Imagine that you’ve been given a map of the entire rail system in India with the assignment to determine where to place grain silos in order to maximize utility and minimize transportation costs. Or perhaps you’ve been tasked with determining how many coast guard helicopters are needed to monitor the Hawaiian coastline and where they should be located. How would you go about solving such problems?
These are the kinds of policy challenges tackled in Professor Solomon Hsiang’s Spatial Data and Analysis class, which trains students to “think spatially” by “engaging with spatial data...intuitively and creatively.”
“Whether its disaster response, public health, environmental management or public finance... a lot of the policy puzzles that we deal with have a spatial component that’s often overlooked or underplayed,” says Terin Mayer, who took the course last spring. “This course confronts the fact that space matters, and gives us a set of tools to think rigorously about that fact.”
Throughout the world and across disciplines, researchers are using the enormous amounts of spatial data generated by everything from satellite imaging to crime statistics to conduct innovative new research. Much of Professor Hsiang’s own research – on the economic cost of large weather events, for example – is in this vein. But as far teaching students how to use spatial data analysis to tackle public policy problems, this class is unique. As such, this class not only draws Goldman School students, but graduate students from ecology, law, public health, social welfare, economics, political science, geography and the School of Information.
The class “encompassed everything I wanted to do in graduate school,” says Lisa Quan. “The content was conceptually advanced; we coded in Matlab, which is one of the more difficult languages to learn, and we learned how to make that leap from conceptual understanding to successful programming of the techniques.”
“The more I explored political and environmental phenomena at a really granular level, the more I realized how little had been done to work with data in this way,” adds Terin. “ It was one of the first recognitions of the fact that I could be on the frontiers of human understanding just by combining information in novel ways.”
Along with lectures, readings and labs, students must complete a final spatial data analysis project. Elizabeth Leuin analyzed optimum beehive placement in almond farms to reduce farmer time and spending on field pollination. She created a complex model that simulates bee pollinating behavior. This included separate algorithms for "scout" and "worker" bees based on honeybee colony behavior literature. She then ran a series of simulations to determine optimal hive placement.
Terin applied similar techniques to his final project, evaluating investments in grassroots organizing for Minnesota’s 32 watershed districts, while Lisa looked at Crime patterns on BART.
“The class cemented my commitment to learning programming skills and advanced data collection techniques to solve policy problems,” says Lisa. “My background is in criminal justice and there has been a long-time push for using spatial techniques in the field. It is definitely applicable in urban policy in general.”
Elizabeth agrees, adding that she wants to incorporate spatial research and analysis into her future work in environmental and agricultural policy. “There's no better way to learn to code than to be thrown into the deep-end with a complex policy problem,” she says. “This was one of the most challenging classes I have taken at GSPP and as a result, one of the most rewarding.”
“In lab assignments every week we had to take a set of formal mathematical concepts explained on the blackboard and figure out how to translate those into algorithms that a computer can understand,” adds Terin. “ Building this kind of skill is a key part of unlocking the potential of computing power to transform how research is done, and it totally got me hooked.”