HACLab’s research portfolio spans climate and environmental change, public health, agriculture, spatial data science, and immersive visualization. The projects below represent our active areas of work: from climate perception and extreme-event modeling to BRFSS health analysis, agricultural insurance, data-center impacts, and the development of new spatiotemporal methods. Together, these efforts integrate geospatial data, advanced computation, and environmental communication to address complex societal and ecological challenges.
This project uses Behavioral Risk Factor Surveillance System data to model geographic and temporal patterns in population health. The work combines public-health surveillance, spatial analysis, and predictive modeling to identify disparities and produce locally relevant health estimates.
This research examines how people perceive changes in climate and environmental hazards and compares those perceptions with measured conditions. The project focuses on heat, drought, wildfire, and other climate-related risks across rural and underserved communities.
This project investigates the geographic growth and environmental implications of data centers, including their relationships with energy demand, water use, land development, and surrounding communities. Spatial analysis is used to identify emerging patterns, potential impacts, and areas of concern.
This research evaluates how climate variability and extreme weather influence agricultural losses and insurance claims across the United States. Spatiotemporal and machine-learning methods are used to identify regional differences in risk and the climatic factors associated with agricultural damage.
This project develops and evaluates new methods for analyzing processes that vary across both space and time. Current work includes geographically weighted random forests, spatial machine learning, local feature importance, and scalable modeling approaches for large environmental and public-health datasets.
This research explores how virtual reality, immersive environments, maps, and interactive visualizations can improve the communication of environmental information. The goal is to make complex spatial and climate data more understandable, engaging, and useful for researchers, stakeholders, and the public.
This project analyzes long-term changes in climate extremes, with an emphasis on drought, extreme heat, and fire-weather conditions. The research uses observational climate data and spatiotemporal methods to examine changes in the magnitude, frequency, timing, and persistence of environmental hazards.