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The University of Delaware launched its Data Science Institute on Sept. 4, 2018, on what was described by UD President Dennis Assanis as, "a momentous day for the entire University of Delaware."
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Urban scientist and astrophysicist Greg Dobler was part of the cluster hire that fueled the establishment of the Data Science Institute, and he has spent the last year acclimating to UD and his home in the Biden School.
From Dobler's perspective, researchers can learn a lot about urban environments in the same way that the universe is studiedthrough passive observation. Astronomers create a wealth of knowledge about the workings of systems in the universe by taking photos and time lapse video and then analyzing what the images depict, and the same methods can be applied to the study of cities.
"Because we can't travel to distant astronomical objects, the majority of our understanding of the universe comes from analyzing pictures of the sky and trying to figure out how the universe works just by looking at it," said Dobler.
"After spending a decade in astronomy, I wanted to put those same techniques to use on problems that had more real world impact on people's lives. I wanted find out if we could figure out how urban systems work just by looking at them and if we could use that information to inform policy and decision making in ways that improve city operations and quality of life of city inhabitants."
Dobler spent five years in New York City developing an "Urban Observatory" (UO) facility that uses a suite of camera systems to document the dynamics of the urban environment. During that time, he fused data that he collected with the UO with large data sets from open data sources to assess the interconnection of people, their natural environment, and the built environment.
Part of what fascinates Dobler about the study of cities is their inherent complexity, even when compared to the study of our vast universe. Unlike the laws of physics, which govern the movements of the planets and stars, there are not many fundamental governing rules for the operation of cities.
The data that is collected by Dobler through his Urban Observatory contains within it the often unpredictable forces of human decision-making, infrastructure elements, and the co-dependency and interaction of different systems.
"Teasing out those interactions through the application of data science techniques can start to uncover ways in which targeted policy interventions may result in societal improvements," Dobler said.
For example, as Dobler expands his Urban Observatory model to cities in Delaware, one of his first projects involves working with faculty in the Biden School and across campus to observe relevant built environment variables and analyze information from open data sets that might give insight into the impact of urban infrastructure on air quality. This work could be used to inform effective policy that would increase the air quality, and therefore the quality of life, for residents.
A tremendous amount of work, algorithm creation, and machine learning conducted by data scientists like Dobler is needed to get to a point where data can be analyzed and interpreted in a way that can inform policy decisions.
"As data science and analytics becomes an increasingly important tool for public policy and administration, it is necessary to ensure that the benefits of these analyses are felt across all sectors of the population," Dobler said. "One of the applications of the analysis of UO data is to understand whether this combination of measurement and administrative data can be merged in ways that reveal underlying inequities and imbalances in cities."
While policy-makers can never be absolutely certain that a given policy will work as intended, relying on thoughtful analysis from solid data is one of the most promising paths toward effective policy.