
GEOG 591: Spatial Data Science in Public Health
Applications and theory in spatial data science for analyzing the geographic dimensions of human health
Course Description
This course focuses on the growing role of Spatial Data Science (SDS) and spatial thinking in understanding human health and well-being. SDS brings together several related and established geospatial approaches while incorporating emerging perspectives and tools from data science and machine learning.
This comes at a time when society is experiencing intense pressure on human health and well-being and the provisioning of adequate health and social support. For example, adverse health risks and impacts of climate change are accelerating, including more intense heat waves and other extreme weather, changing distributions of mosquito-borne disease, longer and more intense hurricane seasons, and other, more general effects on well-being and quality of life. There are other existing and new threats, such as the disease burden of malaria and emerging diseases such as COVID-19. SDS aids our understanding of and can help identify solutions across this complex health and wellbeing landscape through the integration of a range of disciplines, datasets, and methods.
The course covers relevant SDS techniques for analyzing health phenomena, such as methods for detecting areas of higher risk of disease and cartographic techniques for visualizing geospatial health data. The course also provides a foundation in relevant concepts from public health, epidemiology, and social medicine, and focuses on their geographic dimensions and diverse types of study design. Concepts and techniques are explained through weekly lessons, practical activities, and a term project. The techniques introduced are often mathematically complex, but the emphasis is on the choice and application of appropriate methods for the analysis of health and disease often encountered in applied geography as well as developing a framework in which to approach the analysis. Topics range across data surveillance and infrastructure planning, modeling vector-borne diseases, planning for recovery through an evaluation of healthcare accessibility, cluster analysis, predicting health outcomes, and responding to outbreaks and epidemics.
Weekly projects are hands-on, using geographic information systems or other appropriate computational tools, so that students appreciate the practical complexities involved, but also develop an understanding of the limitations of these methods. The term project is intended to allow students to formulate a research problem in a topic area of their choosing, to gather and organize appropriate available datasets, and to understand how different methods covered in the course can be applied in combination to thoroughly explore real questions. Students will be asked to engage with their peers' work during the project planning stage.
Course Learning Objectives
This course has four main learning objectives:
- Subject matter objective – Public Health – Demonstrate a solid foundation of theory and practice in public health, epidemiology, and social medicine from a geographical perspective.
- Subject matter objective – Spatial Data Science – Utilize Spatial Data Science approaches, data issues, assumptions, and requirements to apply specific methods.
- Research design and critical thinking objective – Identify researchable questions, identify data requirements, and select appropriate geospatial and SDS approaches and methods.
- Communication objective – Synthesize information and present findings for different audiences.
By the end of this class, students will be able to:
- Critically evaluate contemporary developments in health from a spatial analysis perspective both in research and applied contexts.
- Explain the issues involved in representing people, their health, and potential explanatory factors.
- Critically evaluate the evidence for and against causal relationships between health outcomes and environmental factors.
- Explain the relative roles of individual-level effects and area-level effects (or composition and context) in influencing patterns of health and the role that SDS can play in exploring these.
- Discuss the role of SDS analyses of health alongside a range of complementary approaches from related fields.
- Use SDS tools to identify spatial patterns in health, and undertake an exploratory analysis of potential explanatory factors.
- Select and apply analytical methods for mapping, modeling, and analyzing health and disease, including point pattern analysis, surface analysis, overlay analysis, network analysis, cluster, and regression analysis.

Students who register for this Penn State course gain access to assignments and instructor feedback and earn academic credit. Information about Penn State's Online Geospatial Education programs is available at the Geospatial Education Program Office.