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, 12:10-2:00 pm
    • 250 Olson
    • Live, in-person
    • Combination of lecture, discussion, activities and lab. See course agenda for schedule
  • Labs:
    • A01: Thursday, 4:10-5:00 pm
    • A02: Thursday, 5:10-6:00 pm
    • 2020 Esau Hall
    • Live, in-person

Instructor

  • Dr. Noli Brazil
  • Contact: nbrazil.at.ucdavis.edu
  • Office: 2325 Hart Hall
  • Office hours: Wednesday from 3:00-5:00 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 40 minutes are open drop in. Zoom link is located here and on Canvas home page.

Teaching Assistant

  • Yan Yan
  • Contact: yayan.at.ucdavis.edu
  • Office: 2420 Hart
  • Office hours: TBD

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.

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.

We will also introduce the program ArcGIS Online Story Maps.

Course Requirements

  1. Assignments (50%)

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://crd150.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 been tested 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 activities (5%)

There will be two in-class activities that will focus on the use of Artificial Intelligence (AI) in learning how to code. We strictly prohibit the use of AI in quizzes and written portions of your assignments and final project is not permitted. You are allowed to use AI in the coding portions of your homework assignments, but only to help aid in your learning of the material. The two in-class activities will demonstrate how to view AI not as a tool to replace your learning, but to enhance it. Attendance is required unless you provide a viable excuse ahead of the designated class. In-class activities cannot be made up. You will be required to use your laptops for the in-class activities. If you do not own one, let us know at the beginning of the quarter.

  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.

Date Class Topic Readings Assignment Quiz Activity Project
31-Mar Lecture Intro to class. Data analysis framework. Handout 1; Duarte & deSouza
2-Apr Lecture Intro to R
3-Apr Lab Intro to R
7-Apr Lecture Data wrangling Handout 2
9-Apr Lecture Data wrangling in R HW 1
10-Apr Lab Data wrangling in R
14-Apr Lecture Intro to the U.S. Census Handout 3 A 1
16-Apr Lecture Working with U.S. Census data in R HW 2
17-Apr Lab Working with U.S. Census data in R
21-Apr Lecture Exploratory data analysis Handout 4
23-Apr Lecture Exploratory data analysis in R HW 3
24-Apr Lab Exploratory data analysis in R
28-Apr Lecture Intro to spatial data Handout 5 Q 1
30-Apr Lecture Spatial data in R HW 4
1-May Lab Spatial data in R
5-May Lecture Exploratory spatial data analysis Handout 6 A 2
7-May Lecture Exploratory spatial data analysis in R HW 5
8-May Lab Exploratory spatial data analysis in R
12-May Lecture Big data and open data Handout 7
14-May Lecture Working with open data in R HW 6
15-May Lab Working with open data in R
19-May Lecture Measuring segregation Handout 8
21-May Lecture Measuring gentrification Handout 9 HW 7
22-May Lab Segregation and gentrification in R Proposal
26-May Lecture Memorial Day holiday
28-May Lecture Story Maps using ArcGIS online Lung-Amam & Dawkins; Davis et al.  HW 8 Q 2
29-May Lab Story Maps using ArcGIS online
2-Jun Lecture Guest Lecture
4-Jun Lecture Guest Lecture
5-Jun Lab TBD
11-Jun StoryMap due 5:00 pm StoryMap
12-Jun 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