Data Visualization Criticism…
Dynamic Visualization Critique
Dance needs music to set the mood, drop the beat, and create the motivation needed to start moving. I have been dancing since my childhood days, and music plays a crucial role in it. Music makes me feel a certain way, which is why for me it had a significant contribution. Different dance styles require diverse music; upbeat music with high beats per minute and energy is suitable for dance styles such as hip-hop and jazz, whereas dance forms like contemporary, ballet, lyrical, and waltz favor slower songs. The genre I fancy dancing to is hip-hop and it is unquestionably the most popular soundtrack. This is the reason why I have chosen the visualization of the most successful hip-hop music labels since 1989.
The visualization can be found here:
The author is uncovering the music labels in the world of hip-hop music. The visualization is performed on above 600 hip-hop music labels over the past 25 years which have made it to the Billboard chart. Major rise and fall are highlighted by understanding the labels that produced the most hits at any given time, further elucidating who was wielding enormous influence on hip-hop’s sound, distribution, and direction. A few of the labels under consideration are Jive, Delicious Vinyl, Luke, and many more established in the most prominent regions of the United States like New York, Los Angeles, Miami, and so on.
Here the music labels are ranked and sorted by the artist’s performance illustrating the number of artists and tracks along with a graph charting the weeks over these 25 years. Moreover, the list can be modified by the use of filters, and with these filters, I was able to twiddle and interpret the information based on year, region, and artists. Furthermore, it is smart to represent music labels as spheres charted on the map of the United States, where the circle size indicates billboard performance. On hovering over these circles it shows the most popular song in the year recorded by the label and other artists’ names who worked with these labels. Moreover, the user can listen to a hit song produced by the label in the year taken into consideration. Additionally, by pressing the play button at the bottom of the map, I was able to scan the map over the years one by one. The performances are also compared and analyzed for the years 1989, 1977, and 2010–15. Thus the visualization over the years makes it straightforward to infer and draw conclusions about how music labels depend on hip-hop music and how the music and artists affect the role of labels in the music industry.
In my opinion, to understand how the record labels are dependent on artists and music’s popularity, a correlogram graph for 25 years can be created. Using this, the relationship between the label’s ranks with the number of tracks released and the number of artists signed contracts with these labels can be determined as these factors also play a vital role. This graph will give us a numerical value to compare and infer which factors contribute more to the success of record labels.
