PUBH6143

This course will give students applied experience in the analysis of environmental health data to evaluate public health practice problems and answer research questions using spatial methods. We will focus on applying spatial analysis to data types common to the study of environmental health exposures and diseases, such as disease surveillance, land cover, animal density, environmental hazards, and community characteristics. Specific sources may include disease surveillance data from the Minnesota Department of Health, air pollution data from EPA, remote sensing data from NASA, social vulnerability index data from CDC, and demographic data from the American Community Survey and decennial census. Guided data analyses accompany most core methods/techniques, providing students with a set of R Statistical code they can utilize in the future.

The intended audience for this course are masters and doctoral students who seek to analyze spatial data and interpret spatial analyses in a variety of areas, including infectious disease, climate change, environmental epidemiology, and environmental justice. Their goal should be the interpretive and quantitative skills to perform spatial data analysis that can be applied in future public health evaluations and research projects. Students will leave this course with the ability to acquire and prepare spatial data, identify their spatial data type, apply appropriate analytical methods, interpret their findings, visually present their data, and have skills to critically evaluate published papers that use spatial data analysis. This course is applied in nature and is not intended for students seeking to mathematically derive existing or new spatial methods.

Every Spring