Course Schedule
Course Schedule
Week One: Introduction:R Studio, and Spatial Data Visualization
Weekly Resources:
Virtual Materials:
Class Overview:
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Tuesday January 14: Course Overview and Class Conversation
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Tuesday January 16: R Mapping Demo QMD Source Code
Week Two: Spatial Data Visualization, Projections & Distances, Point Process Intro
Weekly Resources:
Virtual Materials:
Reading:
- Chapter 1
HW 1: Due Thursday January 30 (Quarto Source Code)
Class Overview:
- Tuesday January 21: R Mapping Demo: Part II QMD Source Code
- Thursday January 23: Project & Distances + Point Process Intro
Week Three: Point Process Testing
Weekly Resources:
HW 2: Due Thursday February 6 (QMD Source)
Class Overview:
- Tuesday January 28: Point Process Tests
- Continue PP Intro (Key)
- Thursday January 30:
Week Four: Point Process Modeling
Weekly Resources:
HW 3: Due Thursday February 13 (QMD Source)
Class Overview:
- Tuesday February 4:
- Thursday February 6:
Week Five: Google Earth Engine
HW 4: Due Thursday February 20 (QMD Source)
Class Overview:
- Tuesday February 11:
- NO CLASS!
- Explore the Google Earth Engine Demo (QMD Source Code)
- Thursday February 13:
Week Six: Review & Exam Week
Class Overview:
- Tuesday February 18:
- Class Review
- Thursday February 20:
- In Class Exam I
- Take Home Exam Assigned (Quarto Source Code) (Due Thursday February 17 at 10:50 AM. Turn in to gradescope.)
Week Seven: Linear Algebra and Conditional Multivariate Normal
Weekly Materials
Class Overview:
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Tuesday February 25: Week 7 Activity: Linear Algebra & Linear Modeling: PDF (Qmd Source) (Key: QMD) (Key: PDF)
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Thursday February 27: Week 7 Activity 2 (QMD Source Code)
Week Eight: Bayesian Analysis with Stan & Gaussian Processes
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Video Lectures: Rstan installation video: ~11 minutes (MD Script) (Quarto Source) (Stan Script: Regression.stan)
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Virtual Materials: R Stan Installation Guidelines
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Suggested Reading: Gramacy: Surrogates Ch 5.1
Class Overview:
- Tuesday March 4: Week 8 Activity (Quarto Source Code) (Week 8 Key: PDF) (Week 8 Key: QMD)
- Thursday March 6: Week 8 Activity continued
Week Nine: Spatial Statistics Fundamentals
Weekly Materials
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Suggested Reading: HMASD Ch.2 Basics of Point Referenced Models
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Weekly Notes:
Class Overview:
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Tuesday March 11: Week 9 Activity (QMD Source Code)
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Thursday March 13:
Week Ten: Spring Break
Class Overview:
- Tuesday March 18: No Class
- Thursday March 20: No Class
Week Eleven:
Weekly Materials
- HW 6 (Quarto Source Code) (Due Thursday March 27)
Class Overview:
- Tuesday March 25 Week 10 Activity (QMD Source Code)
- Thursday March 27 Week 10 Activity b (QMD Source Code)
Week Twelve:
Weekly Materials
- HW 7 (Quarto Source Code) (Due Friday April 4)
Class Overview:
- Tuesday April 1 Week 11 Activity (Quarto Source Code)
- Thursday April 3
Week Thirteen:
Weekly Materials
Class Overview:
- Tuesday April 8
- Thursday April 10
Week Fourteen:
Weekly Materials
Class Overview:
- Tuesday April 15
- Thursday April 17
Week Fifteen:
Weekly Materials
Class Overview:
- Tuesday April 22
- Thursday April 24
Week Sixteen:
Weekly Materials
Class Overview:
- Tuesday April 29
- Thursday May 1
Week Seventeen: Exam III - Areal Data
Class Overview:
- Exam III: Tuesday May 6 10:00am - 11:50am
Course Description
Statistical methods of spatial data analysis, stationary and nonstationary random fields, covariance structures, geostatistical models and analysis, spatial point process models and analysis, spatial lattice models and analysis. An emphasis will be placed on:
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Creating maps and other data visualization products with spatial data,
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Identifying differences between the three common spatial data types: point process, geostatistical, and areal data,
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Using statistical software and either Bayesian or classical statistical techniques to analyze spatial point process, geostatistical, and areal data structures, and
Learning Outcomes:
At the end of the course students will understand
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point process theory and applications including homogeneous and non-homogeneous Poisson point processes
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geostatistics including semivariogram estimation and kriging
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spatial autoregression including covariance estimation, spatial logistic and Poisson models, simultaneous autoregressive models, conditional autoregressive models.
Course Syllabus
A downloadable PDF of the course syllabus is available: Download PDF Syllabus
Prerequisites
- Required: STAT 412, STAT 512, and STAT 422
- Preferred: STAT 506, extensive experience with R, and an understanding or interest in Bayesian statistics
Textbooks
- Hierarchical Modeling and Analysis for Spatial Data, Second Edition, by Bannerjee, Carlin, and Gelfand. While the second edition is preferred, the first edition will suffice.
- Animal Movement: Statistical Models for Telemetry Data, by Hooten, Johnson, McClintock, and Morales. Optional
Office Hours
- Tuesday 8:30 - 9:15
- Tuesday 12:30 - 2
- Thursday 8:30 - 9:15
Additional Resources
Analysis and data visualization will be implemented with R
Course Policies
Grading Policy
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25% of your grade will be determined by homework assignments. Collaboration is encouraged on homework assignments, but everyone should complete their own assignments.
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75% of your grade will be determined by a series of three exams and applied projects.
Collaboration
University policy states that, unless otherwise specified, students may not collaborate on graded material. Any exceptions to this policy will be stated explicitly for individual assignments. If you have any questions about the limits of collaboration, you are expected to ask for clarification.
In this class students are encouraged to collaborate on homework assignments, but exams and projects should be completed without collaboration.
Academic Misconduct
Section 420 of the Student Conduct Code describes academic misconduct as including but not limited to plagiarism, cheating, multiple submissions, or facilitating others’ misconduct. Possible sanctions for academic misconduct range from an oral reprimand to expulsion from the university.
Disabilities Policy
Federal law mandates the provision of services at the university-level to qualified students with disabilities. If you have a documented disability for which you are or may be requesting an accommodation(s), you are encouraged to contact the Office of Disability Services as soon as possible.
Approximate Course Outline
- Course Intro & Preliminaries:
- R
- Plotting spatial data
- Linear Models and Bayesian Inference
- Point Process Data
- Point Referenced Data
- Areal Data