Recently we’ve been working on graphic visualizations using maps to show spatial history. One thing that was clearly emphasized in many of the recent readings is that a map is not just a picture. In Patricia Seed’s article, “A Map Is Not a Picture,” we discovered that maps are intended to mean something. Maps can convey a message about population size, spatial history, demographics, climate, and other information related to the given location. Thus, when a map is treated just like any other image by reproduction companies and is tampered with to increase aesthetic appeal before being published, the message can be lost. Digital mapping technology pointed out this problem upon its development, and so is often more accurate than any map that has been reproduced by a publishing company. Mapping tools are important and much more convenient for researchers who want to map the spatial history of a specific location, as it doesn’t take as much time to map specific datasets by hand.
To practice how these maps may relay information, we started off by mapping a dataset of the Cushman Collection. The Cushman Collection is a dataset of information about the photos taken by the photographer Cushman, and gives a variety of information about these photos, such as the genre, date, IU archives number, slide condition, as well as other pieces of information. We first entered the Cushman Collection into Google Fusion Tables. With this app, we were able to see what kind of information the dataset gave us, and create, in addition to a map, charts and graphs. The dataset gave us plenty of information, from the IU archives number to the date the photo was taken, from the photo’s slide condition to the genre of the photo itself.
With Google Fusion Tables‘ mapping feature, the IU archives number of each photo was automatically mapped using geocoordinates. This map indicates where each photo was taken, using the IU archives number. To get to this map, I uploaded the Cushman .csv file into the Google app, clicked on the “Map of City and State” feature, and made sure the location was set to geocoordinates. Unfortunately, there weren’t very many options for mapping, as it only mapped specific things that you couldn’t change (like the IU archives number).
Another mapping tool we used seemed to be more sophisticated than that of the Google Fusion Tables. In any case, it seemed a little more straight-forward when using it, and it was easier to map specific items from the Cushman dataset, as it had many more options to graph. In the map I created on Palladio, I was able to map the location by genre, rather than by the IU archive number. It looks pretty similar to the Google Fusion Tables’ map, but the Palladio map was much more fun to play with. Hovering the mouse over each dot on the map reveals the genre of the photo that was taken at that location, rather than the IU archives number. In order to get this map, it required a little more effort than the Google Fusion Tables, as the dataset itself had to be altered so the date could be correctly recorded on the mapping tool. But once the time zone was deleted from the dataset, the mapping feature wasn’t so complicated. the only downsides to Palladio was that the website didn’t save the maps, so you had to start all over again whenever you exited the page, something that wasn’t a problem when using Google Fusion Tables.
Altogether, both mapping tools were useful in revealing specific information about the dataset and the importance of each point on the map. Each mapping site has its own downfalls, as one has more mapping options than the other, as well as one site saving the data map whereas the other didn’t. However, both mapping sites serve to exemplify Patricia Seed’s point entirely, in the fact that, if the map was altered too much, it may change the original message. Maps are supposed to reveal information about a given object, place, or thing and its relationship with a given location.