, as the majority of artists and the theme around this year’s Super Bowl performance were California Rap and Hip-Hop, it was labeled under the Hip-Hop/Rap genre.

chart visualization

The graph above was created using the aggregated dataset mentioned previously. I made sure to emphasize which year there was a black artist during the Super Bowl and the name of the artist. As you can see above, using the pop genre as an example because it has the most Super Bowl performances, after 2019, all streams went down collectively, and the Super Bowl performances for the following years were no exception. This can be seen for all other genres as well. In conclusion, based on the data, there is no significant change in the number of streams per genre based on the black Super Bowl artists’ performances of that year after 2019.

While my question did not lend any new and profound knowledge to the world, this project has taught me a lot about data literacy and visualization. The main shift in my thinking about data is determining what data is necessary and unnecessary. The entire dataset includes the streams for genres not shown above, and when I was looking at all the categories, I got lost in what I was supposed to find, which made it harder for me to generate a question from it. I often have trouble creating interesting and achievable research questions. Because of my refusal to leave out information; I tend to find myself trying to incorporate everything and every possibility into my research, which is often times impossible. So instead, with this project I made the conscious and honest decision to only ask questions about what I was personally the most interested in. This also allowed me to analyze my own visualizations’ legibility with graphic design practices such as color, contrast, and typography. Making sure that the colors used in the graph were distinguishable from one another and that the axis and labels were legible. If I were to do this project again, I would first find another question to ask that went deeper into data manipulation and context. Reorganizing or using the data more creatively and knowing when, where, and how the data was sourced, as the data gathered does limit the breadth of questions that can be generated.

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Holt Demo on how to embed a flourish viz

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  1. After you publish your work on Flourish, view it in a new tab. Copy the url – NOT the html embed code, but the actual web link to the published version of your work.
  2. In your post on Voices, click the + sign to add a new block. Search for an “embed” block, and add it to your post.
  3. The embed block will prompt you to enter in the link to your published data viz on flourish. Paste in your link, and save. This should be all you need to make your live, interactive data visualization appear on your post.
  4. If you get an error message, the first thing I would troubleshoot would be to make sure that you have published / made your data visualization public on Flourish. Then, I would look carefully at the url: if it ends with “edit”, you picked the wrong url – you need to publish and then take the url after you open your visualization in a new screen.

If you want to make any updates to your data visualization, make sure you are doing this on Flourish -when you are happy with your changes, you can just re-publish to make your changes “live”.

Project Report 1: Dogs Names vs Breeds

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In my graph, I am trying to portray the number of dogs, per breed recorded, that are named “Max” in Staten Island, NY. However, it will only let me report all of the names. The Y-axis is the breeds of dogs recorded (unfortunately, the axis would not label correctly after trying to work with it for a while), and the X-axis is the number of dogs per breed with the corrolating name. The use of the bar graph shows the difference in amounts by color-coding each breed.

The way that the data was set up the information was limited. There is no way to know if the data is recent, if it changed based on trends over time, or account for dogs not listed. When collecting the raw data I would look at age of the dogs, time name was collected, and age of owners to see if that impacted the naming process. Ethically, I would like to know if the names were given or if the names were collected through records. Do the owners know that the names and breeds have been collected?

In reflection, the tool Flourish was very confusing to use. I am not familiar with doing data online and when trying to name axies I was not able to customize them. The key is also a bit too much and I would maybe limit my data to just the top 5 breeds. When reflecting on the data itself, i go back to why i chose to research this question specifically. My dog is named Maxwell, Max for short, and he is a labrodoodle. I wanted to see where he would fall onto this graph’s data. However, it only let me do all the names, and I was unable to limit my data.

Module 1 Project Report: Data Visualization

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When first introduced to the data, my first question was this: is there a strong correlation between dog breed and name? I hoped to learn how to effectively communicate information of this nature through a legible graph. Flourish as a tool seems to be a largely egalitarian one, with its shortfalls stemming from its internet-based nature, rather than a severe design flaw. It’s easy enough to use, but more complex tasks have a learning curve.

When I first uploaded the data to Flourish, most of the charts created for me were nigh-unreadable. Due to the nature of the data, being more qualitative than quantitative in what it measures, the software seemed to be less equipped for such a task. The animated bar charts moved at a speed and severity that was wholly unpleasant to look at, and the pie charts looked like irises given their extreme amounts of subdivision. A heatmap seemed to be the best choice, as it was able to effectively communicate the density of both name and breed in a relatively readable format. I tried to see if there was a way to get the numbers within the chart to display without hovering one’s mouse, but I was unsuccessful in this task.

Here is the heatmap in all of its glory. My goal was to display as much information as possible while still remaining relatively readable, and I feel that I was able to accomplish this. Comparing it to the spreadsheet itself, I realize that the heatmap is little more than a reprinting of the data, this time represented with colors rather than numbers. This decreases the amount of processing power one needs to expend while looking at said spreadsheet. As well, by looking at the distribution of the “heat” on the “map,” we can see a cluster of larger numbers in the bottom left, directly indicating a connection between breed and name. A more even distribution would not have such pockets of deep blue.

In the process of making this heatmap, I realized that, sometimes, it’s okay to ignore some of your data. While, of course, I did not do this, I do think that if this graph were to be shown on a slide rather than on a webpage, where it can be interacted with, loping off some of the rows and columns would not be a terrible idea. As well, I realized that, while tools like Flourish are a godsend for amateur data visualization, more complicated and specific tasks can be prohibitively complex. I at times felt like I was learning to read, and my only book was Finnegans Wake. If I find myself needing to use this resource for a more intense and professional environment, I would likely spend some more time learning the basics.

I did some fruitless research on whether or not I could display the actual numbers along with the colors, and the relevant Flourish blogpost can be found here. I did not use any generative AI in the production of this blogpost or in the creation of my chart, however I did use the built-in spellcheck on WordPress.