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, 2:10-4:30 pm
    • 158 Olson
    • Live, in-person
    • Combination of lecture, discussion, activities and lab. See course agenda for schedule
  • Labs:
    • A01: Thursday, 9:00-9:50 am
    • A02: Thursday, 10:00-10:50 am
    • 1320 Walker Hall
    • Live, in-person

Instructor

  • Elise Zufall
  • Contact: ezufall.at.ucdavis.edu
  • Office: Zoom!
  • Office hours: Mondays: Zoom appointment only (with at least 1 hour notice) from 11:45 am - 1:00 pm, and Zoom drop-in from 1:00 pm - 1:45 pm. Please sign up for a slot here. Out of courtesy to other students, please do not sign up for more than one 15-minute block. If you do, I will keep only the first block. Zoom link is located here, password 234812, and on Canvas home page.

Teaching Assistant

  • Celine John Philip
  • Contact: cjphilip.at.ucdavis.edu
  • Office: Zoom (link here)
  • Office hours: Fridays, 9:00-10:30 am

Course Objectives

In this course, students will gain

  • A foundation in critical thinking and reasoning skills based on data
  • Skills in acquiring data from a wide range of reliable public and private sources
  • An understanding of the differences between spatial and nonspatial data
  • Skills in data cleaning and management
  • An understanding of how to appropriately present nonspatial data in tables and graphs and spatial data in maps
  • Skills in descriptive analysis of nonspatial and spatial data
  • Proficiency in data analytic tools, specifically R
  • An understanding of how these methods are employed in community research

Course Format

The course is organized into two phases.

  • Part 1: Analyzing communities using nonspatial data. Topics include descriptive statistics, exploratory data analysis, data presentation, and visualization. As the major source of national community-level data in the United States, the U.S. Census will be covered extensively.

  • Part 2: Analyzing communities using spatial data. Topics include big data, government open data, point pattern analysis, spatial clustering, residential segregation, point pattern analysis and story mapping.

Lecture Format

  • Most Monday lectures will be a combination of lecture and graded/ungraded in-class exercises/questions covering the week’s substantive topics. The ungraded exercises during lecture are meant to be less about learning how to do a task in R, and more about deepening your understanding of the week’s substantive topics. Expect that many of these exercises and questions be reflective of those found in the quizzes. See the agenda for when the required in-class activities will take place. Monday lectures will not be recorded.

  • Most Wednesday lectures will be a combination of some lecture covering the week’s topic but mostly computer sessions covering the week’s lab guide, which will be released on the course website every Wednesday before class. I will ask you to bring your laptops to Wednesday lectures in order to follow along. Not all lectures are expected to go the full class period. Wednesday lectures will be recorded and posted on Canvas.

  • On Mondays and Wednesdays, there will also be check-in questions for attendance purposes. Please always bring a device that can either scan the QR code or enter the URL for the check-in questions. Check-in questions are the only time during which phones are acceptable to use in class.

Lab Format

The TA will cover lab guide material that we were not able to get to during the Wednesday computer sessions. They will also provide additional guidance on higher level points and provide more refined assignment feedback and help. The lab guides provide hands on practice using real data. They will provide step-by-step instructions on executing specific tasks using a software program. Although you do not need to turn in lab guides for a grade, it is expected that you will go through each guide and master its contents.

Required Readings

Required reading material is composed of a combination of the following

  1. Journal articles and research reports.

  2. My handouts

There is no single official textbook for the course. Instead, I’ve selected journal articles and research reports. For most topics, in lieu of an article or book chapter, I will provide lecture handouts on Canvas in advance of the assigned class.

Additional Readings

The other major course material are lab guides, which will be released before the Wednesday lecture. Many of the R lab guides will closely follow two textbooks. These textbooks are not required, but are great resources. The first textbook covers the first part of the course (nonspatial data)

  • (RDS) Wickham, Hadley & Garret Grolemund. (2017). R for Data Science. Sebastopol, CA: O’Reilly Media.

The textbook is free online at: https://r4ds.had.co.nz/

The second textbook covers the second part of the course (spatial data)

  • (GWR) Lovelace, Robin, Jakub Nowosad & Jannes Muenchow. (2019) Geocomputation with R. CRC Press.

The textbook is free online at: https://r.geocompx.org/

Course Software

R is the 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. If you do not own a laptop, let us know at the beginning of the quarter.

We will also introduce the program ArcGIS Online Story Maps.

Course Requirements

  1. Assignments (48%)

Assignments will be released on the lab website each week Wednesday morning and will be due the following Wednesday morning on Canvas. Assignment questions are located at the end of each lab guide. They will contain a combination of programming tasks and theoretical questions that you will need to answer on your own. For each assignment, you will need to submit an R Markdown Rmd and html file on Canvas. Complete assignment guidelines can be found here: https://ezufall.github.io/crd150_2026.github.io/hw_guidelines.html.

