It’s hard to believe that two months have passed since the inaugural Association of Research Libraries’ (ARL) Digital Scholarship Institute hosted at Boston College. In the previous post, you can read Sarah Melton’s overview of the goals of the Institute, and takeaways from the keynote by Jennifer Vinopal, Associate Director for Information Technology at The Ohio State University Libraries, and an opening workshop with Alex Gil, Digital Scholarship Coordinator at Columbia University Libraries. The ARL Digital Scholarship Institute was developed by a group of individuals from five institutions brought together by ARL in October 2016 to support one of the primary goals of the ARL Academy to “foster the development of an agile, diverse and highly-motivated workforce as well as the inspiring leadership necessary to meet present and future challenges.”
The sessions offered during the Institute focused on areas of digital curation, metadata, spatial and temporal visualization, text analysis, and scholarly editions and were taught over the course of three days. As one of the instructors for the Institute, I developed a session called “Geospatial and Temporal Mapping with Carto.” The participants of the Institute were library professionals who are new to digital scholarship so it was important that we set achievable and realistic learning goals, which would equip participants not only with a better understanding of what digital scholarship can be, but also with how they can apply their existing skills. For the Geospatial and Temporal Mapping session, I created a series of exercises that would help participants better understand what kind of data can be mapped, why they might want to create a spatial visualization, and the type of formats and terminology associated with spatial data and visualization. Since the Institute was in the Boston area, I identified datasets from Analyze Boston, the City of Boston’s open data hub, which would enable us to visualize points and polygons using Carto, a visualization and analysis tool.
All of the datasets that we used can be found on the lesson page under “Datasets” in GitHub. The goal of the exercises was to create a map with several layers which would include historic landmarks in Boston within designated historic districts and all of the buildings in Boston. We would explore what the current sea-level elevation is for each of the buildings and then add a layer to visualize the impact of the proposed rise in sea-level over the next 100 years in the City of Boston. I won’t recreate the full exercises in this blog post, but rather highlight some of the key elements and visualization output per exercise. You can view all of the exercises in the GitHub repository.
In Exercise 1, we mapped all of the buildings in Boston using the Buildings.csv data file. Initially, these buildings were points, but some of us decided to be a bit fancier and used a house icon within the “Style” menu of the dataset.
In Exercise 2, we spent time reviewing the Boston Landmarks Commission Landmarks dataset in Google Sheets where we normalized all of the dates so they all appeared in the YYYY-MM-DD format. We also geocoded all of the addresses using Geocode by Awesome Table. As part of this exercise, we chose to visualize the landmarks data as points (using the CSV dataset), but these could have been visualized used the polygon data in order to more accurately represent larger segments of land or water, such as the chain of parks and waterways, known as the Emerald Necklace or the Charles River Esplanade. The landmarks are represented with the gold monuments on the map.
The next step was to add a layer of polygon data using Shapefiles in Exercise 3. For this we downloaded the Boston Landmarks Commission Historic Districts and the Sea Level Rise Plus 5 Feet datasets. First, we added the historic districts data and styled the polygons so that each district had a unique color. The other layers are hidden so that only the historic districts can be seen. Once we styled the polygons and added a legend, we uploaded the Sea Level Rise dataset.
In this session, we also explored adding widgets and analysis tools in Exercise 4, including categories and histograms to enable some interaction with the map and data, which allow visitors to adjust the visualization and focus on a specific historic district, building height, and elevation at sea level. An example of the full map can be found on my Carto page. Turning on all the layers, the visualization below depicts the impact of sea level rise over a 100 year period will have on the City of Boston.