Critic #1: Kito
I like the first version a lot more than the second one. The second one doesn't really make sense to me and I don't like that it's broken up. But the first version follows your model really well, but the colors are kinda hard to see between the different reds. Maybe incorporating the black into your model will help distinguish the different data. Also, maybe adding labels for like the countries like Laos and Cambodia. But overall, it looks good.
Kito suggested changing the colors so that they are easier to distinguish. However, I did not choose to use diverging colors because there was no essential "middle" point to be used, so I kept a red gradient but changed the range of data in order to distinguish the reds more easily. I also added labels for the other countries.
revisions:
Critic #2: Andre
I definitely like the first version a lot because it follows your visual model really well. The thing that is a little off for me though is the data on the top right, I don't really understand the numbers so maybe having some sort of description or legend. I don't really read the description first so It becomes a bit tougher to understand. Having the country labeled can also help see what country you're talking about.
I moved the data points at the top right to the top left so that it's closer to the description and title so that people are more likely to see the header and get an idea of what the data represents. I also labeled the other countries.
revisions:
Critic #3: Cindy
Your first one emulates your visual language model really well! It looks like exactly like your visual language model with the map and the arrows that point to the parts of the countries. Change the numbers in your legend so the numbers are understandable. Since you don't have your layout yet, consider how you want to structure this since this looks vertical and you probably need a vertical layout to fit this display in.
I didn't change the numbers too much to be more understandable, rather I changed the location at which people read the head then read the data points.
revisions:
Critic #1: Cindy
I really like your map, it emulates your visual model really well. I like that you moved the numbers to the left, It helps me see what the data is portraying. But i'm not sure about having the other countries outlined like that because of the square constraint. If you consider putting it on the bottom left of your layout, I think it would look really good.
I decided not to keep the map constrained as my visual model did not constrain the map as well. however, I did not include the map of China because I felt it may have been distracting from the map of Laos.
revisions:
Critic #2: Andre
Overall, the image matches your visual, color, abd typography model very well. One thing I noticed first was that since the shape of Vietnam was cut off, it looked like the image was constrained but then the labels were out of the box. When looking at the map, I can tell that XiengKhoaung and Savannakhet were the most impact. Another small thing I notice was that Laos was inside one of its province. I thought that, that part was Laos rather than Xienghouang at first.
I put the entire map of Vietnam on instead of cutting it off, same for the other maps. I also changed the location and size of the word Laos so that it didn't look like it was inside one of the provinces
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Location Element & Critiques
Critic #3: Estelle J
I like that you add a container for the map which makes the map stand out. For the decoding indication, I think you should move them to the bottom right corner or the top right corner since the top left corner has too many things going on, like title etc. Right now the word "Laos" is confusing since I am not familiar with the location of Laos, I thought only that small part is Laos and the other part which has the same color with it is a different country or state or province.
I took out the container and made it look more like my visual model by making them into complete maps of their countries. I moved the word Laos so that it was easier to see that it is the entire country and not a province. I also decided not to move the decoding to the bottom right because it would become more ambiguous of data as it does not contain a label as to what those numbers mean unless it was at the top where people can see the title and then make out what the data will mean.
revisions:
This display shows the reported number of casualties that have occurred in nine of the Lao provinces, all of which are most severely impacted by the presence of unexploded ordnances. These casualties include both injuries and deaths of children and adult within the provinces.
How this element exploits the visual potential of location: The element exploits the visual potential of location by contrasting the major provinces that are affected most by UXO compared to the other provinces that surround. By only using the country of Laos as the location, it emphasizes The impact of the secret war only on Laos compared to the other countries even though this was around the same time as the Vietnam war.
How this element emulates the models:
To emulate my color model I created white borders to distinguish the different provinces and used grey colors to indicate non-data provinces so that it can contrast with the different gradients of red used in my color model. The gradients of red indicate different data with lighter shades having a lesser impact, and darker shade having more of an impact.
To emulate my visual language model I created a flat map with borders that extend only at certain angles, minimal roundness, and separated the lines within the counties with white lines between the provinces. I also used dots and leading lines to name each province that I found to be important with the data I incorporated into the map.
To emulate my typography model I used the default Illustrator font Myriad Pro because it looked the most similar to my typography model in terms of its serifs and spacing.
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Sources/Credits: http://www.uxolao.org/index.php/en/the-uxo-problem/uxo-victims
