What Dr. Sample Said

My observing this week consisted of tracking the number of times Dr. Sample used some particular words, related to our overall idea of “Data Culture”, while he was talking to the class. I was thinking about also tracking frequently used words that people said for people who answered questions in class, but I forgot to count a couple on Tuesday’s class starting out, so I decided to abandon that idea. It was surprisingly easy to forget to count particular words that Dr. Sample spoke to the class so I may be off by one or two words, and also because a few words I started counting when Dr. Sample said them two or three times, though I think I did a good time of remembering how many he said before I started counting.

The particular words I chose to count were related to topics in our class, and specifically for the topics we talked about in class on Tuesday and Thursday. I wrote down some words before class that would be relevant to count, such as Data, Visualization, Information, Powerpoint, Maps, etc., but then added some more as I noticed Dr. Sample used them frequently in class. Here are the two tables for Tuesday and Thursday.

Tuesday’s class

Word Number of times Dr. Sample said the specific word
Map(s) 75
Data 3
Visualization 6
Cartography/Cartographer 2
Information 6
Lie/Lying 8
Timelines 2
Graphically 1
Scale 8

 

Thursday’s class

Word Number of times Dr. Sample said the specific word
Data 10
Network 3
Visualization 13
Information 9
Powerpoint 9
Timeline 3
Maps 11
Timechart 1
History 13

A couple of patterns stand out. First off, the number of times Dr. Sample said map or maps on Tuesday’s class was sizable compared to other words he said in class on both days. On Tuesday, we focused on maps for almost the entire class, and the map exercise where we had to draw maps from Studio D to Nummit, the Davidson Pizza Co, and Chipotle, was one part of class where Dr. Sample used “map(s)” frequently while speaking. “Visualization(s)” was a common word across both class, as well as “information”, which makes sense since they are relevant to our broad idea in class, talking about data. Also, Dr. Sample spoke the word “history” frequently in class on Thursday, since we spent a part of class on the Chronozoom where “history,” such as human history, featured prominently, and Powerpoint,  because we read the article about Powerpoint and discussed Powerpoint towards the end of class.

Overall, counting particular words that Dr. Sample said in class is interesting because it can display the length of discussion we spent on topics for the most part. On Tuesday, almost the entire class we discussed maps, and therefore Dr. Sample used “map(s)” frequently. The words do not give any context behind the discussion, for example, words such as information, data, visualization(s), scale, etc., can be utilized in a variety of discussions, but they give a decent picture overall of the topic of discussion.  Words were frequently used together with one another, such as “data visualization” or “data bars,” but I decided to keep the words separate to make the collection of data easier and more clear.

Focus as a Function of Time

observingcharts-1

observingcharts-2

In class today and Tuesday, I attempted to plot the overall class attention in relation to time, using a variety of indicators. Every 10 minutes, I recorded the number of students who were making eye contact with Dr. Sample, or with the student speaking, and the number of students making eye contact with either their computer or the class screen.

I quickly realized that these were poor groupings, with the potential to limit the data, as some of the readings discussed. I may have imposed my own demonstrations of attention on the entire class, who may have various learning styles. Further the data may be tainted due to both the non-traditional nature of the class, in which we are often working in groups or working alone, or if a person is staring into space in Dr. Sample’s direction, or staring at their phone. It further does not take into account people leaving, having their own conversations or falling asleep at their desks, which could be other indicators of attention in class.

That being said, it is clear from the graph from today’s class when we are working on activities involving screens, 1:50, 2:20 and 2:40. Focus also appears to be more inconsistent on Thursdays, as would be expected. It is more continuous, and decreases with time on Tuesday.

