Great Problems, Great Minds Seminar Series: Incorporating Uncertainty Into Geographical Data Visualization


Daoqin Tong

Join the Department of Social Sciences for this Great Problems, Great Minds seminar series event featuring guest speaker Daoqin Tong, a professor in the School of Geographical Sciences and Urban Planning at Arizona State University. Tong received her M.S. in civil engineering, M.A.S in statistics, and Ph.D. in geography from Ohio State University, and a bachelor’s degree in civil engineering from University of Shanghai for Science and Technology. Tong’s research has mainly focused on spatial analytics including geographic information systems (GIS), spatial optimization, and spatial statistics to support urban system design and planning, including facility location, network design, food outlet placement, green infrastructure interventions, flood mitigation, and bike sharing service planning. Tong’s research has led to many publications of articles in high-quality journals in the fields of geography, transportation, and planning, such as Annals of the Association of American Geographers, Nature Communications, and Landscape and Urban Planning. Her research has been funded by the National Science Foundation, the Department of Defense, the National Institute for Transportation and Communities, Vitalyst Health Foundation, and Arizona Board of Regents Research Innovation Funds. The event will take place on March 9 beginning at 12:40 p.m.

Data is often imperfect, despite the recent advances in data collection technologies. How to incorporate such uncertainty into data visualization has been challenging. This research discusses the use of choropleth mapping to visualize geographical data that contain uncertainty. A new map classification scheme is introduced to account for a wide range of data uncertainty. To address the possibility that a geographical unit might be placed in a wrong map class due to data uncertainty, a robustness measure is proposed and integrated into the optimal design of choropleth maps. Solution algorithms are developed along with a novel theoretical bound for evaluating solution quality. The new approach is applied to map the American Community Survey data. The study provides an important perspective on addressing data uncertainty in map design and offers a new approach for spatial data analysts to incorporate robustness into the data visualization process.

The “Incorporating Uncertainty Into Geographical Data Visualization” event will take place on Zoom.

The event is part of the Great Problems, Great Minds seminar series that explores the major problems facing humanity as we move into the heart of the twenty-first century. To see the full schedule and videos from previous events, visit the seminar series page.

For more information, contact Assistant Professor of Social Sciences Hao Huang at

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