Week 5 Blog

For this assignment, I georectified a historical map of the United States, and the process was a lot more meticulous than I thought. It has strengthened my understanding of spatial DH projects I looked at earlier in the term. I realized spatial DH visualizations aren’t purely objective but depend on the choices I made. For example, I had to decide which cities were going to be my anchor points on the georeferenced map. These points needed to be evenly distributed across the country and accurately placed on both maps. This step was vital because choosing these points directly affected the accuracy of the final map, which I found out real quick. 

After saving the map, I was able to access multiple formats through the map page, such as a discover mode that allows you to compare across time, a compare mode that makes the overlaid map transparent, a 3D mode, and a document mode that shows the rest of the original historical map I georectified. These options open many alternatives for DH research. You can compare how a place changes over time, internally and with its borders, and identify important moments in history specific to locations. Population counts or more statistical data on anchor point cities could be added to improve the quality. 

With this comes some limitations to georeferencing that hold it back. Some historical maps can be inaccurate at times, and the precision of georectifying can result in a lot of distortion, leading to more inaccuracy. This makes it an invalid source to support research projects involving exact measurements or the precision of geographic accuracy. Georectified maps should be treated similarly to generative AI and LLMs: they are great for interpretation as long as human interpretation is still present. 

In summary, this assignment demonstrated that georectification and spatial DH visualizations in general involve a lot of skill and interpretation as well as methodical work to properly and accurately represent spatial data.