Instructor information

• Juan Carlos Villaseñor-Derbez
• Office: MSC 326
• Contact e-mail: jc_villasenor at miami.edu
• Office hours: By appointment

Class schedule

• M / W @ 12:00 - 13:15 SLAB 114

Class description

The course will introduce students to intermediate spatial analysis in R, in the context of Environmental Science and Policy. Introductory knowledge of spatial analysis (e.g. GIS using QGIS or ESRI products) is required, as is previous exposure to the use of scripting languages (e.g. R or Python). During the first part of the course (weeks 1-6), students will gain a solid foundation on modern tools and standards for spatial analysis in R (e.g. simple features, spherical geometries). The second part (weeks 7-15) will expose students to intermediate techniques (e.g. interactive visualization, scripting). Students will learn how to find, retrieve, and work with a suite of spatial data products commonly used in Environmental Science and Policy.

Undergraduate students Some seats are reserved for undergraduate students who have some relevant experience and permission. The expectations for undergraduates are slightly more lenient with regards to the final project. All other expectations remain the same.

Class objectives

At the end of this course, students will: 1. Have a foundational understanding of the principles of spatial analysis in R 2. Be able to identify and access data sources, design and build processing pipelines, and create spatial models related to Environmental Science and Policy

Pre-requisites

Required: previous exposure to GIS, such as EVR 660. Introduction to Marine Geographic Information Systems. Encouraged: And previous exposure to R / statistics, such as: RSM 612. Statistics for Marine Scientists, EVR 622. Principles and Practices of Marine Social Science Research, or EVR 624. Statistics and Data Analysis for Environmental Science and Policy

Class materials

• Will be primarily available on the course website and GitHub repository, but also on UM blackboard.

Reading resources

• GecCompR: Geocomputation with R
• Selected peer-reviewed publications
• Ad hoc software documentation

Grading

• Lab exercise and attendance: 20%
• Assignments: 30%
• Midterm exam: 10%
• Individual project: 40%

Course contents

1. Intro to spatial analysis in R
2. Geographic data models in R
   • Milton track
3. Attribute operations
4. Spatial data operations in R
5. Geometry operations and Raster-Vector interactions
6. Static visualization (maps)
7. Interactive visualization (web maps)
8. Scripts, algorithms, and functions
9. Principles of spatial statistical learning and remote sensing
10. Final project presentations

List of in-class exercises (examples)

• Ex1: Software installation
• Ex2: Vessel-tracking data and Marine Protected Areas
• Ex3: Working with species distribution model outputs
• Ex4: Calculating progress on 30x30 (in the oceans)
• Ex5: Historical hurricane incidence in FL
• Ex6: SST trends in the world’s Large-scale Marine Protected Areas
• Ex7: Florida’s Marine Protected Areas
• Ex8: Interactive map of commercial fishing effort around the FL peninsula
• Ex9: Batch-downloading remote-sensing data
• Ex10: K-means clustering
• Ex11: Supervised classification

List of assignments (examples)

• Assig 1: Proof of attempted or successful installation of all required dependencies
• Assig 2: Manipulation of spatial data in R
• Assig 3: Visualization of some of your project’s data (see below)
• Assig 4: Remote sensing

Individual projects

The individual project is intended to serve two purposes. First, provide the student with an opportunity to apply their newly acquired tools and techniques to their own research or application. And, secondly, to serve as proof of learning. As such, all individual projects will meet the following criteria:

1. All data and code must be primarily analyzed or processed using R and RStudio (or an equivalent scripting / coding language to be pre-approved by the instructor)

2. The student will show they have mastered the course material by correctly and successfully employing two tools and techniques covered in part 1 of the course and two tools and techniques covered in part 2 of the course.

3. The final product will be a 5-7 page report (and a 5-7 slide presentation), roughly divided as follows:

• ~1 page (slide) explaining the objective of the project

• ~1 page (slide) describing relevant data sources

• 2-3 pages (slides) clearly outlining the tools and techniques used for the project, and a justification for their choice.

• ~1 page (slide) describing the main outcome of the project (the outcome may be anything a carefully curated dataset to be used at a later stage in the student’s research, preliminary results, or finalized results)

Note that references, appendices, and notes are not included in the page limit.