This week, I examined the 19th Century Caribbean Cholera Timemap created by Duke University Haiti Laboratory. The interactive program offers both a timeline and a map, while locating all documented cases of Cholera outbreak, hurricanes, tropical storms, and news coverage in the Caribbean. As demonstrated by the timeline, the Timemap features 3 major periods of cholera outbreaks (1833-1834, 1850-1856, 1865-1872). It includes the location relative to other outbreaks and the corresponding primary media sources. Clicking on each event will make the map display a short description of the outbreak, storm, or news article.
I believe that this map reflects a scholarly (statistician or epidemiologist) , point of view of cholera outbreaks. As the news articles reported by mainstream media at the time, the map only illustrates the mathematical data of people affected by cholera, instead of mentioning the life and suffering of the individuals. Such a focus distinguishes the map from a traditional research in the humanities: it excels at factual representation of deaths resulted from the Cholera outbreaks and the climate conditions; however, it fails to measure the data in a historical and humanities context.
The creators of this map assume that all Cholera outbreaks are recorded and mostly published. Yet, lack of documentation does not equal to nonexistence. For casual viewers, this map seemed to be a complete collection of every hurricane, outbreak, and storm that took place. However, the chronological and geographical attribute of this map make obtaining and maintaining a comprehensive record nearly impossible. The display of the map also hides valuable details about the Cholera outbreaks, such as their cause, carrier, and severity. Furthermore, the mapping team attempts to relate Cholera outbreaks across the Caribbean to a single variable: climate. By labeling the amount of outbreaks and stormy weathers as the only pair of value, a correlation in between becomes obvious. But in this way, potential correlations between Cholera and other variables are easily overseen.
To make an alternate map, I would first narrow down the scale of the map to focus on the Caribbean region, since the original design of a global map was a waste of resources and functionality. Instead, I would leave more space to show the details of each location and outbreak. Secondly, the model of display can be improved by designing independent icons to represent each of the 4 data types, so that events such as storms and Cholera outbreaks can be distinguished from each other. Also, offering an option to display a heat map can lead to a better visualization than the original scatter plot. Moreover, if records like immigration patterns or economical status across the region are available, I would introduce them into the map as well to show other potential factors of Cholera outbreaks. Last but not least, there seems to be a minor bug where viewers can select timelines even prior to year 1833, although there is no information available back then.
I think you make an interesting point in that the global map wastes resources and functionality, since the project focuses on the Caribbean region. I had the opposite issue with the reVilna project – the map zoomed in on the Vilnius Ghetto so closely that, while the website specified that the Ghetto was in Lithuania, it was difficult for me to see this when I looked at the map. Because I was unable to zoom out and view the surrounding areas, I had difficulty understanding exactly which area I was viewing and struggled to situate the data. I think our experiences reveal the importance of scope in a mapping project. While displaying too large of an area can prevent the viewer from focusing on certain locations, displaying certain locations in too much detail can prevent the viewer from locating the data in a larger context.
Great analysis! Your alternate map sounds really interesting, and it would be cool to see what other factors come into play with Cholera outbreaks. Another interesting thing to see would be what types of neighborhoods these outbreaks concentrated in, and whether or not overall cleanliness plays a big part in the outbreaks and the severity of them.
Your analysis is very thorough in that you recognize the intended purpose/functions of the current map as well as offer valuable suggestions for how you would revise it. Although factual representation of deaths from Cholera outbreaks and climate conditions serves an aspect of the project, the topic has so much more potential and using the right functions as you mentioned would not only improve informatively, but also with clarity.
I really liked how you highlighted that the focus of this visualization leaned more towards a factualization of cholera outbreaks compared to a more humanities-based focus which might include details like the actual living conditions of the individuals who suffered with the disease. I also liked how you pointed out the initial global view of the map seemed like a waste of space and that your suggestion of focusing in on the Caribbean seemed to make a lot of sense more personally at least.
Interesting analysis! I enjoyed reading your take on an “alternate map” to improve what should have been included in the source itself. This post was particularly interesting because you really highlighted what the project should have devoted time on, and what the project wasted time on, and how if the team allocated resources correctly, they could have come away with a much stronger visualization. It’s important to recognize things such as this when creating constructing our own final projects, so not only that time is utilized efficiently, but that the project transcends a greater definition of meaning and utility.
Your detailed comments on what an alternate map could look like really shows that you carefully examined and analyzed the project. I had the same problem in my data set with the idea that a lack of documentation = nonexistence, and I agree that this is troubling when trying to create complete narratives, especially for people from differing backgrounds. I like your suggestion of using a heat map – I think that would really aid in furthering the meaning of this data!