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Directed Reading: Applied Spatial Statistics for Public Health Data

Course Number
CHL7001H S4
7000 (Reading Courses & Research Projects)
Course Instructor(s)
Erjia Ge, Lennon Li

Course Description

The course contains three parts: (1) exploratory spatial data analysis, (2) spatial regression
models, and (3) geostatistics. Part 1 introduces spatial data structure and methods of
exploring spatial point pattern and spatial autocorrelation in public health data. Part 2
focuses spatial and spatiotemporal regression models to quantify exposure-response
relationship in diseases. Part 3 introduces advanced geostatistical methods for disease
mapping and risk prediction by Bayesian spatial inference.

Course Objectives

This course aims to:

  1. introduce concepts, theories, and methods of spatial statistics applied in real-world public health data;
  2. strengthen students’ abilities and skills in spatial data analysis and modeling; and
  3. improve students’ understanding for population health issues from the perspectives of space and time.

After the course, students will have a relatively complete knowledge in spatial statistics and
be able to lead spatial and spatiotemporal studies for public health data.

Methods of Assessment

Participation 10%
Assignments (x 2) 25% each
Final project 40%