Out of the datasets provided, I decided to work with the moose and wolf sightings in North America. However, in order to be able to manage the data more efficiently, I decided to only use the data on the wolf sightings. I did this by opening the original dataset in Excel and sifted through the data to delete all of the moose sighting data points. I ultimately decided to create a heat map using the GPS coordinates provided in the dataset. With that, I used ArcGIS to visualize the map; I did notice that on the lab computers, ArcGIS didn’t take the file layer because it was too large, yet when I did the same on my personal computer, it took the file without having to make a special layer as it had previously suggested. To better visualize the data, I first changed the base map to “National Geographic” (it would have been a crime if I hadn’t). I then changed the appearance of the heat spots by choosing a different color scheme for more of an eye-catching look and decreasing the transparency for better visibility. There is a wide array of information to be gained from using this map specifically and this type of map in general. Of course, it can be used to better understand the habitat and frequented areas of wolves, but it also can be used to determine how many wolves there are quantitatively, or it can be compared to similar maps of other species in order to learn about shared habitats, the ecosystem of areas, etc. Now overall, the key distinction between data science and DGAH is that in Digital Arts & Humanities, the emphasis is on storytelling, emotional engagement, and often a focus on humanistic questions about the interaction between nature and culture. Whereas data science is purely focused on analyzing the data itself. Yet, many projects blur the line between both.
