Data Visualization Analysis: Assignment 1
In all these bar charts and line
graphs the data allocation has been done reasonably well with proper color schemes
and points on the graphs to easily navigate the viewer. In Figure 3 with the data
being so precise the data is closely packed as there is less difference. So,
the use of points on the graphs helps us in understanding the data easily. But when
the data is constantly changing it becomes hard to keep up with the difference
at every step. Therefore, the variations in data can be highlighted more
accurately because if the line graphs aren’t clicked the data isn’t shown which
eventually becomes hard to understand for the readers. The division of years
and months are a bit hard to differentiate as there is a constant change in
every step and it cannot be tracked. The gap between 1990-1991 there have been
so many changes, but the month wise segregation cannot be seen.
Secondly, the use of different colors
makes the data stand out and it feels more appalling for the viewers. The small
gaps and changes can be easily identified as in Figure 8 but as it is a blend of two different
types of graphs which might make it harder to understand. Bar and line graphs
together make the data look organized, but it can be hard to keep track of both
as you must make more efforts in understanding them. The line graph overlaps
the bar graph, and you cannot keep an idea how the data is going about. As the
identification of minute aspects becomes harder eventually for the viewer. In
Figure 9 the values (Trade Balance) have a different starting point as the values
are present above and below zero. This might confuse the viewer in the very first
go and they would require paying more attention to it.
Lastly, the data in each section
has been explained well but the sectioning of months and years could be a bit more
detailed. The overlapping of two different graphs also makes it more difficult
to follow the variations and changes. The monthly deviation in the stats could be a hard to tally as the changes are very minute but with uneven line graphs the wrong information could be extracted. So, by classifying these categories it eventually turns out to be feasible for individuals to understand faster.
Hi Jasmine, good job! I agree that parts of the graph look clustered, but you did a good explaining what you liked and didn't like. I wish you would have included an annotated picture of the graph also.
ReplyDeleteHi Jasmine, I also agree that even though bar and line graphs are the best to use, figure 8 does look complex and all over the place. Since you mentioned figure 9, I wish that you could have included that in your blog post, rather than having to go to the website and check it there. Overall, a great post!
ReplyDeleteHi jasmine, good job! I agree that parts of the chart seem clustered, but you did a good job of explaining what you liked and didn't like. I wish you had added an explanatory image to the chart as well.
ReplyDeleteHi Jasmine, I liked how detailed your analysis of the strengths and weaknesses of the visuals you provided. As you said, a data visualization that is densely packed with a volatile graph (Figure 3) can be challenging to understand, so the usage of arrows with a little title beside it is helpful. Explaining more than one strength of the visuals is also a nice touch, as there can certainly be plenty of information deduced from it. One constructive point I’ll make is that using only 1 visual rather than 2 could have been easier to digest, as there is already a ton of info that can be taken from Figure 3 alone. Overall, good job analyzing the visuals.
ReplyDeleteI think you chose a really interesting data visualization! I really like the detail you went into when explaining the data visualization and it's strengths and weaknesses. I think if you had introduced your chart at the start, and explained the name and where you found it, it would have made it easier for the reader to understand where you're coming from. Overall, great work!
ReplyDelete