Giving Feedback on Blog Posts
Looking at blog posts from other groups
In this post we will give feedback on the blog posts of other groups enrolled in the class on Data Visualization at KU Leuven. The posts we are about to evaluate give an overview of the design process followed by each group and illustrate how the design space was explored. We will give feedback to four different groups in this blogpost for practicality reasons.
EnergyDB
Let’s start with EnergyDB (Group 19). Their blog post on “Exploring the Design Space — Our Initial Ideas” precisely explains the Diverge-Emerge-Converge process. The presented sketches are compelling and show thoughtful considerations of different designs. Their MIRO board shows all sketches with detailed descriptions in an organized way.
Particularly interesting is the Converge 2 sketch, which represents an interactive chart that shows the relationship between the year of construction and the average EUI. It seems visually appealing and logical. The only thing that they should take into account is that different color scales might confuse the viewer. One common color scale for the whole visual might be more appropriate.
Besides, the Emerge 5 sketch is intriguing. It combines bubble graph with a two-dimensional scatter plot. I would suggest to make this sketch interactive in terms of time (year). To see the bubbles move and possibly change size would be visually appealing.
The polar chart of the Converge 4 sketch is definitely a different and interesting design, however I think it in terms of clarity, it is not straightforward or easy to understand as a viewer. As this is the most important consideration for the group, I would not suggest implementing it in this way.
The sketch Emerge 4 is a novel design, which I have not seen before. It also combines the group bubble chart and a scatter plot in an interesting way. Nevertheless, I think that the bow might mislead the eye, since overlaying areas with different colors and bubble sizes suggests an additive effect of the climate types (e.g. the type A is larger than all others combined).
Data Vizers
When it comes to group 5, Data Vizers, they aim to explore the effect of COVID-19 pandemic on mobility and economic indicators across time and geographical areas. Their approach could be useful for politicians and decisions-makers since they attempt to present an analysis of the pandemic’s effects from both an epidemiological perspective and an economic point of view.
The team did a good job during the diverge session. They described several sketches while explaining the reasoning behind the given choice and demonstrating the usefulness of plots to answer their research questions. For example, the flower plot used to illustrate the evolution of cases across regions/provinces within a country was an interesting choice. Although simple, this kind of figure could be integrated within a more complex graph where all countries are shown together with some economic information.
As for blueprints developed during the emerge stage, a more detailed description is missing. In particular, the team explained well in their third post the creative process followed as well as the structure of their MIRO board. However, they could have included more information concerning the characteristics of plots individually. For instance, they have developed a radial line graph showing the number of cases where they plan to include mobility indicators but the way to do so is not clear.
On another issue, the “World Tour” visualization proposed to show which country experiences the greater decline in mobility each week seems promising. Notwithstanding, making it two-dimensional could be more informative since this way all countries are shown at the same time. Additionally, given that they selected New Zealand as a national case-study, they could use a similar visualization to illustrate the movements of citizens over time in this country.
Finally, considering economic indicators other than GDP would add greater value to the analysis. It would be interesting to study the changes in employment in light of the pandemic outbreak. Specifically, the increase/decrease of unemployment figures could be examined and also, if there is information available, the evolution of remote versus in-person workers numbers.
Team Robin
I’ll be looking at the work that group 25, Team Robin, has produced so far and shared on their blog. Their research focuses on visualizing migration trends and connecting these to economic and social factors. I think it’s fantastic that you merged your primary dataset with additional datasets to provide further context to the questions you posed in your first blog post. What I’m missing here is a clarification of why you chose these particular indices over others.
Now, moving on to your designs. You have done a great job of exploring different ways of representing the flow of migration, taking some very novel approaches to highlight changes over time and over different geographic areas. In particular, I found that the diagram on the left is able to clearly communicate the flows of migration, as it directly compares the flows of migration between neighboring countries and continents. In addition, the diagram on the right is able to describe the different flows of migration in a easy to compare way, also adding in the proportion of male to female migrants elegantly.
I really like the idea of using a mirrored world map. I would find it especially interesting to see how the trends of migration vary within a continent and between continents and see how these are different for the different indices you have selected.
The majority of diagrams that you have presented in your blog post attempt to show migration flows from a global perspective. This entails plots that can easily become very cluttered as they contain a lot of different countries. I think it would be beneficial to focus on specific segments of your dataset. For instance, compare how the flows of migration are different for low-, medium-, and high-income countries.
Overall, I really appreciate the work you have put into making your designs. You have found very creative ways to visualize the flows of migration. I will be interested to see how you include the economic and social indicators in your diagrams, to show the different trends in migration and answer the other two questions.
Pallet Town Gang
The group 13 studies how representative are the FIFA winners to evaluating groups and players for the last 6 years of FIFA. Multiple questions were explored in order to better understand the dataset. In a general aspect, the method of explorative data was followed, without imposing some statistical constraints at first. In terms of methodology, the diverging phase is respected and simplified sketches on paper were used.
The world map representing representing transfers of players, seems like a simple and good way of representation, while could be maybe a bit more imaginative, by maybe adding other factors to explore.
The JR12 and the rethought bar charts sketches are very nice in their divergence ideas. We can see some play with mixing different visual aspects while remaining simple(length of bar, and color codes).
In my opinion, The last sketches were less efficient and creative. Drawing a country just for the sake of mentioning it is for example Italy, doesn’t seem very productive, unless it was linked to other countries in a way, or maybe related to the surface of the country. But the symbolic notion, should only be used to simplify the understanding. An alternative would then be to superpose those sketches with others.
In general the sketches, mostly used the notion of color codes. Which is in itself and good exploration, but I would hope you could explore other coding methods in more detail( that seemed to be lacking in presence). Hopefully, you can explore questions a bit further, to have a full approach.
