Syllabus
CRD 230 - Spatial Methods in Community Research
Winter 2023
The following is an abridged version of the course syllabus. A full course syllabus can be found on the Canvas class website.
Lecture and Lab
- Lecture: Monday 10:00-11:50 am, 166 Hunt
- Lab: Wednesday 10:00-11:50 am, 1137 PES
Lecture and lab are held in person.
Instructor
Dr. Noli Brazil
- Contact: nbrazil.at.ucdavis.edu
- Office: 2325 Hart Hall
- Office hours: Monday and Wednesday from 4:30-5:30 pm or by appointment, Zoom or in person. Please sign up for a slot here. Out of courtesy to other students, please do not sign up for more than two 10-minute blocks. If you do, I will keep only the first two blocks. The last 20 minutes are open drop in. Zoom link is located on Canvas home page.
Course Objectives
In this course, students will gain
- A theoretical understanding of the role of space and place in community-level phenomenon
- An understanding of what kinds of spatial data are available and where to find them
- Proficiency in spatial analytic tools (R) to
- Manage and process spatial data
- Descriptively examine spatial data
- Run spatial models for statistical inference
- An understanding of how these methods are employed in community research
Course Format
Most weeks will adhere to the following format: The first meeting of the week will contain (1) me lecturing about methods and (2) us discussing how the week’s readings employ the methods. The second meeting of the week will take place in lab whereby we will apply the methods learned in lecture/discussion using real data in R. All students are expected to actively participate, which means not only being present, but reading all material and engaging in class discussions. This is a survey course for different techniques and approaches in dealing with these data in R.
Required Readings
Required reading material is composed of a combination of the following
- Journal articles and research reports.
There is no single official textbook for the course. Instead, I’ve selected journal articles and research reports.
- My handouts
For most topics, in lieu of an article or book chapter, I will provide lecture handouts on Canvas in advance of the assigned class.
There are also a set of weekly optional readings listed at the end of the syllabus that provide applications of the methods.
Additional Readings
The other major course material are lab guides, which will be released at the beginning of Tuesday’s lecture on the class website. Many of the R lab guides will closely follow two textbooks. These textbooks are not required, but are great resources.
The first textbook provides the foundation for using R
- (RDS) Wickham, Hadley & Garret Grolemund. (2017). R for Data Science. Sebastopol, CA: O’Reilly Media.
The textbook is free online at: http://r4ds.had.co.nz/introduction.html
The second textbook covers spatial data in R
- (GWR) Lovelace, Robin, Jakub Nowosad & Jannes Muenchow. Geocomputation with R. CRC Press.
The textbook is free online at: https://geocompr.robinlovelace.net/
Course Software
R is the only statistical language used in this course, as it has become an increasingly popular program for data analysis in the social sciences. We will use RStudio as a user friendly interface for R. R is freeware and you can download it on your personal laptop and desktop computers (along with RStudio, which is a user friendly interface for R). Note that although the course does not require students to have experience with R, this class does not devote too much time introducing students to the program. In other words, this is a not an introduction to R programming. The lab guides will provide as much detail as possible to execute tasks and functions, but you will likely run into tasks that will require you to go beyond the guides. My suggestion is to (1) look up RDS or GWR as they are excellent resources and (2) if (1) fails search online. As such, you are expected to do as much independent learning of the software as I teach in the labs.
Course Requirements
- Assignments (4 x 15%: 60%)
Students are required to complete four homework assignments during the quarter which are due approximately every 2 weeks. The assignments will largely correspond to the material covered in lectures and labs. Each assignment will ask students to apply methods in R. Collaboration of ideas among participants is encouraged, but the assignments must be completed independently. For each assignment, you will need to submit an R Markdown Rmd and its knitted file on Canvas. Complete assignment guidelines can be found here: https://crd230.github.io/hw_guidelines.html.
Late submissions will be deducted 10% per 24 hours until 72 hours after the submission due time. After 72 hours your submission will not be graded. No exception unless you provide documentation of your illness or bereavement before the due date. If you cannot upload the assignment on Canvas due to technical issues, you must email it as an attachment to me by the submission due time.
- Course project (40%)
Students will conduct a research project using methods discussed in class on a topic of their choosing. A full description of the project can be found in the file final_project_description.pdf located on Canvas. All students must submit a Prospectus. Students have two final project options: (1) StoryMap presentation and a Policy Brief; (2) Final paper and a presentation of any format. Students will present their projects in person during a time/day TBD.
Course Agenda
The schedule is subject to revision throughout the quarter. Please see the full syllabus for a more detailed version of the agenda
| Date | Topic | Readings | Assignment | Project |
|---|---|---|---|---|
| 9-Jan | Introduction to class; Introduction to U.S. Census | Handout 1 | ||
| 11-Jan | Introduction to R | |||
| 16-Jan | MLK Holiday | |||
| 18-Jan | Working with U.S. Census data in R | |||
| 23-Jan | Introduction to spatial data | Handout 2 | ||
| 25-Jan | Introduction to spatial data in R | HW 1 | ||
| 30-Jan | Big data and Open data | Handout 3 | ||
| 1-Feb | Working with Open and Big data in R | |||
| 6-Feb | Spatial accessibility | Handout 4 | ||
| 8-Feb | Spatial accessibility in R | HW 2 | ||
| 13-Feb | Community vulnerability | Handout 5 | ||
| 15-Feb | Community vulnerability in R | |||
| 20-Feb | Presidents Day Holiday | |||
| 22-Feb | Spatial Autocorrelation in R | Handout 6 | HW 3 | |
| 27-Feb | Spatial regression | Handout 7 | Proposal | |
| 1-Mar | Spatial regression in R | |||
| 6-Mar | Social Network Analysis | Handout 8 | ||
| 8-Mar | Social Network Analysis in R | HW 4 | ||
| 13-Mar | Introduction to StoryMaps | Lung-Amam & Dawkins (2019 | ||
| 15-Mar | TBD |
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Website created and maintained by Noli Brazil