In order to get full credit for each assignment, you will need to

  1. Show the correct statistical results for a given question (e.g. map, table, statistics).
  2. Show the code producing the results.
  3. Provide comments in your own words explaining what each line of code is doing
  4. Provide correct written answers.
  5. Submit an R Markdown Rmd file and its knitted html file on Canvas.

Note that assignments will get progressively harder, so it is important that you master the material each week as assignments will build on one another. 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. 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 the TA by the submission due time.

  1. Quizzes (20%)

There will be two quizzes that will test conceptual material covered in lecture and readings. The quizzes are open book and will be taken in class on your laptop 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 tested positive or have been exposed to COVID, and cannot take the test in class but can take it at home, we will provide accommodations to take the quiz during the same time as the rest of the class.

  1. In-class participation and attendance (7%)

Lecture and discussion attendance is required unless you provide a viable excuse ahead of the designated class. As part of your participation and attendance grade, you will be expected to complete in-class check-ins and activities. In-class check-ins and activities cannot be made up. You will be required to use your laptops or smartphone for the in-class activities. One absence per quarter will be excused without documentation, to accommodate illness. Use of technology not related to class may result in receiving a 0 for attendance & participation for the day.

  1. Final course project (25%)

The purpose of the final course project is to provide students the opportunity to apply the concepts and methods learned in class on a real-world problem of their choice. The project is an individual project. It will be completed in phases, which are designed to ensure progress throughout the quarter. The project will involve choosing at least one specific community (city or county) and answering a question about that community. You will (i) identify a community of interest (city or county with a population size in the top 100); (ii) identify a question you want to answer for that community; (iii) find some data that pertain to the community and topic of interest; (iv) organize those data so that you can analyze them; (iv) perform some analysis on the data; (v) present your results through a StoryMap; (vi) give feedback to your peers’ StoryMaps. More detailed information of project parameters are provided on Canvas in the document final_project_description.pdf in the Final Project folder on Canvas.

Other Information


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

Course Agenda


The schedule is subject to revision throughout the quarter. Please see the full syllabus on Canvas for a more detailed version of the agenda.

Week Date Class Topic Readings Assignment Quiz Project
1 5-Jan Lecture Intro to class. Data analysis framework. Handout 1; Duarte & deSouza
1 7-Jan Lecture Intro to R
1 8-Jan Lab Intro to R
2 12-Jan Lecture Data wrangling Handout 2
2 14-Jan Lecture Data wrangling in R HW 1 due
2 15-Jan Lab Data wrangling in R
3 19-Jan NONE HOLIDAY Handout 3
3 21-Jan Lecture Guest Lecture: Sara Ludwick. Intro to US Census. Working with U.S. Census data in R HW 2 due
3 22-Jan Lab Working with U.S. Census data in R
4 26-Jan Lecture Exploratory data analysis Handout 4
4 28-Jan Lecture Exploratory data analysis in R HW 3 due
4 29-Jan Lab Exploratory data analysis in R
5 2-Feb Lecture Intro to spatial data Handout 5 Q 1
5 4-Feb Lecture Spatial data in R HW 4 due
5 5-Feb Lab Spatial data in R
6 9-Feb Lecture Exploratory spatial data analysis Handout 6
6 11-Feb Lecture Exploratory spatial data analysis in R HW 5 due
6 12-Feb Lab Exploratory spatial data analysis in R
7 16-Feb NONE HOLIDAY Handout 7
7 18-Feb Lecture Measuring segregation HW 6 due
7 19-Feb Lab Segregation in R
8 23-Feb Lecture Big data and open data Handout 8
8 25-Feb Lecture Working with open data in R HW 7 due
8 26-Feb Lab Working with open data in R Proposal
9 2-Mar Lecture Story Maps using ArcGIS online
9 4-Mar Lecture Final project in-class workshop Lung-Amam & Dawkins; Davis et al.  HW 8 due Q 2
9 5-Mar Lab Story Maps using ArcGIS online
10 9-Mar Lecture Guest Lecture: Maryam Tasnif-Abbasi from DTSC Office of Brownfields
10 11-Mar Lecture Lab XC (attendance optional) quizzes returned
10 12-Mar Lab Lab XC (attendance optional) quizzes returned
Finals 18-Mar StoryMap due 5:00 pm StoryMap
Finals 19-Mar StoryMap eval due 5:00 pm StoryMap peer evals

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Website created and maintained by Noli Brazil and adapted by Elise Zufall