Inaccuracy of Maps

In class the other day we were told to draw maps around campus to other places that we may or may not have known the location. After thinking about the drawings that were made and talking about how different maps could be inaccurate. I wanted to see what different ways in which a map could be inaccurate. So as I was looking on the internet I came across a study done by Colorado University at Boulder looking at the different types of inaccuracies that can be found in maps. The article, “Error, Accuracy, and Precision” talks about “the problems caused by error, inaccuracy, and imprecision in spatial datasets.” Throughout the article it talks about the different inaccuracies. The different types that they bring up are format of the data, age of the data, relevance, density of observations, map scales which all deal with the characteristics of the map itself. It also goes over inaccuracies made through general error such as numerical error and analyzing the data gathered. It was interesting to see the different types of inaccuracies, and it goes with the notion that was brought up in class stating that all maps have errors and are inaccurate.

Questions are constantly brought up about how to make maps more accurate. The article raises some of these questions. How accurate are positional and attribute features? What projection, coordinate system, and datum were used in maps? These are just two of the many questions that can be asked on how to make maps more accurate. What can cartographers do to make sure their maps are as accurate as possible? What are the most important aspects of a map that need to be the most accurate in order for amble data to be taken from it?

 

A Mercator Mapping Mystery

After our discussion Tuesday, I began to do some research on my own. While searching for some interactive inaccurate maps, I found a blog that boldly declared to me that every map I had ever seen was radically wrong. The blogger began to explain that one of his co-workers mentioned a thing to him called the Mercator project. Being unfamiliar with the term, as was I, the blogger browsed the term “Mercator.”

As Cam Bard previously noted during his discussion, it is impossible to represent a three dimensional world on a two dimensional scale. Apparently, a cartographer from the 1500s named Gerard Mercator created the ovular map that we recognize today. In an attempt to preserve the shapes of the world’s countries, Mercator drew the countries as we commonly see them on the map. However, he did not account for the scaling with relation to the Equator. On most maps, countries and landforms appear larger and larger as they are placed farther from Equator. The blogger’s primary example was Greenland. The point was made that Greenland is roughly 60% smaller than we believe it to be according to modern maps.

A Mercator Puzzle was created to show how warped depictions are on most maps. It was quite intriguing to find that, according to the Mercator Puzzle, Greenland was roughly the size of the Democratic Republic of Congo (an average-sized country in the middle of Africa). The thing that stuck me after investigating the Mercator mystery was, how can the people who designed the Mercator Puzzle be so sure that their map predictions are accurate??

The Role of the Cartographer and the daily map user

In response to cabard’s post, I believe that he has a very good logical standpoint on who gets to decide what goes on maps, but I feel like he downplays the role of the user. He states that it is up to cartographer to decide what goes on maps, and how truthful that information is. I believe that while the cartographer does have this power, those who utilize the services have power as well. If a particular person wishes to have something on a map, then they should be able to suggest this to the cartographer. Since this information is for the users, then the users should be the people kept in mind when creating maps. These people can band together to get a say in what they believe to be most important for representation purposes.  Regarding the point made about getting the bigger picture but losing the details, I believe that this does not always have to be true. While the little bits of information can be lost, this can be prevented if we decided what the little bits information are. If a map wishes to have information regarding the local area, then the little information will not be relevant as the overall purpose  is to graph a small area. Once again, the user should decide what the purpose is, so if they wish to have a big area mapped then they must be conscious that certain details will be left out. If they want small, details should not be left out. Finally, I agree with the point that we can have either distances or shapes, but i do not believe that this will be a problem for much longer. With the increasing amount of technology in our surroundings, soon we will be able to bridge the gap that has been established. It would be crazy to think that this issue regarding maps will be a problem in the future.

Scaling: How Map Makers Make Mistakes

In the discussion following Tuesday’s map making exercise, which required us to create three maps leading from Studio D to Nummit, Davidson Pizza Company, and Chipotle, a number of people mentioned how they had difficulty managing the scale of their maps. I encountered this scaling issue when I ran out of space while attempting to draw the path from Studio D to Nummit. Despite the distance between the two points amounting to only a few hundred yards and the fact that I make this walk on a daily basis, the other two maps that I drew were far more accurate. While I am clearly a terrible cartographer, I believe that my familiarity with the path from Studio D to Nummit actually hindered my ability to successfully depict the route. Because I know the route so well, I found myself trying to add every little detail to the map that I fit, and as a result, I had to drastically alter the scale on the map to fit Nummit on the page. Conversely, when drawing the other two maps, I had far better spacing and was able to fairly accurately show how to get from one place to the other. I believe this was the case as I was not as concerned with drawing every minute detail, but instead I focused more showing the route from point A to point B.

