R for Mapping and Spatial Analysis
Course Meetings
- This course will meet weekly to discuss reading and complete a laboratory
- This course will be a hybrid of lecture and lab; however, outside reading will need to be completed before lab.
- The students in this directed study will make the decision on when and where the course will meet at Rollins School of Public Health.
Introduction
This course provides an overview to a number of techniques aimed at the analysis of spatial public health data. We will study local and neighborhood level methods, regionalized variables, and the modifiable area unit problem. While most techniques have a geographic origin, we will address geo-spatially relevant methods in the context of global epidemiology of infectious disease. As this is a graduate directed study course, the bulk of the content will come from you, the student. The role of the instructor is mostly to provide structure and guidance. Each student conducts an individual analysis project that relates to spatial analysis using given data or data that is approved by the instructor. This course will primarily use the R programming language with an emphasis on reproduceable research.
Course Objectives
This course has five intertwined objectives. After completing the course, students will be able to:
Geographic information science: Describe the concepts that form the foundation of GISc work and its relationship to public health.
Spatial data management: Perform basic spatial data cleaning, modification, aggregation, and exploratory data anlysis tasks using R
Spatial data visualization: Create and present visualizations of spatial data using R and other design tools.
Spatial analysis development: Apply techniques that make GISc work more reproducible, accurate, and collaborative using GitHub, R, Markdown, and other tools.
Spatial research synthesis: Plan and implement a public health spatial data analysis project that utilizes the techniques described throughout the course.
MPH/MSPH Foundational Compentencies
- Analyze quantitative data using biostatistics, informatics, computer-based programming and software, as appropriate.
- Interpret results of data analysis for public health research, policy or practice.
Text
Required
- Lovelace, Robin, et al. Geocomputation with R. CRC Press, 2019.
- Available for free at: https://geocompr.robinlovelace.net
Optional, but helpful
- Bivand, Roger S., et al. Applied Spatial Data Analysis with R. Springer, 2013.
- Brunsdon, Chris, and Lex Comber. An Introduction to R for Spatial Analysis Et Mapping. Sage, 2015.
Assignments and Weights
| Assignments | Weight |
|---|---|
| Labs/Homework | 15% |
| Project Milestone One: Data cleaning plan | 10% |
| Project Milestone Two: Data visualization and EDA | 20% |
| Project Milestone Three: Data analysis plan | 20% |
| Project Milestone Four: Analysis progress report, Data analysis plan revision | 5% |
| Project Milestone Five: Present final findings | 30% |
| Total: | 100% |
Course Schedule
| Module | Topic | Lab | Readings |
|---|---|---|---|
| Introduction | Introduction and R Review/Bootcamp | R Review | Lovelace Ch. 1, R review materials (handouts) |
| More R Review | R review materials (handouts) | ||
| Foundations | Geographic data in R and basic mapping CRMs | Intro to Basic Mapping, CRMs | Lovelace Ch. 2 |
| Open Lab, Project Work | Lovelace Ch. 2 | ||
| Data Reading, Writing, and Manipulation | R spatial I/O | Introduction to Projections and File Types | Lovelace Chs. 6 and 7 |
| R spatial Reprojection | Open Lab, Project Work | Lovelace Chs. 6 and 7 | |
| Attribute data manipulation | Attributes and Vector Manipulation | Lovelace Ch. 3 | |
| Vector data manipulation, aggregation, and joins | Spatial Data Manipulation | Lovelace Ch. 4 | |
| Vector/raster data manipulation, aggregation, and joins | Open Lab, Project Work | Lovelace Ch. 4 | |
| Geometry Operations on vector data | Buffers, centroids, clipping | Lovelace Ch. 5 | |
| Geometry Operations on raster-vector interactions | Advanced Mapping/Special Topics | Lovelace Ch. 5 | |
| Advanced Mapping and Open Topics | Advanced Mapping With R | Open Lab, Project Work | Lovelace Ch. 8 |
RSPH POLICIES
Accessibility and Accommodations
Accessibility Services works with students who have disabilities to provide reasonable accommodations. In order to receive consideration for reasonable accommodations, you must contact the Office of Accessibility Services (OAS). It is the responsibility of the student to register with OAS. Please note that accommodations are not retroactive and that disability accommodations are not provided until an accommodation letter has been processed.
Students who registered with OAS and have a letter outlining their academic accommodations are strongly encouraged to coordinate a meeting time with me to discuss a protocol to implement the accommodations as needed throughout the semester. This meeting should occur as early in the semester as possible.
Contact Accessibility Services for more information at (404) 727-9877 or accessibility@emory.edu. Additional information is available at the OAS website at http://equityandinclusion.emory.edu/access/students/index.html
Honor Code
You are bound by Emory University’s Student Honor and Conduct Code. RSPH requires that all material submitted by a student fulfilling his or her academic course of study must be the original work of the student. Violations of academic honor include any action by a student indicating dishonesty or a lack of integrity in academic ethics. Academic dishonesty refers to cheating, plagiarizing, assisting other students without authorization, lying, tampering, or stealing in performing any academic work, and will not be tolerated under any circumstances.
The RSPH Honor Code states: “Plagiarism is the act of presenting as one’s own work the expression, words, or ideas of another person whether published or unpublished (including the work of another student). A writer’s work should be regarded as his/her own property.” (http://www.sph.emory.edu/cms/current_students/enrollment_services/honor_code.html)