In only a few short weeks, my fellowship project has undergone a series of substantial transformations. While the Fellowship course provided useful information about the inner-workings of digital humanities, having a chance to see my own project develop from start to (tentative) finish was an invaluable lesson about everything that goes into a work of digital scholarship.

The changes my project has undergone this summer are largely a result of my own naivety and my misguided belief that the mapping technology could accommodate imperfect datasets. Before submitting my proposal, I compiled a list of databases which tracked climate traced displacement throughout the 20th century. While these datasets came from a variety of sources, I hoped that I could combine them to form a single set of information which accurately depicted the climate diaspora. Instead, I discovered that datasets can rarely be melded together, and that ArcGIS and other mapping programs require an almost completely uniform set of data to create a comprehensible map. If there is one thing that I will take away from the fellowship experience, it is that a basic understanding of DH tools and programs is an absolute must before you even begin to formulate a project. With a better understanding of the capabilities of these programs, you can ensure that your chosen data can be presented in the way that you initially envisioned.

I am happy to report that through this fellowship, I now have a greater understanding of several digital skills, which I can use to inform any future projects. My greatest leap in understanding this summer took place through my continued use of QGIS. When my Fellowship partner Jay Bowen first showed me the program, it could not have looked more alien. But through continued practice, (and Jay’s bottomless pit of patience), I came to understand QGIS as a fairly user-friendly program that is adaptable to a variety of datasets. In using GIS, a scholar can generate migration paths, join csv data to vector layers, reproject, and convert data to a geojson format. From there, we were able to use Atom for scripting. Jay also showed me a brief tutorial on the program Leaflet, which we used as a Javascript library for building the interactive story map. Github will be used to host and publish the completed story map. We chose Github because it allows publishers to include short windows of background information to better explain the data and provide a human element to what would otherwise be dots on a map.

The digital storymap I created illustrates the immense migration that took place in the United States as a result of the 1930s Dust Bowl. My hope is to continue adding to this map or to create accompanying maps, which will eventually show the total number of climate migrations that occurred in the U.S. during the 20th century.

Lost in the conversation about the impact of global climate change, and policies designed to prevent rising temperatures and mitigate their consequences, is an understanding of the human impact of an increase in global temperature. I propose a comprehensive storymap that shows the migration of people throughout the world as a result of climate related events. This will illustrate the myriad of ways in which climate change has already impacted human populations, as well as the potential for rising global temperatures to cause the greatest migration of people in human history.

This project was conceived as a way to illustrate the mass movement of people as a result of man-made climate change. While there are dozens of storymaps which describe the impact of rising global temperatures, none of them give the viewer a sense of mass migration, of human populations in flux. One of the challenges I will face during this summer is organizing my datasets and creating my storymap in a way that adheres to the overarching principal of portraying movement.

One of the inspirations for this project is the storymap created and published by The Slave Voyages Consortium. This timelapse storymap uses orbs to represent enslaved people who were kidnapped and transported to other places as part of the Atlantic slave trade. The orbs grow in size to represent greater numbers of enslaved people being transported. The orbs are also color coded to illustrate the different destinations of the slave ships. As the timelapse progresses, we see the orbs moving across the Atlantic, representing the forced migration of Africans to the U.S., Caribbean, Brazil, and Europe. In this way, the storymap not only represents the loss of population that took place in Africa, it also shows the dispersal of the African diaspora that took place as a result of the slave trade.

Compare that storymap to the ones published by the Esri Storymaps team. This series of maps introduced viewers to the human impact of the climate crisis, but like other digital humanities projects focused on climate change, places the planet at the center of its narrative, not humans. Maps, like the one below, which do represent human populations impacted by climate change, give the impression that these populations are stagnant. My project will contribute to digital scholarship related to climate by change by placing movement at the center of my narrative and depicting the climate diaspora that has been growing as a result of global climate change.