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Spatial Analysis III: Spatial Statistics

East Baltimore
3rd term
4 credits
Academic Year:
2015 - 2016
Instruction Method:
Class Times:
  • Tu Th,  1:30 - 2:50pm
Lab Times:
  • Wednesday,  3:30 - 4:20pm
Auditors Allowed:
Yes, with instructor consent
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, (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:

Method of student evaluation based on assignments and 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.