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Spatial Analysis III: Spatial Statistics

3rd term
4 credits
Academic Year:
2022 - 2023
Instruction Method:
Synchronous Online
Class Times:
  • Tu Th,  1:30 - 2:50pm
Lab Times:
  • Wednesday,  3:30 - 4:20pm
Auditors Allowed:
Yes, with instructor consent
Undergrads Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Frank Curriero

140.621.-623 (enrollment in 140.623 may be concurrent with enrollment in this course)


Introduces statistical techniques used to model, analyze, and interpret public health related spatial data. Analysis of spatially dependent data is cast into a general framework based on regression methodology. Topics covered include the geostatistical techniques of kriging and variogram analysis and point process methods for spatial case control and area-level analysis. Although the focus is on statistical modeling, students will also cover topics related to clustering and cluster detection of disease events. Although helpful, knowledge of specific GIS software is not required. Instruction in the public domain statistical package R/RStudio, (to be used for analysis), is provided.

Learning Objectives:

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

  1. Describe the concept of spatial dependence and apply techniques to quantify it with different types of spatial data
  2. Conduct routine spatial statistical analysis using extended regression techniques within the R Statistical Computing Environment software
  3. Identify the potential consequences of overlooking spatial information when conducting certain types of public health research
Methods of Assessment:

This course is evaluated as follows:

  • 60% Assignments
  • 10% Lab Assignments
  • 15% Quizzes
  • 15% Final Exam

Instructor Consent:

No consent required

Jointly Offered With:
Special Comments:

The course schedule includes 2 lecture periods and one lab per week. The lab hour is devoted mostly to computing for the assigned problem sets. All Tuesday and Thursday lectures (1:30-2:50pm EDT) will be delivered in a synchronous remote format. The Wednesday lab (3:30-4:20pm EDT) will be in person with a remote option. If you are planning to attend the entire course remotely (i.e. remote for the Wednesday lab) then register for the .41 section. If you plan to attend the Wednesday lab in-person, then register for the .01 section.