UAE Map Rectification and Comparison: A Snapshot from Year 1 to Year 45

In honor of GIS Day, a.k.a. Geographic Information Systems Day, and, to some extent, the UAE’s 45th National Day, our Digital Humanities mini-tutorial of the week at the NYUAD Center of Digital Scholarship was on map rectification or geo-referencing using old Soviet maps and current satellite images. The process involved creating shape files of the satellite images, putting them into a program, and matching coordinates or features together. Further explanations are provided by our professor, David Wrisley.

Complicated technical details aside, on a philosophical level, GIS Day was an interesting exercise in transparency and definitions of reality. Often, particularly growing up in America, maps seem incontrovertible in terms of their veracity: bodies of water are defined; borders are established. While these may just be lines drawn on a map when you’re wandering through the woods, the borders seem very clear and delineated when a sign along the highway welcomes you to a new state at a specific point. However, as proven by America’s relentless debates on borders and immigration, as well as Africa’s and the Middle East’s colonization, borders are often far from distinct, incontrovertible separations of states and nations.

In regards to the Soviet maps specifically, the choice of map accuracy is illuminating and peculiar. As outlined in the article, Hyper Detailed Soviet Maps of Washington, Soviet precision was remarkable as pertained to government buildings and vital infrastructure, but haphazard when charting residential areas. Before this exercise, I had never assumed that maps would be “filled in” with such carelessness. I imagined maps either as fully researched entities or transparent charts that would say, circle an area lacking detail and simply note “residential area,” instead of providing approximated or fictionalized houses. In our own project, you could see distinct differences in the water borders. Any number of things could have caused this difference: changes in the tide, long-term environmental changes, inaccuracies in the Soviet map, etc. Thus, unfortunately, further information, from the site itself or other maps, is required to determine the actual root of the discrepancy.


Consequently, the project was simultaneously encouraging and discouraging. The subjective decisions taken in the map-making process allows greater analysis in terms of the mapmakers’ motivation. However, this step also necessitates prior knowledge of the area due to the opaque nature of maps. In the Soviet Union, where access to maps of Washington, DC. was likely limited, there’s little possibility for the readers of the map to know of its discrepancies with reality. Map scaling and “bucketing” of certain information only exacerbates these problems of precision as it draws away from the specific data and into a simpler representation. Of course, whether the specific data is necessary will vary based on the map’s goals and subjects; however, while university teaches these ideas, it’d be nice to see more dissemination of these ideas of GIS and subjectivity in high schools. Such a change in the general curriculum would ensure greater awareness and critical thinking skills among the general populace, of whom some cannot afford higher education, and likely foster greater interest in the oft-overlooked field of geography in American classrooms.

Networking with Nodegoat

Our recent project networking with Nodegoat evidenced how quickly  webs of information can become complicated. For instance, the network of films, their directors, and relevant actors, writers, etc. quickly became a bit muddled in our Egyptian cinema project. The names of films and people overlap; the exact connections between that one large mass on the left and its branches blur. Although the outline colours can be charged, there is no clear-cut way to choose one colour for one type and another colour for another type, facilitating easy visualization. These have to be established through “conditions” within the design of the data, instead of, for instance, in the visualization panel.

Meanwhile, devoid of spatial data, the geographic representation of the project is currently meaningless, as seen here:


Conversely, other projects are not conducive to social representations, but work fairly well with geographical representations. For instance, this image presents a preliminary work of Sudanese authors and their “migration,” from birthplace to university to current residence (that will hopefully include lines representing their migration patterns in the future).

sudan-prelim-authorsAlthough this map does not yet express the distinction between universities, birthplaces and cities of current residence, the points are revelatory simply by representing where the current Sudanese literary “elites” have lived at some point over the courses of their lifetimes. However, it’s likely that even this map won’t present the whole picture when complete, principally because I have left out childhood statistics. Due to the relative difficulty of ascertaining where all these authors went to primary school or in what specific city they lived, I have decided not to include this data at all. Similar phenomena would have occurred even in the overly complicated network of Egyptian cinema shown in the first image. A young actor or director may have played a minor role in one of the included films before becoming a star and taking on a role large enough to constitute Person 1, 2 or 3. Therefore, there would be a link between that person and a film that wouldn’t be documented in the network.

Ultimately, Nodegoat tries and overall succeeds at simplifying the process of creating networks and relationships. However, the very nature of these networks often makes them inherently complex, with countless executive decisions to condense and present the information in an easily digestible way.