Asynchronous: Spatial Epidemiology

Wednesday, June 1, 2022 - Thursday, June 30, 2022

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Course Description

Spatial epidemiology is the study of geographic distributions and determinants of health in populations. The goal of this class is to introduce students to relevant theory and methods, in order to provide the foundational skills required to understand and critically analyze spatial epidemiologic studies. The course emphasizes spatial epidemiology as a sub-discipline of epidemiology while acknowledging the many scientific disciplines that shape it, including biostatistics, cartography, criminology, demography, economics, geography, psychology, and sociology. We begin by defining spatial epidemiology and exploring these multi-disciplinary roots, with particular regard to the theoretical causal mechanisms that provide a bridge between social and physical environmental conditions and population health. We then provide a basic overview of geographic information systems and their utility for descriptive spatial epidemiology—including data visualization and cluster detection—before demonstrating how to incorporate spatial structures within conventional epidemiologic study designs to examine associational and causal relationships between environmental conditions and health outcomes.

Course Objectives

Students who successfully complete this course will be able to:

  • Describe the unique contribution of spatial epidemiology compared to other areas of epidemiology;

  • Use social ecological theories to explain associations between spatially structured social and physical environmental conditions and health;

  • Use epidemiologic methods to assess causation in associations between spatially structured social and physical environmental conditions and health;

  • Evaluate published studies in light of common biases that affect spatial epidemiologic research;

  • Demonstrate how to use Census data, archival data, survey data, and field observations to measure neighborhood social conditions;

  • Explain available approaches to measuring individuals' exposure to social and physical environmental conditions, and identify optimal approaches given specific research questions;

  • Describe spatial ecological study designs, including cross-sectional studies, quasi-experimental studies, and experimental studies.



Students must have prior training in epidemiology, such as a Master of Public Health. Familiarity with Geographic Information Systems (e.g. ArcGIS) is advantageous but not required.

Course Reading List

There is no required text for this course, however students are encouraged to read the following texts in preparation for the course:


Monmonier M. How to Lie with Maps. Chicago, Il: University of Chicago Press; 1991.


Sampson RJ. Great American City: Chicago and the Enduring Neighborhood Effect. Chicago, Il: The University of Chicago Press; 2012.


Christopher Morrison, PhD

Christopher Morrison is a social epidemiologist specializing in spatial analytic methods. His research seeks to understand how social and physical environmental conditions affect population health, particularly injuries and alcohol-related harms. His recent work has examined associations between ridesharing (e.g. Uber) and motor vehicle crashes, bicycle infrastructure and bicycle crashes, and firearm laws and firearm violence. Dr Morrison also maintains research interests in his hometown of Melbourne, Australia, including current projects to assess the scientific content presented in liquor licence hearing, and geographic determinants of road traffic crash risks.

Course Fee

Early registration discount before April 1, 2022: $1,260.00
After April 1, 2022: $1,400.00


The registration period has closed for this event.

Online Course Format

This is a month-long, 30 hour, digital course. Lectures and course material will be presented online in six modules. The course is self-paced, with weekly discussion boards to promote discussion and interaction among participants. The flexible format will include video or audio recordings of lecture material, self-assessment exercises, and access to the instructor for feedback during the course. The course is roughly 50% didactic lecture and 50% applied programming and analytics. The course utilizes the learning management software, Canvas (; participants will receive an e-mail inviting them to join on the first day of the course. Any additional information about technical requirements and access to the course will be shared in the weeks before the course begins.

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