GEOG 584
Logistics
| Instructor | Sergio Rey | Lecture Meetings | Tue 11:00-12:40 |
| Thu 11:00-13:40 | |||
| Office Hours | Thu 12:40-13:40 and by appointment | Cla | ssroom SH 324 |
| Office | PFSA 316G | Course LMS | Canvas |
Introduction
This course offers an introduction to open source tools and practices for geographic information system (GIS) applications. Much of today’s world is dominated by “big data”, data science, machine learning, and artificial intelligence, and GIS is no exception. This requires that GIS students have the capacity to understand and analyze quantitative information. At the same time, there are increasing demands for openness and transparency in both GIS applications and GIS in science.
This course is designed to equip students with the skills and practices to fully engage with, and contribute to, these developments. It relies on open source software which is essential since it provides access to free, high-quality, cross-platform tools. At the same time, free availability does not necessarily translate into skill acquisition, and a key goal of this course is to flatten the learning curve for students discovering these new tools and practices.
Organization
The course is structured as a blending of a lecture and studio. In the lecture component of the course, the literature surrounding the course project will be discussed and evaluated to shape the empirical project which will form the key focus of the second half of the semester. In the studio component of the course, students will be exposed to open source tools (software), technologies (github, CI/CD), and practices (collaborative development) in GIS projects.
Learning Outcomes
By the end of the semester, students will
- acquire competency in using open source software and tools for their own GIS research projects
- gain experience in collaborative open source and open science best practices
- apply these skill sets to contribute to a reproducible empirical GIS project
Prerequisites
GEOG 484 or equivalent.
Courses numbered 500 through 599 are in the upper division and are also acceptable for advanced degrees when taken by students admitted to graduate standing;
Credit Hours and Course Work
This is a 3-unit course. The definition of a unit is one credit hour:
One hour of direct faculty instruction and a minimum of two hours of out-of-class student work each week for approximately 15 weeks for one semester or equivalent amount of work over a different amount of time;
As such, you should allocate six hours of time per week for working on course related materials outside of our in-class meetings.
Grading
Exercises
A series of exercises will be assigned that provide opportunities to further develop skills in open source tooling and practices. Each student will be evaluated individually on all four exercises. You can view these exercises as practice for the skills that go into the final project.
Exam
There will be one exam on week 11 covering the materials from the first part of the course.
Project
The last 5 weeks of the course are organized as a studio in which each student will contribute to a final project that requires the application of the various open source technologies, packages, and practices that are introduced.
Rubrics for peer evaluation of project work will be given out in week 14.
Quizzes
Starting the second week of sessions, quizzes will be given on the assigned reading material. Quizzes will run through week 10 of the semester. Quizzes are graded on a pass/fail basis. Quizzes will be given during the first 5 minutes of a class meeting.
Tokens
Each student is provided with three tokens at the beginning of the semester. Tokens can be used in the following ways:
- One token can be used for a one-week extension on an exercise.
- One token can be used to resubmit an exercise that does not demonstrate competency.
- Two tokens can be used to request a make-up date for the exam. (Requests have to be submitted by March 14 5pm.)
Each token that remains unused by May 2 will be counted as a passed quiz.
Final Course Grade
Your final course grade is determined using specification grading. Your course grade will be based on the quality and quantity of the work that you submit that is evaluated to be of an acceptable level of quality. The acceptable level of quality demonstrates competency in the concepts and methods covered in the course.
There is a two-step process for determination of your final course grade at the end of the semester:
- Using your exercises, project and quiz grades, your base grade is determined.
- Using your exam grade to determine if your base grade is adjusted upwards, downwards or remains as your final grade.
For Step 1, the base grade is determined as follows
| Base | Hurdles |
|---|---|
| A | Demonstrates competency on 4 of 4 exercises |
| Obtained a valued contributor in evaluation | |
| Pass 90% of the quizzes | |
| Group project achieves competency in all 10 areas | |
| B | Demonstrates competency on 3 of 4 exercises |
| Obtained a team player ranking in evaluation | |
| Pass 80% of the quizzes | |
| Group project achieves competency in 8 areas | |
| C | Demonstrates competency on 2 of 4 exercises |
| Obtained a free rider ranking in evaluation | |
| Pass 70% of the quizzes | |
| Group project achieves competency in 6 areas | |
| D | Demonstrates competency on 1 of 4 exercises |
| Obtained a missing in action ranking in evaluation | |
| Pass 60% of the quizzes | |
| Group project achieves competency in 4 areas | |
| F | Failure to clear all D-level hurdles |
Your base grade is the highest grade for which you clear all the hurdles. For example, a student who demonstrated competency on 3 of the four exercises, worked on a group project that achieved competency in 9 of the 10 areas, passed 85% of the quizzes, and obtained a free rider ranking in their peer evaluation would have a C base grade.
For Step 2, your final course grade is determined as follows:
- If you earn at least 85% on the exam, you will obtain a “+” for your grade. So a B base grade would become a B+ course grade, and so on (Note: SDSU does not record A+ grades).
