Exploiting the visual potential of numbers
The viewer should get the message that the United States has a big issue with mass shootings compared to other countries. In addition, the viewer should understand that the number of guns per capita may be linked to the high number of mass shootings in the US. In my first display I am exploiting the visual potential with a horizontal bar graph that has the category of country on the x-axis and the number of mass shootings and guns per 100 people on the y-axis. The numbers are represented by length on the bar chart. In my second display I am using a bubble chart with the category on the x-axis and the numbers as the size of the circles. There is a sharp decrease from the US numbers to the other countries for both data groups and a gradual decrease with the rest of the countries numbers for both data groups.
The first display resembles that of the first display on my visual language model. The horizontal chart set up, different color bars, number labels, title and the legend match my model. In addition, the font matches the axis labels, number labels and title.
Both of my displays follow my color model. The background color is the same and the red, dark blue and light blue/green is the same. The blue and red are used for the data and the light green is used for the axes. Similar to my model how the light green is used less frequently.
Critique
Autumn
Autumn reads the title first, then the legend, and the bars in the first display. She notices the drastic difference between the US and the other countries. She says that it is not necessary to write the 100 at the end of the first bar cause it is explained in the legend as "guns per 100". Autumn mentions that she glosses over the rest of the other countries because the US is the most drastic and at the top of the display.
Autumn reads the title, the red labels from left to right but does not address the country names. She says that in this display it is more like a relationship display for each country as opposed to comparing the circles for each country.
Autumn prefers the first because the data is clearer and more apparent off the bat. She says it is cleaner and highlights the numbers.
Autumn mentions that the first display is kind of busy with the light blue/green as the background. In my model there is a background color for the bars but it is much more subtle. She notices the background color is the same and the blue as the title. She notices the dotted line in my visual language model in addition to the fact that the first display matches the first display in my visual language model with the horizontal bars.
Response to first critique
I took out the 100 at the end of the first bar because it is unnecessary if I say it in the legend and it is not featured in my visual language model. I changed the background color to a light blue/gray that is featured in my color model. Even though it is used infrequently, this color emulates my visual language model more as a more subtle background. I flipped the ordering of the countries to have the most drastic last so that the eye will see the shorter ones first. In addition, the matching display in my visual language model does the same progression from least to most.
Matthew
Matthew sees a lot of light blue in the first display. Sees bars but does not initially know what they represent. He proceeds to read the title and looks at the labels to attempt to understand. He says that he has to keep looking back at the labels to see what the bars represent.
For the second display, Matthew sees red initially. He reads the title, sees the circles and labels and gathers what the inner/outer circles are about. Matthew says, "Number of guns is correlated to the number of mass shootings" which is not what I intended.
Matthew prefers the first because it is clearer to understand the message as a bar graph instead of the circles.
Matthew notices the small details. He says that the text in the bars is larger in m model. He also says that those numbers have more right padding on the bars in my model. The country boxes are more rectangular on my display and they are more square on my model. The text on the labels is colored but is not colored on my model. The background color of the bars is more toned down on my model.
Response to second critique
I kept the legend even though Matthew had to keep looking back and forth from the bars to the legend. The legend matches my visual language model and keeps the design cleaner and not full of words. I made the text in the bars larger and with more padding to match my model. I keep the country boxes rectangles to fit the typography model with the correct size of text. I also kept the text on the labels the same because my color model does not offer enough colors to add a neutral color for both. I changed the background color of the bars to match the toned color in my visual language model.
Liam
For the first display, Liam sees the title and reads the label. He sees that the US has much larger red and blue bars, sees that Germany, Canada and France have medium blue and small red, and sees that New Zealand has small blue and red. Liam thought that the number of mass shootings was measuring people killed as opposed to how many happened in a year.
For the second display, Liam first reads the title, reads the x-axis first then the inner and outer circle labels of the US circles. He then reads in that order left to right. He says that he still sees that the United States as an outlier. He notices that the size of the circles of France, Germany and Canada are similar.
Liam prefers the first one. He says that the design is way clearer with the given data and it is easier to see the trend with the separated bar chart.
