# A1-SarahVanWart

## Good Visualization

### Source

Silver, Mark. "High School Give-and-Take." National Geographic, September, 2008, p. 22. Art: Sean McNaughton, NG Staff. Source: Council on Standards for International Educational Travel.

### Explanation

This figure is a two-part visualization depicting the diversity and quantity of high school foreign exchange students who come into the US and who study abroad. The top diagram depicts colorful cascades, which represent the quantity and diversity of the high school foreign exchange students that "pour" into the United States each year – 29,004 in all. The bottom diagram shows a similar cascading image, but inverted and less than one tenth the size of the top diagram. This image represents the number of US high school students who study abroad (only 2,255), and in which countries they study.

### Deconstruction

The data is a statistical data model, where countries and continents are nominal variables, and the number of high school foreign exchange students studying in a particular country is a quantitative variable. The data is essentially bivariate – count of exchange students by country – but continent, a meta-category, serves as an additional grouping.

Color is used to group countries into a particular continent. Width (size) is used to indicate the number of students that study abroad from each country. Proximity (location) is also used to group the countries together, while the countries are differentiated from neighboring countries on their continent by alternation the hue from dark to light.

### Critique

As discussed in class, the width of the intersection of a country’s color ribbon with the x-axis represents the number of students who either (1) study in the US [top diagram] or (2) study abroad [bottom diagram]. Visually, one can perform a quick spot check to determining the proportion of students coming in versus going out for a particular country. For example, one can easily see that although 8,189 German students come to the US to study each year, only 440 US students study in Germany. However, an inadvertent consequence of this design is that each country’s ribbon has an area, which is based on where the country is positioned with respect to the edges of the diagram. This area is arbitrary – the width of the x-axis is the measure of significance – and the area actually distorts the viewer’s perception of the visualization. In the top diagram, although Sweden and Denmark supply the US with an almost equal number of exchange students, the area of Sweden’s ribbon is disproportionately larger than Denmark’s.

### Source

Kagan, Robert. "Power Play." Wall Street Journal, Saturday/Sunday, August 30 - 31, 2008: W5. Image from "Sipa/Associated Press."

### Explanation

This visualization attempts to show the viewer a snapshot of the US, Russian, and Chinese military, and is associated with a Wall Street Journal article remarking on the importance of military might – as well as economic power – in light of the Russia / Georgia conflict. By giving the viewer some diagrams, figures, and statistics, the graphic aims to give the viewer a better understanding of the military profiles of each of the three countries highlighted.

### Deconstruction

Like the “Good Visualization” above, the data model is a statistical data model, where country is a nominal variable, and GDP 2000, GDP 2007, defense spending 2000, defense spending 2007, total population 2007, total number of active duty soldiers 2007, and total number of reserve soldiers 2007 are quantitative variables (eight variables total). The image model is multi-dimensional, though most of the data is displayed numerically, rather than visually. The visual components –photographs of each country’s military and soldier icons depicting each country’s number of active duty soldiers – are positioned side-by-side to assist the viewer in comparing the data. Very few visual encoding are used to describe the data, other than position (the profiles are displayed adjacent to one another) and size (number of tiled soldier icons).

### Critique

Selection of Variables: Although eight variables are analyzed, only two – “active duty soldiers 2007” and country – are displayed in a way that visually makes sense of the numbers. This decision to visually highlight “active duty soldiers 2007” while only numerically displaying others may disproportionately weight certain variables more than others, thus skewing the viewer’s perception of each country’s military snapshot. Though such a weight may have been appropriately given, no support for this claim is mentioned in the article or in the diagram. The diagram gives the impression that China has surpassed the US and Russia as a military superpower based on the variable selection.

Display of Visual Variables: For the “active duty soldiers data” that is graphically presented, the lateral tiling of soldier icons is not as easy to compare. It would assist the user if these icons were displayed next to one another, or in some other forma (such as bar chart) so a quick comparison could be made. In addition to the difficulty the viewer might have in comparing the soldier icons, even more difficult still is the task of the comparing numerical statistics for all of the variables reported. I found myself asking questions like:

• How do the reserve troops compare with one another?
• How much money does each country spend on defense again (I kept going back and forth between the numerical statistics and attempting to order them in my mind)?
• How much bigger is China than the US or Russia (again, the numbers were difficult to conceptualize)?
• What percentage of their population is allocated to military personnel?
• How big are the economies of these countries, and how much of their spending is allocated to military expenditures?

Since population and expenditure percentages are presented in the snapshot, some visual representations and comparisons of this data would have been helpful.

### Redesign

In order to improve upon the figures displayed above, I created a series of diagrams to help the viewer better understand the data, and divided the visualizations into two categories – population-related diagrams, and budget-related diagrams as follows:

Part I: GDP and Military Expenditures

Figure 1

Figure 2

Part II: Population and Military Personnel

Figure 3

2000 and 2007 GDP data taken from the World Bank's Data Query Tool: http://ddp-ext.worldbank.org/ext/DDPQQ/member.do?method=getMembers&userid=1&queryId=135

#### Figure 1

In the first diagram (top left), I compare each country’s GDP with its military spending for 2007, to assist the viewer in visualizing how each country allocates its financial resources. I felt that GDP would be a helpful visualization to give the viewer a quick understanding of the economic size of the three countries. I attempted to use a “drill-down” approach in which the broad chart (bottom) would convey military spending as compared to GDP, and the detailed chart (top) would highlight just the defense spending. Rather than using numbers alone, a bar chart allows one to more easily see that the US spends proportionally more on military (4.17%), followed by Russia (1.65%), and then China (1.39%). Note the different impression that an economic visualization of the data gives, as opposed to a purely active-duty-centric visualization.

#### Figure 2

In the second diagram (top right), I compare the percentage growth in both GDP and military expenditures from 2000 to 2007. The original diagram simply listed a country’s increase in defense spending:

• China increased spending by 222%
• The US increased spending by 96%
• Russia increased spending by 14%

However, though the numerical statistics alone give the impression that China is disproportionately ramping up spending, when one looks at a graph of military expenditure growth juxtaposed next to economic growth, it seems that the US has actually increased spending more than China with respect to economic growth. Russia's defense spending, on the other hand, has not kept up with economic growth. Again, it looks as if the US has had a disproportionate increase in military spending since 2000 as compared to GDP growth, which is not reflected in simply listing the statistics.

Note: Russia's GDP growth data doesn't seem quite right, though I retrieved these statistics from the World Bank's data repository -- I would need to investigate this further.

#### Figure 3

In the bottom diagram, I compare the populations in each of the three countries by dividing the total population into three subsets – non-military personnel, reserve soldiers, and active duty soldiers. Again, I use a drill-down approach to put the data into perspective. The broad chart shows military personnel as a fraction of total population for each country, and the detailed chart shows only military personnel. When seen from this perspective, the disparities between the US, China, and Russia look different. Both the US and Russia are leveraging a proportionately higher percentage of their population towards their military. Compare this figure to the original diagram, where the soldier icons are tiled above. The original diagram seems to over-emphasize China's military might by only depicting active reserve soldiers.

#### Conclusion

The visualization re-design has used all of the same data as the original visualization, but has converted more of it into a graphical representation. The result is a slightly different depiction of military power. By bringing incorporating the country's economic data, population data, and active reserve data, the viewer can gain a more complete interpretation of military profiles among the US, China, and Russia.