140.698.01
Spatial Analysis III: Spatial Statistics
Location
East Baltimore
Term
3rd Term
Department
Biostatistics
Credit(s)
4
Academic Year
2024 - 2025
Instruction Method
In-person
Tu, Th, 1:30 - 2:50pm
Lab Times
Wednesday, 3:30 - 4:20pm (01)
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frank Curriero
Contact Email
Frequency Schedule
Every Year
Resources
Prerequisite
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:
- Describe the concept of spatial dependence and apply techniques to quantify it with different types of spatial data
- Conduct routine spatial statistical analysis using extended regression techniques within the R Statistical Computing Environment software
- 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
Jointly Offered With
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.