UNIVERSITY PARK, Pa. — Obesity is a predictor of multiple negative health outcomes, ranging from Type 2 diabetes to coronary heart disease to premature death, according to the National Institutes of Health (NIH). Of the nearly two-thirds of U.S. adults that are overweight or obese, non-Hispanic Black adults have the highest age-adjusted prevalence of obesity.
The NIH’s National Institute on Minority Health and Health Disparities has awarded Zhenlong Li, associate professor in Penn State’s Department of Geography, a two-year, $399,391 grant to study how environmental factors contribute to obesity, particularly in racial and ethnic minority communities.
"This award marks a significant milestone in my research," Li said. "It offers an exciting opportunity to broaden the scope of my work and apply geospatial methods to develop innovative solutions for addressing critical public health challenges beyond infectious diseases."
Li and his team will develop and test a novel measurement tool to assess obesity-related behaviors at multiple geographic levels — ranging from neighborhoods to counties across the U.S. — and potentially offer insights that can inform more targeted public health interventions.
“Measuring the obesogenic environment is measuring the various factors in our environment that support being obese,” Li said. “Obesity disparities by race and geolocation result from complicated interactions between individual behaviors like physical activities and healthy food choices, and socioeconomic and environmental contexts like income, public infrastructure and neighborhood green lands.”
Traditional obesogenic environment indices are limited by a lack of timely monitoring, challenges in integrating with behavioral data and potential bias due to self-reporting surveys, according to Li.
The researchers’ new tool, named the visitation-based obesogenic environment measurement or VOEM, is designed to address these limitations and better assess and explore racial disparities of obesity. VOEM will use cellphone visitation data to track how frequently people visit locations like parks, gyms, fast-food outlets and grocery stores — places known to influence behaviors related to physical activity and food choices, Li said.
While traditional measures of obesogenic environments often rely on static data, such as the number of fast-food outlets or parks in a neighborhood, these methods do not capture how people use these resources, which limits understanding of how environmental factors directly impact obesity, according to Li. Li’s method takes a more adaptive approach by mapping real-time visitation patterns.
"By using dynamic visitation data, we’re tracking how people actually interact with their environment in near real-time," Li said. "This will provide policymakers with actionable data to help reduce health disparities, particularly in racial and ethnic minority communities that often face greater barriers to accessing healthy resources."
The VOEM tool represents one piece of Li’s broader research, which focuses on the intersection of geographic information science (GIS), public health and geospatial artificial intelligence. At Penn State, Li leads the Geoinformation and Big Data Research Laboratory, where his team applies advanced geospatial techniques to tackle a wide range of public health and environmental challenges. His work integrates spatial computing and big data analytics to develop new methodologies for understanding health behaviors and outcomes, with the ultimate goal of improving health equity.
Li said his research is rooted in collaboration. He emphasized that the success of these projects relies on interdisciplinary partnerships with experts in public health, data science and geospatial analytics. For this project, Li teamed up with colleagues from the University of South Carolina, where he was a faculty member before joining Penn State earlier this year.
"Each project involves a network of interdisciplinary partners," Li said. "For this obesogenic environment measurement project, I will be collaborating with researchers in South Carolina’s Big Data Health Science Center. These partnerships ensure we can access critical health data and domain expertise to translate our findings into effective public health interventions."
In addition to this award, Li is currently working on several other projects that align with his broader research focus on geospatial big data and public health, and in total he has secured over $ 1.2 million to support his research activities at Penn State.
One of these projects investigates the impact of HIV-related service interruptions during the COVID-19 pandemic in South Carolina. It uses geospatial methods to track disruptions in HIV care to understand how pandemic-related interruptions have affected health outcomes, particularly in vulnerable populations.
Another NIH-funded project focuses on visualizing and predicting new and late HIV diagnoses in South Carolina. This project uses big data approaches to map and forecast trends in HIV diagnoses, providing public health officials with valuable information to better target interventions.
Li said that while these projects focus on different public health issues — obesity and HIV — they share a common goal: using advanced GIScience, which integrates multiple sources of information through location, and geospatial data to address health disparities.
"Whether it’s obesity, HIV care or pandemic-related disruptions, our focus is on using spatial data to reveal disparities and inform public health interventions," Li said. "The use of geospatial big data allows us to gain deeper insights into how health behaviors and outcomes are influenced by environmental and social factors."
Li emphasized the significance of these initiatives as part of a broader effort to leverage geospatial technologies for societal impact, reiterating the importance of collaboration and innovation in tackling complex public health issues.
"Penn State geography is a leader in geographic information science, and I’m fortunate to be working in such a collaborative and innovative environment," Li said. "These projects represent a cohesive effort to use geospatial big data and computational methods to address public health challenges in a way that can make a real difference for communities in need."
Li earned his undergraduate degree in GIS and remote sensing from Wuhan University in China, his master of science and doctorate, both in Earth system science, from George Mason University.
Other collaborators on this grant include Shan Qiao, associate professor; Andrew T. Kaczynski, associate professor; and Xiaoming Li, professor and SmartState Endowed Chair for Clinical Translational Research, all in the Department of Health Promotion, Education, and Behavior at the University of South Carolina Arnold School of Public Health.