When looking at a map, Mark Monmonier encourages users to be critical of the subjective influences that have shaped it, emphasizing the undue respect maps receive as compared to other data visualizations. Specifically, Monmonier states that “maps, like numbers, are often arcane images accorded undue respect and credibility,” reflecting his frustration with individuals’ lack of skepticism (Monmonier 3). Well, Monmonier may have been disappointed with my habit of obsessing over college scattergrams during my junior and senior years of high school.
For those who do not know, college scattergrams “are collected data points graphed to show the GPA and test scores of applicants to the college, indicating their accepted pool of students in a visual form” (McNamara). To be honest, I used to scour through these scattergrams for hours, switching from college to college on sites like Cappex , ultimately affecting my decision to apply to certain schools based on the probability that I would be accepted. I fell prey to those who designed these scattergrams, who stripped me of my identity beyond my GPA and test scores. Similarly, scattergrams do not capture the importance of other factors that determine college acceptance, such as extracurriculars, legacy status, college essay, teacher recommendations, etc.. Monmonier would also criticize how rejections are seen as red dots, while acceptances are seen as green, which “mislead the map viewer” into thinking they are an ‘other’ or not worthy enough to be a part of the green dots (Monmonier 3). Therefore, this “visual reduction” of a student negatively impacts their self worth by making it seem as though test scores and GPA are the most prized attributes colleges focus on (Manovich 38). In analyzing data visualizations, we should be mindful of how they only offer a limited and biased view of the bigger picture.
Additional Sources Used:
https://www.cappex.com/page/collegeCenter/scattergramStandAlone.jsp;jsessionid=EC6AE4ABF835738BEA2A1BC9B2F08D94.tomcat-main01?id=198385&collegeID=198385&collegeName=Davidson%20College&isForProfitCollege=false
http://threeforfreecollege.com/2013/09/20/the-truth-about-gpa-test-scores-and-scattergrams/
wibyrd 
maschimanski
Your critique of college scattergrams is a great extrapolation of the way Monmonier encourages us to be critical readers of maps. I’m interested in the signal-to-noise ration of such scattergrams: how much useful information do they convey versus non-essential or even confusing information?
I find your analysis of college scattergrams to be fascinating. While it is true that the scattergrams make grades and SAT scores seem like the most valuable characteristics of a student applying to college. An interesting point I would add is when you are analyzing these scattergrams you are looking at students that applied within the past 5 years. Meaning that these scattergrams assume that admission standards are static and don’t change substantially over 5 years. This assumption is not always true. For instance, when I was looking at Ohio State there standards had increased substantially over the five years so the information I was analyzing was irrelevant to my undergraduate admission. The graph poses an issue because students admitted 5 years ago are weighted equivalently to students admitted in the past year. A better graph would weigh recent admission statistics as more important when creating a visualization of this data. To build on this issue when you move from school to school you are not offered data on whether the admission standards have changed significantly in the past 5 years. Therefore you are looking at static maps that are attempting to represent dynamic data that changes from year to year. With this in mind, My decision and my classmates decision to apply to colleges based on these graphs were ill-founded.
While I do believe that these sorts of figures and graphs are at times misleading when making decisions such as college applications, I am going to reference what Dr. Sample just commented. Many of these figures have both signal and noise, that is, useful information and information that may not be applicable to the observer. At what point does the reader have an obligation to realize what is signal and what is noise in graphs like these? Yes, these figures may be misleading, but shouldn’t the reader have some sort of obligation in deciding what is useful and what is not? It is clearly stated, in the figure above, that this graph only takes into account a student’s GPA and ACT/SAT score. Shouldn’t the reader understand that those two variables are the only two being measured here, and that there may be more important variables that are not displayed in this graph? What responsibility does an observer have when it comes to looking at figures and taking the displayed figures with a grain of salt? Should the publisher be responsible for displaying misleading information, or should the observer be responsible for not realizing that information was not helpful to them? Is it all on one or the other? I believe that some of the burden lies with both the author of the data and the observer. The author has the obligation to list, in plain sight, the variables that they are using in their figures (which is what this author of the figure above does). The observer has the responsibility to also take any and all figures referenced with a grain of salt, meaning that one ought to seriously ponder the information given, and think about whether that information is really helpful to them (aka: ponder the signal-to-noise ratio of that particular figure).