While others may not have had similar experiences drawing their maps, mine left me asking if too much information can be a detriment when constructing a map. Ultimately, this question relates to Monmonier’s article and highlights the tradeoff between accuracy and usefulness that all map makers face. I found through this exercise that the more information one tries to incorporate into a map, more difficult it becomes to use the map. On the other hand, too little information appearing on a map is potentially dangerous as the omission of an important detail could render a map completely ineffective.

Regarding Maps and Other Visualizations

In How to Lie With Maps, Mark Monmolier shows us how something we often take for granted, maps, are often designed to purposely persuade us and not to just represent information. Reading through Monmollier’s summary reveals the different ways map data can be construed, and although this deception proves interesting, I personally thought more about data visualization as a whole–and its inherent limitations. Monmollier, for example, discusses how maps must represent either distances or shapes correctly because both cannot be achieved at the same time. The problem stems from trying to represent a 3-dimensional object on a 2-dimensional plane; in the vast majority of cases, loss of information is guaranteed. So, we have to make decisions when mapping data regarding what is important enough to include and what isn’t. This goes, of course, for every attempt to represent data visually, making processes like cartography very interesting. If the point were to be as absolutely accurate as possible, some people probably would not be able to understand what’s going on in the visualization, effectively being shut out. It’s like the oft-quoted difference between OSX and Windows. Make the data too accessible, and parts of it are lost in the simplicity. That’s the trade-off. Mapping data allows us to see the big picture of raw data quickly and clearly without trudging through lines of code, but it misses a lot. So, who decides what data is important and what isn’t? In the case of maps, that’s completely up to the cartographer, who will be hopefully be as truthful as possible; it’s too much of a chore for us to sort through data to make sure it’s all there.

The Issue of Authoritative Maps

In Cartographies of Time, Daniel Rosenberg and Anthony Grafton illustrate how easy visualization of data can facilitate a deeper understanding of its meaning.  They take care to introduce and detail multiple different different data recording systems from the last several centuries, explaining the nuances and distinctions among each of them.  The evolution of these charts helps illustrate the relative importance of chronology as a field of study.  It seems, however, that while these charts are immensely detailed and intricate, today’s charts are considerably less so.  A timeline is no longer a revolutionary new method of visualization but more likely a homework assignment for a second grader.  These charts have grown simpler over time, resulting in the standard line graph, something that was previously innovative but is now mundane.  This lack of detail and intricacy points to an argument that Mark Monmonier makes in his book How to Lie with Maps, that exclusion of data and distortion of reality in a chart is necessary to get the most important points across.

A message that both books present is that simplicity is an important aspect of data visualization.  Clarity is key.  Unfortunately, the act of simplifying allows for the possibility of manipulation, which Monmonier details in his accounts of political propaganda maps. Monmonier alludes to the point that simplification of maps is dangerous because people trust them.  Maps are often awarded more authority than they deserve.  Take, for example, the following map:

bbc-chinas-territorial-waters

This map means a lot of different things to a lot of different people, and is a frequent source of international tensions.  The 9 dashed line (China’s claimed territory in the South China Sea, in red) was originally published in 1947 by the Koumintang, not the Chinese Communist Party.  In 1949 when the Koumintang fled to Taiwan and the CCP founded the People’s Republic of China, the CCP conveniently adopted this map as their own despite it having been created by the Koumintang, who also maintain claims to the area.  The CCP continues to insist upon their right to claim the entire South China Sea to this day, despite the fact that this violates mutually agreed upon international law.  Some might find this map to be completely legitimate while others find it outrageous.  It stands as an example of how lines drawn on a picture of the earth can represent something so incredibly significant as national sovereignty, and how oversimplification of maps can have potentially dangerous ramifications, especially when those maps carry authority.