Exploiting the visual potential of location
For first display, I used different colors to encode regions on the map with different percentage data, and include the encoding legend on the side. For the second display, I put the percentage label on top of the corresponding regions on the map, and only keep 2 colors to encode the numbers - the higher 2 percentages are grouped with orange and lower ones are in dark green.
This display is used to communicate the proportion of yoga practitioners in each region of the United States - yoga is most popular in the west coast and the Mid Atlantic region.
For my display, I reduced most outlines for the state boarders in the map and used solid color blocks to emulate the flat design in the model.
In the first display, I used the 3 different shades of orange with dark grey in my color model to encode the 4 levels of popularity in the map. In the second display, I grouped the 4 levels into 2 main categories and assign them with orange or green.
I used the bolded capitalized title for my subheading, and used the small light typeface for the descriptive text, which followed the same font style in the text model.
I designed my location display to fit the bottom right corner of my layout model.
Critique
Julie Zhu
In the first version, I see a big bold title with a subtitle underneath. There’s a color coded U.S map with each color representing a percentage range. I don’t know what the white areas are since they are not explained by the indicators. There seems to be a typo in the big title where it says “reginal “ but should be “regional”. In the second version, I see the same big title and subtitle. The U.S map seems to be pulled apart from each other. It increase the visual clarity. The percentage on top of each area makes it easier to see the data. However, the blue and orange color coding is a bit confusing. It took me a bit of time to find the pattern between two colors. It seems like orange meaning above 15% and blue is below 15%.
I like the second one better because pulling states apart makes it more visually appealing, and the percentages on top saves the trouble of looking among the area, color, and the indicator.
It seems like you emulated the color, typography, and layout model well. In terms of visual language, you don't have a location display model. However, your maps all look 2D instead of 3D, which matches the flat design of your visual language model. You used all the colors exists from the model, and the text style are a close match to the fonts used in the model. I can see your display will fit pretty well in the section in your infographic.
Response to first critique
Julie pointed out the typo in the subheading of my display, I fixed this issue in my revised display. She liked the simplicity of colors and separating the states in my second display. I agreed that using just the orange and green for my map is enough and creates a better contrast., so I used the same color scheme for my revised version. In response to her comment that using just 2 color for encoding 4 different categories is confusing for viewer, I separated the 4 types into 2 maps, and used labels "above 15%" and "below 15%" to help with distinguishing the more popular regions and less popular regions.
I also got rid of the white region that represents the lakes since it deviate from my color model.
Emily Qiao
In the left one I see a US map with different colors group the map into four sections. In the right one you divided the map into eight parts and I think areas with higher participation rate are orange and the other areas are blue.
I like the right one better because the left one has too many colors going on and it may not be a very good match of your color model. However, the right version did a really good job in emulating your color model and I think it’s really smart what your separated the areas. It’s like a jigsaw!
Both of them matches your visual language and typography model very well. The only thing is the left one has too many colors and I don’t see where does the blue come from. The right one, however, did a really good job in emulating your color model.
Response to second critique
I agree with Emily's comment that the second display is a better emulation of my color model, so I chose it for my revision. She commented that separating the areas in the map is a smart way to separate the regions without outlines. I kept this design strategy for the revision so that my map can distinguish the areas with but still following the "no outlines" tradition in the visual language model.
Chelsea Wang
In the first version, I can see a US map with different color encoding on different states. I see the title and sub-title which helped me a lot in making sense of what the map is about. I also see the color indicators on the left side. In the second version. I see the title and subtitle as well. I also see different color encoding with numbers on top of the regions.
I prefer the first version better. I like the color in the first version but I think you might consider changing the <10% color into a lighter orange. I think that makes more sense to me since the color you have right now is still pretty dark. I think you also need to explain why there's a white region on the top right on your map. However, I also like the clarity in the second version. And the simplicity of using just 2 colors increases the visual clarity as well. But compared to the first one, the color in the second version looks less fun.
I think you did a good job emulating your visual language model and color model. I can see the same level of details in both your model and your display. Since your model doesn't have a location display. It's hard to emulate but I think you did a good job.
Response to third critique
Chelsea commented that the first display would look better if I can use 4 gradient colors of orange to encode the increasing percentage in the map, however, this is impossible as my color model only has 3 gradients of orange that I can use, and using a lighter orange for the <10% area would be deviating from my model. So I decide to revise the second display and kept the simple color encodings to follow my models. Although using the 2 main colors (orange and green) may be less fun, I believe they create a nice contrast compared to having a gradient, which makes the map more visually appealing and clear to the viewers.
For the revised display, I used text labels on top of each map to distinguish the areas with more yoga practitioners and areas with less. For each map, the I put percentage data inside each region it represents. I also used orange to encode the regions I'm talking about in each map, and used dark green to encode the non-related regions.
This display is used to communicate the proportion of yoga practitioners in each region of the United States - yoga is most popular in the west coast and the Mid Atlantic region.
