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 Times

  • Lecture:
    • Monday and Wednesday, 9:00-9:50, in-person
    • 1007 Giedt
  • Labs:
    • Monday and Wednesday, 11:00-12:50, in-person
    • 2212 Teaching and Learning Complex

Instructor

Dr. Noli Brazil

Contact: nbrazil.at.ucdavis.edu
Office hours: Monday and Wednesday from 4:10-5:10 pm or by appointment, Zoom or in-person. 2325 Hart Hall. 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 here and on the Canvas home page.

Teaching Assistant

Sara Ludwick

Contact: scludwick.at.ucdavis.edu
Office hours: Friday, 1:00-3:00 (in-person or Zoom). 2420 Hart Hall. Zoom link is located on Canvas and pdf syllabus.

Course Objectives

After successfully completing this course, you should:

  1. Have an understanding of what quantitative methods are used to study geographic phenomena
  2. Have an understanding of how and which quantitative methods are used in tackling geographical problems
  3. Have an understanding of the statistical underpinnings of each method
  4. Be capable of comprehending academic literature that uses quantitative methods
  5. Be able to employ quantitative methods using the software program R
  6. Feel empowered working with data
  7. Be prepared to take advanced courses in multivariate and spatial statistics

Course Format

The course is organized into the following phases.

  1. Statistical Foundation: We will cover core statistical concepts that will form the foundation to many of the methods covered in the class. Topics covered include sampling, confidence intervals, and hypothesis testing.

  2. Descriptive: We will cover methods of using data to describe geographic phenomena. We are not trying to make any causal claims, but are simply trying to explore the data and reveal associations. Topics covered include exploratory data analysis, linear regression, mapping, clustering, and point pattern analysis.

  3. Predictive: We will cover approaches of using data to predict geographic phenomena. We don’t care about description or about making causal claims, but about predictive quality and best fit. We will cover topics in Geostatistics.

Lecture

The Monday/Wednesday 9:00-9:50 am classes will be a combination of lecture and discussion covering the substantive topics from the week’s readings. There are readings for each lecture and they generally cover the mathematical underpinnings of the topics for that lecture

Lab

The Monday/Wednesday 11:00-12:50 pm labs will provide students the opportunity to apply the methods from the week’s readings on real data using the statistical software program R. Attendance is not mandatory. However, labs will provide an opportunity to gain assistance with lab guides and assignments from the TA and your classmates.

Required Readings

The primary reading material is composed of a combination of the following three:

  1. (OSU) O’Sullivan, D. and D. Unwin. 2010. Geographic Information Analysis, 2nd Edition, Wiley.

  2. (ISLR) James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.

  3. My Handouts

OSU will be used to cover most geographic methods and techniques. ISLR will be used to cover the predictive modelling portion of the course. Both books are available for hard copy purchase from the campus bookstore. Digital copies are uploaded on the Canvas website in the Readings folder. Several topics are not covered by the textbooks. As such, I will provide handouts covering these topics on Canvas in advance of the assigned class. At the end of each my handouts, I will provide additional references if you are interested in learning more, all of which will be available free on Canvas.

The other major course material are lab guides. The week’s guides will be released before lecture on Monday and will be located on the class GitHub website linked above.

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. R is freeware and you can download it on your personal laptop and desktop computers. We will use RStudio as a user friendly interface for R. Lab computers will also have R and RStudio.

Course Requirements

  1. Assignments (60%)

Assignments will be released on Canvas typically before Monday’s lecture. They will be typically due on Canvas one week later on the following Monday before lecture. For each assignment, you will need to submit an R Markdown Rmd and a knitted pdf or html file on Canvas. Some assignments will require you to also submit lab code/output as the questions will be embedded within the lab. Complete assignment guidelines can be found here. If you get stuck you can seek help from the TA, who will be available in the scheduled lab sessions and during office hours. We also encourage you to work with other students, but you must submit your own assignment.

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.

  1. Quizzes (20%)

There will be two 25 minute quizzes that will test conceptual material covered in lecture and readings. The quizzes are closed book and will be taken in class during their designated dates and will cover only the material covered since the last quiz. They will consist of short computational, multiple choice and short answer questions. You will not be expected to write or interpret R code. Make-up quizzes will be given ONLY in the case of extreme emergencies (severe illness, death in the immediate family) and when accompanied by appropriate documentation (e.g. doctor’s note). In the case of unexcused absences (travel plans, overslept, etc.), there are no make-up quizzes. If you have been tested or have been exposed to COVID, and cannot take the test in class but can take it at home, we will provide online accommodations to take the quiz during the same time as the rest of the class.

  1. Final (20%)

You will choose one of two options to fulfill your final submission (for full details see Canvas):

  1. Select a reading from the set of empirical readings that complement the course topics. Provide a reading response for each selected reading.
  2. Establish a research question and develop a data and methods proposal to answer the research question.

Other Information


Please see the full syllabus on the Canvas website for information regarding course communication, code of conduct, and grades.

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 Due Quiz
1-Apr Introduction to Class
3-Apr Qunatitative Research Framework
8-Apr Exploratory Data Analysis HW 1
10-Apr Statistical Inference
15-Apr Hypothesis Testing HW 2
17-Apr Linear Regression
22-Apr Linear Regression HW 3
24-Apr Logistic Regression
29-Apr Intro to Spatial Data HW 4 Quiz 1
1-May Maps, Patterns and Processes
6-May Point Pattern Analysis HW 5
8-May Spatial Autocorrelation
13-May Spatial Regression HW 6
15-May Predictive Modelling
20-May Variable Selection HW 7 Quiz 2
22-May Spatial Interpolation
27-May Holiday, no class
29-May Spatial Interpolation HW 8
3-Jun Regression Trees
5-Jun No Class HW 9

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