As I was looking at different data visualization projects I found this example, which shows the differences in the societal rate of growth within different countries. This visual separates different kinds of data by applying each set of data in the metadata with different colors that are not to bright, and are very clearly different from each other. By doing this the data is far less intimidating, and easier to read and understand. These types of visualizations unlock a new ways in which to share and depict complex data through a visually pleasing and readable source. One problem that arises when creating 4d visualizing data is that the creator could be biased and may end up simplifying the data to the point to which the data becomes unreliable or invalid. This problem is mainly do to the fact that collecting real time data requires observations and analysis to be made on the spot rather than a thorough allotment of time to go through all the factors involving the metadata.
Some mistakes I would want to avoid would be making my visualizations to generic and elementary, by having everything be in the format of a x,y graph starting at the origin. Another mistake that I really want to avoid is being biased and leaving out important aspects of the data, so that I can avoid the consequences discussed above.
Hi Vick! I definitely agree with you that this visual is both visually appealing/captivating, but also not too intimidating. I think it’s also memorable, which is a big thing that I took away from these examples, because I also feel like I sometimes get in the habit of just making basic graphs and charts without considering how the viewer will appreciate my data visualization.
Hey Vick,
I thought that the project that you chose was very interesting. I think that it could be used to predict future trends. I also thought that what you mentioned about being biased in the interpretation of your data is really important as that could drastically sway the conclusion of your project.