So for this blog post, I delved into two different digital map making tools in order to explore what kinds of things can be learned from maps: Palladio and Google Fusion Tables. The data set that acted as the centerpiece for my maps was the Cushman Collection, a spreadsheet archive of photographs from around the U.S, dating from 1938 to 1969 and taken by Charles W. Cushman. (The website where the photographs originally came from can be found here.) In creating different maps of this data, I was able to discover various things about the photographs, particularly some of the most recurrent locations where they were taken.
This map at the left was the first I made in Palladio. It’s rather difficult to see, but each pink dot on the map represents a place in the U.S. where a particular photograph – or a group of photographs – was taken. Obviously some areas have more condensed dots than others, like the West Coast. These dots act as markers of the places Cushman traveled and documented with his camera. Behind each little spot of pink is a bit of spacial history, specific to the year any given photograph was taken.
The second map I made in Palladio was a little different. I made a slight change to the way my lovely pink dots were displayed, adding the “size points” feature so that areas with more photographs documented had larger dots. This gives a much more dramatic visualization of how many pictures were taken in places like California, versus how few came from places like the Northern states. Here we’re provided with a means of comparing the spacial history that these photographs show, and we can easily see how much these pictures might tell us about certain parts of the country over others.
Now then, moving on to Google Fusion Tables. The first map I created with this tool was very similar to my first Palladio map. I wanted to see what a simple layout of picture locations might look like between the two different tools. Instead of dots here, I used little location markers, like the ones you would see on a Mapquest search, but the principle is the same. In this case, each marker on the map stands for an individual photo, rather than some acting as representations of many photos. Clicking on one marker would tell me exactly what photo it represented, and the metadata that went along with that photo. We can still see here that some locations in the U.S. have a much more dense collection of pictures than others, but the impact seems greater when Google Fusion Tables provides so many more markers than Palladio does.
Out of curiosity, I tried creating a different kind of map with Google Fusion Tables than my previous three. It’s called a “heat map”, and from what I gather, it’s supposed to represent the densest parts of a data set on the map. Again, the image I took of my map is rather small, but there’s a very obvious red and yellow circle around the North Eastern part of the country, meaning that a vast majority of the Cushman photographs probably come from there. Other places on the map have lighter green splotches on them, showing that there are indeed many photographs from there, but not nearly the amount as are in that big circle. While this map does give a better idea of the hot spots in the U.S. for these photographs, it does a disservice to the data as a whole, because it leaves out locations that didn’t have enough photos to be included. The Northern states and Texas aren’t included at all here, when it was clear that there were photos from these places on the other maps. So this kind of map eliminates some of the important spacial history to these photographs that the other maps represent more accurately.
Observing these different maps, and the perks and drawbacks to each, reminds me of the article we read for class by Patricia Seed. In her writing, she drives home the point that maps are more than just pictures, they’re visualizations for conveying meaning. That comes from the spacial history of maps – from being able to see things that have occurred in the past, connections between different parts of the world, and pretty much anything else under the sun. To treat maps as mere pictures is to lose the most important element of them. But in order to convey the appropriate meaning, a map has to be suited for the job. Some maps, like the heat map I showed above, or maps that have been tampered with upon going digital, don’t display the spacial history of their data like they’re supposed to. And that can sometimes leave out the most important details of the data. Maps must be made and treated with respect and care, otherwise the stories they tell may be lost.