- If you score between 70-85% on the exam, your base grade becomes your course grade.
- If you score between 50% and 69% on the exam, you will obtain a “-” for your grade. So an A base grade becomes an A- course grade, a B base grade becomes a B- course grade, and so on.
- If you score less than 50% on the exam, your course grade will drop one level: An A base grade becomes a final B course grade.
Course Schedule (Planned)
| Week | Dates | Topic | Readings |
|---|---|---|---|
| 1 | Jan-18 | Course Introduction | |
| 2 | Jan-23 | Accounts | |
| Jan-25 | Computational Environment | ||
| 3 | Jan-30 | Pandas | pandas |
| Feb-01 | GeoPandas | geopandas I | |
| 4 | Feb-06 | Geovisualizaiton | choropleths |
| Feb-08 | Exercise 1 | ||
| 5 | Feb-13 | Projections | projections |
| Feb-15 | Spatial Joins | spatial joins | |
| 6 | Feb-20 | Spatial Inequality | spatial inequality |
| Feb-22 | Exercise 2 | ||
| 7 | Feb-27 | GeoSnap | geosnap |
| Feb-29 | Isochrones | isochrones | |
| 8 | Mar-05 | Accessibility | accessibility |
| Mar-07 | Exercise 3 | ||
| 9 | Mar-12 | Clustering | clustering-clusteringII |
| Mar-14 | Spatial Clustering | regionalization | |
| 10 | Mar-19 | Geodemographics | geodemographics |
| Mar-21 | Exercise 4 | ||
| 11 | Mar-26 | Review | |
| Mar-28 | Exam | ||
| Apr-02 | Spring Break | ||
| Apr-04 | Spring Break | ||
| 12 | Apr-09 | Projects Portfolio, git | Project preferences due (midnight) |
| Apr-11 | github and remotes | Project teams defined | |
| 13 | Apr-16 | Project Studio | |
| Apr-18 | Project Studio | Project proposals due (5pm) | |
| 14 | Apr-23 | Project Studio | |
| Apr-25 | Project Studio | ||
| 15 | Apr-30 | Project Studio | |
| May-02 | Project Studio | ||
| 16 | May-07 | Final Exam Week | Presentations |
Additional Readings
As we are learning Python through GIS, and assuming students already posses an understanding of GIS fundamentals, supplementary readings on Python should be consulted for further understanding:
“Think Python: How to Think Like a Computer Scientist” is an insightful and approachable guide designed for beginners looking to learn Python programming. Authored by Allen B. Downey, this book simplifies the process of learning Python by breaking down its core concepts into manageable sections, making it ideal for those new to programming. Emphasizing the importance of computational thinking, the book gradually builds from simple topics, like variables and basic operations, to more complex subjects such as data structures, algorithms, and debugging. Throughout the text, Downey encourages a problem-solving approach, reinforcing key programming concepts with practical examples and exercises that challenge the reader to think critically and apply their learning. This book is renowned for its clear, concise, and reader-friendly style, making the often-daunting task of learning a new programming language both manageable and engaging.
The book “A Whirlwind Tour of Python” by Jake VanderPlas can also be accessed online for free. . This book is a fast-paced introduction to essential features of the Python language, aimed at researchers and developers who are already familiar with programming in another language.
Policies
Accommodations
If you are a student with a disability and are in need of accommodations for this class, please contact Student Ability Success Center at (619) 594-6473 as soon as possible. Please know accommodations are not retroactive, and I cannot provide accommodations based upon disability until I have received an accommodation letter from Student Ability Success Center.
Privacy and Intellectual Property
Student Privacy and Intellectual Property: The Family Educational Rights and Privacy Act (FERPA) mandates the protection of student information, including contact information, grades, and graded assignments. I will use Canvas to communicate with you, and I will not post grades or leave graded assignments in public places. Students will be notified at the time of an assignment if copies of student work will be retained beyond the end of the semester or used as examples for future students or the wider public. Students maintain intellectual property rights to work products they create as part of this course unless they are formally notified otherwise.
Academic Integrity
The SDSU student academic integrity policy lists violations in detail. These violations fall into eight broad areas that include but are not limited to: cheating, fabrication, plagiarism, facilitating academic misconduct, unauthorized collaboration, interference or sabotage, non-compliance with research regulations and retaliation. For more information about the SDSU student academic integrity policy, please see the following: https://sacd.sdsu.edu/student-rights/academic-dishonesty.
Code of Conduct
As course instructor, I am dedicated to providing a harassment-free learning experience for all students, regardless of gender, sexual orientation, disability, physical appearance, body size, race, religion, or choice of operating system. All course participants are expected to show respect and courtesy to other students throughout the semester. As a learning community we do not tolerate harassment of participants in any form.
All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery are not appropriate in this course.
Be kind to others. Do not insult or put down other students. Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate for this course.
Students violating these rules may be asked to leave the course, and their violations will be reported to the SDSU administration.
This code of conduct is an adaptation of the SciPy 2018 Code of Conduct.