Global Road Traffic Deaths

Katherine Panich

Many issues readily spring to mind upon hearing the term “global health”: starving children in low income countries, alarming increases of childhood obesity around the world, and contaminated, disease ridden drinking water to name a few. Traffic injuries and fatalities are not amongst these topics. Often overlooked, road related accidents are becoming ever more prevalent and lethal as low income countries begin to urbanize and industrialize. The urban populations swell far more quickly than infrastructure can be built to support the expanded needs of the area, proper roads especially. As such, for this visualization project, I chose to examine data from the World Health Organization describing the gross number of road traffic death for 181 countries as well as the deaths per 100,000 in each country in the year 2007.

I started with a world map for my first visualization in order to see on a macro level the severity of the problem. Because the data provided is so comprehensive and includes so many countries, the size of the issue is starkly apparent. As shown in the two maps, it affects every country, no matter the socio-economic status. With regards to the total number of deaths, India and China stand out as the darkest regions, but this coincides with the fact that these two countries contain the bulk of the world’s population. The fatality rates give a clearer picture of which regions are most gravely affected, specifically Africa. I made the visualization on the Many Eyes’ website, and as such I could not change the color scheme as I wished. The program made it simple to upload and display the raw numbers in a more easily accessible format than the columns of a data sheet, but at the cost of creativity and individuality. This visualization appeals to a wide audience as it requires only an understanding of geography and color gradation in order to comprehend the data. It is not bogged down by excessive statistics, though those are provided in a key on the side in order to make sense of what the colors represent and for those who require statistics in order to validate statements.

In order to get a better grasp of the situation around the world, I pared down the 181 countries to 23, carefully choosing ones representative of their region, population, and statistics. For example, I excluded Finland and Switzerland because their data was nearly identical to that of Germany. In doing so I noticed an interesting pattern. In many cases, the number of deaths and the death rate did not align. Take China. Even though it had the highest gross number, its per capita rate is in the median range. Conversely, Middle Eastern and African countries have a relatively low number of fatalities, but these deaths compose a great deal of their annual deaths. Small island nations prove interesting for while the small number of deaths reflects the small populations, their death rate is either close to zero, as in the Marshall Islands, or one of the highest in the world, as with the Cook Islands which has a rate of 45 traffic related deaths per 100,000 people. In constructing the visualization in their line graph format, the correlation between the two factors is highlighted. The colored lines along the blank background put a focus on the data itself, with little outside imagery to distract the eye.

This next visualization also is more mathematically based, but instead of showing patterns between the number of deaths and the rate per 100,000 individuals in a population of countries typical in their region and socio-economic status, this graph focuses solely on the number of deaths and gives a broader overview of the issue. It breaks down the sheer annual number of road deaths into how many occurred in each region. This is illustrated by the percentages associated with each segment of the graph. In this visualization, I attempted to merge data and pictures. To remove number clutter, I took the original excel spreadsheet and divided the 181 countries into six different WHO regions represented in the data. I took the total road traffic fatalities for each category and from that determined the percentage composition. The pictures in each segment depict either a fatal accident that occurred in a country in the region or typical traffic conditions. In having a more visual, conceptual representation of the data, I intended to draw a more emotional response.

Having the photos in the slices brings a humanistic aspect to the chart, putting the data into perspective. I attempted to show that this data is not just numbers in an Excel spreadsheet, but represent tragic accidents and deaths of individual people. In keeping with the somber attitude in this chart, I kept the colors mostly monochromatic, with hints of bold colors for emphasis. In global health, it is easy to forget that the figures in statistics represent single individuals because when working with such large numbers, like 175,668 people in the Eastern Mediterranean region perishing on the road, it is easy to get overwhelmed because it is difficult to visualize 175,688 different people. But if you accompany these statistics with images of single cars on fire or the common image of a traffic jam, then that puts the data into perspective and in a more relatable context.

With this final visualization, I focused on pictures and representations as opposed to numbers. In doing so, I created a representation that could be understood at a glance, without having to look at keys in the margins, or worry about trying to determine which segment coincided with which piece of data. I chose four salient points that I wanted to highlight and portrayed them in an easily accessible format. No prior knowledge or terminology of global health is needed for full comprehension. It simply describes facts through pictures. I wanted this to be accessible to all age groups, so I kept the pictures cartoonish, and fairly pleasant, leaving the images of death and destruction to the pie chart. I created it with adolescents in mind, having stark facts at the top of the page and two supplementary bits of information to keep them interested at the end. This is also the reason why I chose pictures of palm trees and elephants; they are two very beloved and idyllic images. Contributing to the non-threatening but informative tone of the visualization, I chose colors found in nature, nothing too bold. In creating this, I compromised the full details for simplicity, but I found this acceptable because it is intended more for the younger demographic of the general public than the scientific community.

In constructing these four representations of the factsheet on global road fatalities comparing the numbers and the per capita rates, I attempted to show the information on a gradient. As my visualizations progress, they get less specific and more visually complex. The first had data points for 181 countries and while the map does effectively give a global perspective on the issue, the color schemes and visualization itself is somewhat lacking. The next visualization only has 23 countries, but keeps the two different determinants. It focuses more on comparing the data and drawing connections than anything else. The third further combines the data, this time into 6 different categories and forgoes one of the factors. It integrates pictures with the numerical data. The final visualization focuses solely on images, keeping text to a minimum. The text highlights the message rather than being the message. In each, I gave varying levels of information, using the aspects of the data sheet that could be manipulated to best represent the message and tone I wanted to create. I compiled, simplified, and minimized in order to best fit the mode and message of the visualization. While each visualization was made with the intent of demonstrating the gravity of this often ignored global health issue, I did not maliciously manipulate the data to only highlight one aspect and thus create a different reaction with each diagram. In doing this project however, I came to understand just how easily data can be spun in order to get a specific message across. Visualizing data is a very complex and powerful tool.


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