Dr. Guangqing Chi is an environmental demographer with a focus on socio-environmental systems. His research seeks to understand the interactions between human populations and the built and natural environments and to identify important social, environmental, infrastructural, and institutional assets to help vulnerable populations adapt and become resilient to environmental changes. A key characteristic of his research is the attempt to integrate social systems and the built and natural environments in understanding population dynamics by developing and applying innovative spatial and big data methods to traditional and nontraditional data.
In the substantive area of socio-environmental systems, Chi investigates community development and resilience in response to climatic and environmental changes, as well as the impacts of transportation and infrastructure on population change, inequality, and health at multi-scales over time and across space. His current research focuses on climate change impacts on coastal communities in the Arctic and the pasture–migrant system in Central Asia. He leads several NSF-funded projects, including the $3 million multi-institutional, transdisciplinary POLARIS project to investigate how environmental changes and the COVID pandemic impact social well-being, the subsistence way of life, food security, migration, and community infrastructure in Arctic Alaska rural communities by working with local stakeholders, community members, and researchers. The ultimate goal of POLARIS is to enhance community resilience via social capital, institutional resources, and critical infrastructure.
In the methodological domain, Chi’s work has led to innovative methods for identifying and measuring human–environment hotspots relating to land developability, population stress, wildfire–population corridors, ecosystems–development stress areas, rural land vulnerable to abandonment, critical riparian zones, and urban areas with high heat risks. He also led the development of spatiotemporal regression methods and applied them in his research on migration, poverty, and fertility. Chi is lead author of the textbook Spatial Regression Models for the Social Sciences (SAGE 2019). His work in applied demography has led to state-of-the-art spatial methods for population forecasting. His current methodological focus is to build an infrastructure for collecting, integrating, and analyzing multi-dimensional and multi-scale data, including big social data (60+ TB; Twitter, Facebook, mobile phones, credit cards, web scraping). He currently leads an NSF project to study the (mis)representativeness of Twitter data and to develop weights to generalize the data, which will create myriad opportunities for social scientists to take advantage of rich social media data.
Chi has received more than $50 million through 60+ grants from NSF, NIH, NASA, USDOT, DOD, SSRC, and others. He has authored or co-authored more than 120 publications, including 70+ peer-reviewed journal articles, that contribute to foundational advances in environmental demography, spatial demography, and the population–infrastructure nexus. His gasoline price research has been highlighted more than 2,000 times by such news outlets as National Public Radio, Money, and Huffington Post. His work on developing spatial methods for small-area population forecasting was awarded the E. Walter Terrie Best Paper Award twice by the Southern Demographic Association. He served as a deputy editor of Demography, and he currently serves on the editorial boards of Population and Environment and Spatial Demography. He is the director and PI of the Computational and Spatial Analysis (CSA) Core of the Social Science Research Institute and Population Research Institute at Penn State, which is funded in part by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). His work is often collaborative and transdisciplinary, aiming to create significant impacts through the integration of research, education, community engagement and outreach, and sometimes international collaboration.