Vaseer Grayscale

By Gagan Vaseer • July 2, 2019

How do you improve the streets so children can walk to school safely?

What are tangible ways in which cities can improve the health of newborns?

What can the movement patterns of refugees tell us about how governments should allocate resources?

Questions like these are top of mind for policymakers around the world yet are surprisingly challenging to answer. While data exists for these and countless other policy challenges, a typical analysis of the data might allow a city to determine which school zone has the most traffic collisions but may fail to show broader nuances essential for meaningful and effective intervention. Analyzing data solely on a spreadsheet limits one from seeing how it interacts with the realities of our physical world, where there are often other factors influencing the outcome.

Here at Harvard Kennedy School the curriculum endeavors to prepare future public servants to tackle such challenges. I was the Course Assistant this year for “Urban Innovation: Using Technology to Drive Change” taught by Professor Stephen Goldsmith, director of Data-Smart City Solutions. A core theme of the class was how location intelligence and the layering of variables could lead to pattern recognition and, in turn, innovation. In an effort to drive towards a more holistic analysis and understanding of public challenges for students in the class, I worked with international and domestic classmates on exercises designed to help them think more deeply about the questions above using the tools of data and technology.

The theory of the course involved challenging students to address problems by going beyond the acceptance of existing approaches and assumption to consider cross-agency and cross-sector approaches to problems. As one of the prime goals was centered on how digital tools could enhance inquiry and improve the application of public discretion, the teaching team planned an exercise that would both use place-based visualizations as an example of these lessons and as a path to innovative, data-driven solutions.

We provided students with access to policy mapping tools and asked them to utilize mapping and spatial analytics to visualize layered data in order to uncover the insights necessary for innovation. The class was comprised of students from an array of backgrounds, from those studying public policy to urban design to education. Modern mapping tools enable even those without technical backgrounds to map and examine data; the class used Esri’s ArcGIS platform, a comprehensive data mapping and analysis tool.

Equipped with the Esri software and a purposefully broad problem statement, students were on their way to analyzing one of the questions above or a policy issue of their interest. By encouraging students to pick a topic and location that was most aligned to their interests, the hope was they would have a more personal investment in the analyses and, in turn, arrive at more innovative solutions. And although a majority of the students had never used a mapping or visualization tool before, they quickly understood the distinct capabilities of the platform while discovering that the mapping task allowed them to analyze problems with a new perspective.

The students not only developed analyses and memos but also showed a thoughtful reflection on the multidimensional nature of policy challenges and innovations that had previously not been considered in similar contexts.

Through geospatial data mapping, a group was able to, for example, layer school zones, collision types, collision locations, and number of collisions to determine the root causes of accidents (such as issues with traffic signals or improper turning) in Pasadena, California. As mapping allowed them to see that accidents most often occurred at intersections near schools, students were able to pinpoint exact locations where interventions were needed. From simple solutions such as the placement of additional signage and the inclusion of 3D crosswalks at specific intersections to more technical ones like pedestrian and speed sensors, students offered a variety of concrete suggestion that would make the streets safer for school children.

The mapping exercised opened up even more inventive proposals concerning improving newborn health. One team that looked into birthweights in Kentucky mapped population density and median incomes across ethnicity, age, and education parameters. When coupled with a literature review on Kentucky’s health challenges, the findings showed that those in Eastern Kentucky, African Americans, and individuals with limited educational attainment had the highest prevalence of low birthweights. The team proposed solutions like the creation of a cross-functional team that would integrate representatives across an array of health and human services offices, as well as economic development programs in low-income neighborhoods. Another team that looked into the same topic in Florida took a different approach to the task, mapping birth weights and mortality, the uninsured rate, and quality of public transit. As the team discovered that access to transportation served as a barrier for those in need of maternal and infant care, they proposed creating mobile maternity and infant care units to fill this need–an innovative solution to a serious problem.

Other students used maps and geographic information to propose how to address development in Guinea, enable governments to anticipate refugee demand, and improve coordination of refugee integration. By utilizing the wealth of data and analysis available in the GIS toolkit, students were able to propose policy solutions that were both grounded in reality and innovative in thought and approach.

Ultimately, this activity and the accompanying analyses showed us three things:

  1. A course that teaches technology-driven innovation should offer technology as part of pedagogy. Today’s policymakers need to be equipped with the skills to leverage technology to solve public problems, so it is important to consider technology as part of a public policy education. Although this was the first time many had used geospatial analysis, students were quick to recognize its power in helping analyze the data in hand.
  2. Mapping and visualizations allow for a multidimensional understanding. Once students were able to see their data outside of spreadsheets and on maps, they could evaluate new patterns and relationships and understand the problem in a more nuanced manner.
  3. Layered variables produce breakthroughs. Students were able to find unique insights and come to novel solutions because they found patterns across new variables. Innovative solutions require a fresh way of thinking about the problem.