GGR322 GIS and Population Health

Course overview

Spatial examination of population health entails engaging with population vulnerabilities, environment exposures, health data, and spatial methods. In this context, this course examines the nexus of geographical information science, place (neighbourhoods), and population health, with the aim of helping students form and answer questions about the ways in which different exposures to different types of environments (physical, social, and service) influence health behaviour, risks, and outcomes. The course setup is designed towards paving the way for students to carry out GIS analytical projects independently. Hence, the setup is based three edges:

• introducing foundational concepts and approaches through lectures;
• practicing data preperations and methods' design through GIS practicals (labs);
• and finally, students applying the knowledge they gained in carrying out projects independently through assignments (projects).

Themes covered in the course

• Spatial measurements of population vulnerabilities
• Operationalization and measurement of Neighbourhood environments
• Conceptual frameworks in GIS and Health
• Examining, preparing, and merging health, environment, and population demographics datasets
• Geographic representation of health and population data
• Structure and stages of GIS projects in health research
• Strengths and limitation assocaited with using spatial data and spatial boundaries
• Examinations of global and local spatial clusters
• Spatial Access to medical and non-medical resources
• Regression and Geographically weighted regression
• Triangulations of spatial methods in examining complex population health topics
• Plain interpretation of GIS analytical output and forming policy recommendations

Student Mini-project portfolio

The mini project focuses on examining the presence of a potential vulnerable resident population and their neighbourhood walkability, which is a feature that can shape daily mobility, access to amenities, community interaction, and health outcomes. Yet, walkability resources are not distributed evenly across neighbourhoods, and this uneven distribution can affect population groups that face health risks or social inequities. Students were placed in a hypothical project where they act as a GIS analysts working within a geospatial data science team tasked with supporting a new funding initiative aimed at improving walkability for populations of concern. Each student was assigned a Census Division and identified a priority population based on vulnerability or equity considerations. Using spatial methods, specifically, the Local Indicators of Spatial Autocorrelations (LISA), students identified where potential vulnerable population clusters and analyzed where walkability resources are insufficient. Thereafter, students overlayed both outcomes to highlight neighbourhoods that can benefit from improving walkability infrastructure.

Student Final-Project Portfolio

The final project focuses on examining spatial and the aspatial access of the Greater Toronto Area’s residents to medical and non-medical resources (e.g., pharmacies, walk-in clinics, community centres, childcare. In this context, students were placed in the hypothetical role of GIS analysts applying statistical analysis and floating catchment area (FCA) methods to identify neighbourhoods facing inequities in access to sufficient resources, while accounting for an aspatial group (e.g., residents with vulnerable health status, and vulnerable demographic groups). For the supply side, each student started by selecting an administrative division and one type of resources. Thereafter, for the demand side, students determined a relevant demand population as well as an aspatial population. By integrating accessibility analysis to the presence of aspatial group, students were able to provide a comprehensive understanding of thresholds for different accessibility levels across different divisions, along with determining geographic units for interventions aiming at improving accessibility.