I used solid color blocks for encoding to match the flat design in my visual language model. For both maps, I kept minimal level of details and restrained from using outlines for separating the areas in the map.
I separated the 4 categories of region into 2 maps so that I can encode them by using just the orange and dark green from the color. The highlighted areas are in orange, and the remaining areas are in dark green.
I used bolded text for my subheading to emulate the font style from my text model. I also capitalized the title to match the model. For the body, I used thin small text which has the same typeface as my model.
I designed my location display to fit the bottom right corner of my layout model.
Critique
Eshin
I see 2 maps in 2 different colors, the orange part is showing the percentage, I see title saying it's about percentage of people doing yoga. It seems like the west and east coast have more practitioners. The map on the right is a bit separated for the regions.
I prefer the revised version, the label "above 15%" and "below 15%" helps me see how the data is split into 2 maps. I didn't realize the percentages inside eacb map are actually of different range. Because just by looking the 2 labels on the top, I assumed all orange regions are of the exact same range. This version is easier to compare the areas with different popularity of yoga. However, I suggest you experiment with just one map and split the data into 3 levels so you can use the 3 shades of orange in your color model - this way you can clearly encode all the regions given the limited color palette.
You are able to emulate the visual language model well as you are using only fills in the map, which matches the visual language model pretty well. I see the same same greenish blue and orange used from your color model.
I see that you are emulating the label of percentage in the text model for the titles of the map. If you can try having different shades of orange in your map, you will be able to emulate the color model even better - since right now your infographic is just using one type of orange.
Response to first critique
Eshin pointed out using just one color (orange) encoding for regions of different percentage is confusing, and even separating the map into 2 small map doesn't help. I agree with her and took her advice to add more shades of orange in the map. I regrouped the data into 3 categories - less than 10%, 10-15% and 15-20%. With 3 colors, viewer are more easily tell the different popularity in areas by the gradients.
Julie Zhu
I see 2 maps showing the different popularity level of yoga across the US. It looks like the west and east coasts have more people practicing yoga. The maps are separated with regions with yogis above and below 15% percentage of the total population.
With the revised version, I still find it hard to tell the difference between the different range of percentage, since you're using orange to highlight all the regions. The same color encoding makes me assume that all the orange parts in the map have the same data level. The labels inside the each region are a bit too small to recognize. So I suggest you try using legends on the side to represent the color encoding so it's easier to read.
You are using solid color fills with no outlines in your map, which follow the pattern in your visual language model. The orange and blue you used are the 2 theme colors from your color model, so good job using just 2 colors to produce a map! Your text are using the same font weight and typeface as your model, and I think the display will fit very well in the bottom right corner you planner.
Response to second critique
Julie commented that the small label on top of each region are too small to read, which adds to confusion on top of the single color encoding for all categories. In response to her comment, I encoded the regions with progress colors. I followed her advice of using legends to communicate what the number the color corresponds to, and got rid of the text inside the map. This not only helps make the map cleaner, but increase the clarity of communication as well.
Emily Qiao
I see the title saying the percentage of yogis in the US. There are 2 maps - one shows regions have over 15% people doing yoga, and the other shows regions with less than 15% yoga practitioners. It seems that areas with different percentage is separated. All the regions are highlighted with orange to distinguish from blue.
I prefer the revised version, even though the regions of different percentage are split into 2 maps, they are still in a whole since you are using the same orange for highlighting, and green for the other regions. However, the different width of gaps between the areas bothers me a bit, so you may want to consider ways to avoid having wide gaps, and even consider ways to not include gaps in the map -- if you can consider using different colors of orange so the areas can be naturally separated by the color.
You seems to following the simple visual language language model, where you're using solid fill in the maps and keeping the flat design. You are using the same orange and dark green from your color model, and the same bolded text from your text model. Although none of your models include a map, you did a good job emulating the models.
Response to third critique
Emily suggested getting rid of the gaps so the map will look cleaner and more as a whole. By changing the encodings into different shades of orange, I was able to achieve that. Even without the gaps, the colors naturally divide the regions into independent areas on the map.
For my location display, I want to show the percentage of yoga practitioners in the US from different regions. In order to communicate the areas of different percentages, I used 3 shades of orange to represent the level of popularity from lowest to highest. For the color encoding, I followed people's common sense that darker area means larger number. As for the legend, I used a vertical layout so it shows the decrease of number from top to bottom, which also align with people's reception of vertical placement.
I used solid fills for encoding the map to match my visual language model, and used circle legends since the model has circle shapes at the bottom section. I kept the design minimal to match the flat design in my model.
I used the 3 shades of orange from the bar chart in the color model. Since the model do not have the green color in the display, I also restrained from using green color in my display.
I used the same bolded capitalized font style for the subheading from my color model. And kept the body in thin small typeface to match the model.
I designed my location display to fit the bottom right corner of my layout model.