Liam has the same visual language model so he notices the dotted line as the same size and that the title matches with the spacing and text size. He notices the font for the countries is the same. He says that I should consider using the subtitles like the "gender" and "age" one because those are well-received.
Response to third critique
I added "number of mass shootings in one year" to make it clear that the number of mass shootings was not measuring the people killed as opposed to how many mass shootings happened a year.
Viewers of this display should gather that high gun ownership may be linked to mass shootings. Another major point that viewers should gather is that the US has a drastically larger amount of guns and mass shootings compared to other countries.
By switching the order of the countries, the viewer should be able to compare the countries and see the trend as opposed to just seeing the US's data. In addition, the larger numbers and country labels highlight the numbers and the category they are associated with. The addition of "in one year" to the "number of mass shootings" label exploits the visual potential of the numbers by making it clear the length of time that the numbers are being measured.
I flipped the order of the data so that it goes from least to most to match the first display in my visual language model. I increased the size of the number labels and country labels to match the size on my typography model. I muted the background color of the bars to match my visual language model.
I changed the background color of the bars to a muted light blue/gray that is featured in my color model with the dark blue, red and light blue.
Critique
Josephine
First Josephine sees the red and blue bars then she reads the categories (countries) and reads the title. She reads the key last but says it is clear that he two bars represent different data for the same country. She says what she anticipates match what she sees.
Josephine prefers the new version because the light blue background distracts from the numbers.
Josephine notices the small comment about percentage to the right of the similar display on my visual language model. The label boldness or size on my model looks off and distracts from the title because they are more prominent in my model and longer. IN addition, I color my labels and in my model they are not colored.
Response to first critique
I did not add a comment to the right of my display because there is no more information that is needed to be said that the legend does not say. I changed the font size of my legend to make it smaller and the same size as the legend on my typography model. I again did not change the font color of my labels because it is confusing with my color model to have the red label have the dark blue font that represents something else in my display.
Ali
First, Ali sees the different country names first, he reads the labels and relates the bars within each country. Notices a small number for both bars for New Zealand and a large number for both bars for the US. The middle ones still have a small number of mass shootings but a larger number of guns per 100 people. He stills sees the increase.
Ali prefers the new version because he says the 100 was unnecessary because it says 100 in the label. He recognizes the flipped order and says that it shows a better message.
Ali could not find the light color at first and says that it is not really used as a background color but mostly for an icon. He sees a slight difference in the font title compared to titles in my typography. He says the labels on the graph match and the key is laid out the same as my model.
Response to second critique
I have kept the light background color the same to emulate my visual language model. The color background in my visual language model is subtle and light like the color I have chosen from my color model. In addition, these colors are used in my color model but just infrequently. It is used as a background for people in the shape of a circle in a display on my color model.
Harry
Harry reads down the page starting with the title and the key. Since he read the keys he knows what the different colored bars represent. He starts with New Zealand and reads the data down to the United States where he is astounded.
Harry prefers the revision because of the better emotional impact by having the United Staes last. Having to read the smaller numbers for the other countries and then the drastically large numbers with the United States is really profound. He also likes that the background color of the bars provides a lower contrast and is more muted. Overall, the design is much cleaner.
Harry searches for the light color so I point it out. He says I use them the same in that the red, blue and the light color are used together. He notices that the dark blue and red are used together frequently. In terms of visual language, he says that the bars within each year on the display are flipped with the bigger bar above the smaller bar. Mine is the opposite.
Response to third critique
I flip the bars within each country to match my visual language model.
Viewers of this display should gather that high gun ownership may be linked to mass shootings. Another major point that viewers should gather is that the US has a drastically larger amount of guns and mass shootings compared to other countries.
By switching the order of the bars within each country the viewers should gather more of an increased trend going downwards. Since the eye is typically drawn to the red, having the dark blue on top draws more attention so both bars are analyzed.
I made the font of the legend smaller to match my typography model. I switched the bars within each country to match my visual language model that has the larger bar on top. I also spaced out the title and legend more to match my visual language model.
I kept the colors the same to continue emulating my color model.