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Penn Institute for Urban Research 2019–2020 Annual Report
Master of Urban Spatial Analytics (MUSA) Penn’s Master of Urban Spatial Analytics (MUSA) is a one-year graduate program that teaches students how to use spatial analysis and data science to address pressing issues in urban policy and planning. Penn IUR contributes to the MUSA program through convening its advisory board and hosting events that connect students to experts who are applying these methods to a variety of real-world problems. In order to complete the degree, students must complete a capstone project that applies spatial analysis to an urban content area. Examples of research projects students completed in Spring 2020 include: scooter equity and demand analysis in several American cities; optimizing social worker assignments in Guilford County, North Carolina; and predicting bus ridership in Austin, Texas.
SPOTLIGHT
MUSA Master Class on Rayshader Package On November 15, 2020, Tyler Morgan-Wall, SAS’09, led a workshop on the rayshader package, an open-source mapping tool he authored to create 2D and 3D data visualizations in the programming language R. Penn IUR co-sponsored the event, which was part of the MUSA Master Class series that brings together data scientists worldwide to learn from expert practitioners in data analytics. Participants joined the workshop at the Weitzman School of Design and through a live webcast. After a brief introduction by MUSA Director Ken Steif, Morgan-Wall explained that rayshader gives users a high degree of control and flexibility in map design, allowing them to create graphics directly from elevation data. Morgan-Wall said the tool improves data scientists’ ability to create realistic hill-shaded elevations, which are critical to a map’s legibility and power to communicate. Emphasizing the value of compelling graphics, Morgan-Wall pointed out that simply convincing experts is not sufficient for realizing public policy goals: the public also needs to understand the data and, for that, clear and cogent visualizations are essential. “Science and policy analysis that engages the public,” he said, “is infinitely more valuable than science and policy analysis that only engages people in your field.” After reviewing the fundamentals of 3D mapping in rayshader, MorganWall explained how to generate hill shading, demonstrating the use of variables such as time of day, time of year, and geographic context. He then walked participants through examples of data visualizations. Following his talk and demonstration, Morgan-Wall encouraged participants to replicate his analyses using open-source data he posted on GitHub, an online repository of code that facilitates collaborative software development. Participants’ work can be seen on social media using the hashtag #MUSAMasterClass. A video of the event can be found on the Penn IUR website .
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