By: David Mercer

The patchwork of abandoned homes left behind by Detroit’s economic struggles is a longstanding and complex problem. A common question among those working to revive the city is how to best address the challenges posed by sparsely populated neighborhoods.

Luc Anselin, director of the Center for Spatial Data Science (CSDS) and Stein-Freiler Distinguished Service Professor of Sociology and the College, and his team are helping to answer that question. Anselin and the CSDS team, who came to the University of Chicago in 2016, use location-based data to explore urban challenges in housing, health, and the built environment.

Using spatial data analysis in a 2015 study, Anselin showed that each demolition increased the value of nearby homes by 4 percent, and that average property values in broader zones where abandoned homes were demolished grew by 14 percent. The results were widely publicized and used to successfully argue in favor of investments of more than $20 million in Detroit’s demolition program.

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“For many years now, the center and its predecessors have gained new valuable insights in various empirical analyses of policy questions by making space (location, distance, interaction) explicit in the models and methods,” said Anselin.

That project is just one of the ways CSDS is reaching beyond the discipline of spatial data to collaborate with researchers from other fields and with the world beyond the campus.

“Partnerships create connections between the university and, for instance, city policy efforts, or between the spatial analytics community and computer scientists,” Julia Koschinsky, the CSDS’s research director, says. “This integration of researchers who have traditionally been separate is something we plan to continue to strengthen in the future.”

A second central tenet of the center’s mission is changing the standard thinking about how spatial data can be used in the social sciences, Koschinsky says.

“When most people think of spatial analytics, they think of something like putting dots on a map, of data visualization,” she said.We’re working to help people think of a spatial perspective throughout the research process, from framing a research question to hypothesizing your results, as well as in the computational infrastructure.”

In one long-term project with the Chicago Department of Public Health and community groups, CSDS Assistant Director for Health Informatics Marynia Kolak is building a location-based directory of social services resources in the West Humboldt Park neighborhood. The map gives users ready access to the locations of everything from medical providers to community gardens and sources of job training.

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As part of its mission to integrate spatial data analysis into research, the center also holds a regular study group for researchers from across the campus – students, faculty, post-doctoral researchers, and visiting researchers -- to help them apply spatial perspectives to the research problems, as well as regular sessions with physicians and post-doctoral students from University of Chicago Medicine.

Someone presents their research, and then we discuss how to devise the project so they can gain spatial insight,” Koschinsky said. For instance, a researcher recently discussed the question of how information on HIV prevention spreads through social networks. CSDS researchers were able to suggest methodologies for collecting data based also on physical addresses of those in the social networks, adding geographical analysis to how the information spreads.

CSDS researchers are also working with James Saxon, a postdoctoral fellow in the economics group of the Harris School of Public Policy, working to apply spatial methods to the analysis of gerrymandering and how redistricting could work to minimize or eliminate the practice. Saxon uses algorithms to analyze the structure of districts, down to the neighborhood level. The center also developed GeoDa, an open source software program for spatial-data analysis now used by almost 250,000 people, she said.

On-campus education, too, is a key component of the center. Anselin has introduced new undergraduate courses, and the new additional courses Introduction to Spatial Data Science and Spatial Regression Analysis. He is also building a new curriculum for a geographic information science minor.

The timing for all of these projects is ideal, Koschinsky says, as ‘there’s just an explosion of interest in this field.”

Center profile image GeoDa.png

Caption: Usually, areas with higher rates of economic hardship and a lack of insurance also tend to fare worse in terms of health outcomes. However, in line with research on the “immigrant paradox” (Coll and Marks; Kolak) the figure illustrates that, while this is the case in areas with higher shares of African-American households in Chicago, it does not seem to hold in areas with higher shares of Hispanic households. The views in the figure are linked to reveal these relationships through spatial data exploration: In the top right view (a parallel coordinate plot), areas with more than 55% of Hispanic households are selected. This subset is simultaneously highlighted on the map and histogram of premature mortality (West and Northwest areas have lower premature deaths) and in the scatterplot (with higher rates of no health insurance in these areas). The analysis was done in GeoDa, which can be downloaded for free at