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Applied Spatial Statistics

3rd term
Online Programs for Applied Learning
4 credits
Academic Year:
2021 - 2022
Instruction Method:
Auditors Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructors:
Frank Curriero

Public Health Statistics II (600.712.86), or equivalent; Spatial Analysis for Public Health (601.731.86)


Introduces statistical techniques used to model, analyze, and interpret public health related spatial data. Casts analysis of spatially dependent data into a general framework based on regression methodology. Covers the geostatistical techniques of kriging and variogram analysis, point process methods for spatial event and case control data, and area-level analysis. Focuses on statistical modeling and topics relating to clustering and cluster detection of health related events. Provides an introduction to the public domain statistical software R, to be used for analysis. Reinforces skills and concepts related to the spatial science paradigm: Spatial Data, GIS, and Spatial Statistics.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Define and describe the concepts of spatial dependence with a public health context
  2. Apply techniques to quantify spatial dependence with different types of spatial data
  3. Conduct spatial statistical analysis using regression techniques extended to address properties of spatial data
  4. Identify the potential consequences of overlooking spatial information when conducting public health research
Methods of Assessment:

Quizzes: 30%; Problem set 1: 25%; Problem set 2: 25%; Problem set 3: 20%

Enrollment Restriction:

Restricted to students enrolled in the Spatial Analysis for Public Health program

Instructor Consent:

No consent required