University of California - Winter Quarter 2019
Instructor: Sergio Rey
Class Meetings: Tu/Th 3:40-5:30pm, Watkins 2101
Office Hours: Th 1:00-2:00pm, Center for Geospatial Sciences, Rivera 159
This course introduces the fundamental concepts of geographic information systems (GIS), geographic information science (GIScience), spatial data, and applications of spatial analysis in the social sciences and public policy.
The course takes an explicitly computational thinking approach to its pedagogy. Students are introduced to computational concepts and tools that are increasingly important to public policy and social science research that engages with geospatial data. By adopting these tools, students acquire a deeper engagement with, and mastery of, the substantive concepts.
In the scope of a 10-week quarter course we can only introduce a handful of the key concepts and methods relevant to GIS for Public Policy. As such, the course is not intended as an exhaustive treatment. Instead, the goal is that students will acquire an understanding of the more common and useful methods and practices, and use the course as an entry point for further engagement with the field.
At the end of this course students will:
-
possess a sound understanding of fundamental spatial concepts and theory;
-
know how to locate, import, manipulate, display, and analyze geographical data in open source computational tools;
-
have the ability to apply GIScience concepts and methods in public policy and social science research.
Graduate standing or consent of instructor.
We will using open source geospatial software throughout the course together with Jupyter Notebooks, and Python as our scripting language. No prior programming experience is assumed and all computational concepts are presented in a self-contained manner.
All software for the course will be made available through JupyterHub a web-based framework. Students wishing to install these materials on their own machines will be given instructions to do so, but this is not required.
Primary readings supporting the course will be assigned from:
de Smith, M.J, Goodchild, M.F. and Longley, P.A. (2018) Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. Winchelsea Press.
Available for reading on-line or for purchase.
Readings are to be done prior to the date listed. Computational Materials will be posted the day of the meeting.
| Week | Date | Topic | Reading | Exercises |
|---|---|---|---|---|
| 1 | 01-08 | Introduction to GIScience | 1, 2 | |
| 01-10 | Introduction to Jupyter and Python | Python for GIS | ||
| 2 | 01-15 | Spatial Thinking | 2 | |
| 01-17 | Python II | Exercise 1 Out | ||
| 3 | 01-22 | Spatial Data in Python | 4 | Data |
| 01-24 | Mapping and Geovisualization in Python | Mapping | ||
| 4 | 01-29 | Data Structures and Models | 4.2 | Shapely |
| 01-31 | Geoprocessing | Spatial Joins | Exercise 1 Due | |
| 5 | 02-05 | Project proposals, Spatial Analysis | 5, 5.1, 5.2 | |
| 02-07 | Project proposals, EDA for Spatial Data | Exercise 2 Out | ||
| 6 | 02-12 | Guest Lecture: Elijah Knaap | 5.5 | |
| 02-14 | Project Resource: Github and Slack | |||
| 7 | 02-19 | Exercise 2, Projects | 6 | |
| 02-21 | Clipping, Projects | Exercise 2 Due | ||
| 8 | 02-26 | Spatial Joins, Projects | 5.4 | |
| 02-28 | Projects | Exercise 3 | ||
| 9 | 03-05 | Projects | ||
| 03-07 | Projects | |||
| 10 | 03-12 | Exercise 3, Projects | ||
| 03-14 | Next Steps | Exercise 3 Due |
For those not enrolled in the course, you can interact with these computational materials at the course Binder: 
Your course grade will be based on a series of exercises designed to build your GIS skills, together with a group project and course participation. Details on the course project will be given out towards the middle of the quarter.
| Component | Points |
|---|---|
| Exercise 1 | 20 |
| Exercise 2 | 20 |
| Exercise 3 | 20 |
| Course Project | 35 |
| Participation | 5 |
The course may be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.
The UCR 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 UCR student academic integrity policy, please use the following web link http://conduct.ucr.edu/policies/academicintegrity.html
Qualified students with disabilities who will require disability accommodations in this class are encouraged to make their requests to me at the beginning of the quarter either during office hours or by appointment. Note: Prior to receiving disability accommodations, verification of eligibility from the Student Disability Resource Center is required. Disability information is confidential.
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 PBPL273.
Students violating these rules may be asked to leave the course, and their violations will be reported to the UCR administration.
This code of conduct is an adaptation of the SciPy 2017 Code of Conduct.