Data, Evidence, and Communication for the Public Good
USC PPD534 / Fall 2021 / 4 units
Email: boeing at usc dot edu
Office hours: Tue 16:30-17:30, RGL 301A
Classroom location and meeting times are listed online
TAs:
- Alycia Cheng (email: ascheng at usc dot edu, office hours: Thu 13:00-14:00, RGL 105 student lounge)
- Jaehyun Ha (email: jaehyunh at usc dot edu, office hours: Wed 13:00-14:00, RGL 105 student lounge)
This course provides you with a toolkit for telling stories with urban data. It will introduce basic coding, stats, and reasoning with evidence. The course takes a computational social science approach to working with data. It uses Python and Jupyter notebooks to introduce coding and statistical methods that students can reproduce and experiment with in real-time in the classroom. We start the semester with the basics of coding, then move on to data loading and analysis, then on to basic statistics, then hypotheses and the scientific method, and finally a critical assessment of smart cities and urban informatics.
Each week, students will be expected to:
- Complete and be prepared to discuss all assigned readings
- Complete and submit any assigned reading responses
- Attend the lecture
- Complete and submit any assigned group projects
The course has no specific prerequisites. Coding itself will be taught from the ground-up. However, this course requires patience and practice: learning to code will take lots of trial-and-error, self-direction, repetition, and experimentation on your part. You will get out of it what you are willing to put into it. Please note that this syllabus is a living document and may be updated by the instructor during the semester as needed.
- Write simple code to manipulate, analyze, and visualize urban data
- Understand how to use basic descriptive and inferential statistics to evaluate and interpret social science evidence
- Tell stories about public issues with a combination of text and visuals using data and evidence
- Evaluate the politics and ethics underlying how technology and innovation impact cities and planning processes
We are available if you need help throughout the semester and are happy to answer your questions. Please ask course-related questions in our course's Slack channel: you should expect a reply typically within two working days. If you have a sensitive personal matter to discuss, please ask by email. Office hours info is provided at the beginning of the syllabus.
If you have a question about class material, homework, or a coding/data problem: post on Slack or drop by the TA's office hours. If you have a specific question for the professor outside of those categories, please drop by his office hours to chat.
Given the nature of this course, we do expect a few things of you before you seek assistance with coding/data questions:
- Close all open programs, restart your computer, then try your task again
- Search Google and StackOverflow for the topic/problem (for example, the name of the function you're struggling with or the error message you are seeing)
- Go back through the relevant lecture materials to look for any insights
- Go back through the assigned reading materials to look for any insights
- Ask your teammates if they have any insight
If the above steps haven't solved your problem, post on Slack (or attend office hours) and include the following information:
- A detailed description of what you're trying to do, why, and how
- A complete minimal reproducible example of your code so far (never send screenshots of code/tracebacks)
- What you've already tried to do to solve your problem and what you have learned from it so far (specifically, explain the results of steps 1-4 above, including relevant links from StackOverflow etc)
We do not ask for this to be pedantic. Rather, we need it to be able to help you solve your problem.
Coursework will be based on free open-source software.
Copyrighted course reading materials are available via Blackboard for enrolled students to download. The course lectures assume that you have read the assigned readings prior to the class session and are now reasonably fluent in their contents and ready to discuss/debate them in class. Lectures are supplemental to the assigned reading and are of little value if you haven't taken the time to prepare in advance. So, before class, make sure you have completed the reading, taken thorough notes, and prepared any questions you may have about the material.
Per USC guidelines, for each course unit the university expects 2 hours of out-of-class student work per week. This is a 4 unit course. Therefore, you should expect an average of 8 hours of out-of-class work each week: please budget your time accordingly. The balance will vary from week to week, but will comprise a mix of individual reading, individual writing, and group data/code assignments. See the schedule below for assignment due dates and see the "assignments" folder for instructions. Active participation is expected (and graded) in the classroom.
Final grades will be weighted as follows:
- 20%: eight individual reading responses
- 30%: five group assignments
- 20%: individual midterm exam
- 20%: final group project
- 10%: active participation in classroom/Slack and team work
Assignments must be submitted via Blackboard by 23:59 pacific time on their due date. Late assignment submissions will be deducted one letter grade per day. Late final project submissions will not be accepted or graded. Please submit early to avoid any last-minute headaches such as slow uploads, weak internet connections, or temporary system outages. If you have any technical issues when submitting, contact USC IT. It is solely the student's responsibility to ensure that all submissions have gone through on time, so please visually confirm successful submission in the system. The timestamp in the submission system is our official record: if it says you're late, you're late. You can expect grades to be posted within two weeks. Grades are non-negotiable.
Group assignments leverage your diversity of skills and life experiences. We expect each of you to contribute to your group assignments in whatever way you can create proportional value: some will be better at code, others at writing, etc. We don't intend to hold your hand: coordination and delegation are challenging but these are necessary professional skills. Organize, collaborate, and communicate proactively with one another. There will be a formal group peer review at the end of the semester to help us assign participation grades.
Aug 26 - Introduction
We introduce the course, the syllabus, the semester's expectations and schedule, and set up the computing environment for coursework.
Readings to be completed prior to class:
- Boeing and Arribas-Bel. 2021. GIS and Computational Notebooks. In: The Geographic Information Science & Technology Body of Knowledge, edited by J.P. Wilson. Direct link.
- If you're on Windows, read this guide. If you're on Mac, read this guide.
- Conda user guide
Pre-Survey
Sep 2 - Research design and data collection
We introduce research design including qualitative and quantitative methods, discuss basic concepts and terms in statistics, introduce the US census and its methods, and discuss sources of data including the decennial census, the ACS, and government data portals.
Readings to be completed prior to class:
- Wheelan, Naked Statistics, intro + ch. 1
- Urdan, Statistics in Plain English, ch. 1. USC link.
- Macdonald. The American Community Survey. Journal of the American Planning Association. Direct link. USC link.
Reading response 1 due the night before class
Group assignment 1 due the following Wed
Sep 9 - Coding Bootcamp I
We introduce the basics of Python, a powerful programming language for data analysis, visualization, and software development. We work with Python via the Jupyter notebook, which lets you use Python in an interactive coding environment.
Readings to be completed prior to class:
- JupyterLab user guide sections on the interface and the notebook
- Downey, Think Python, ch. 1-3. Direct link
Sep 16 - Coding Bootcamp II
We introduce the basics of Python control: using loops, conditionals, and functions to control the logic and flow of your code's execution.
Readings to be completed prior to class:
- Downey, Think Python, ch. 4-7. Direct link
Group assignment 2 due the following Wed
Sep 23 - Data Cleaning and Descriptive Stats
We introduce the basics of loading and cleaning data, then discovering patterns in them with descriptive statistics.
Readings to be completed prior to class:
- Wheelan, Naked Statistics, ch. 2-3
- Urdan, Statistics in Plain English, ch. 2-3. USC link
Reading response 2 due the night before class
Sep 30 - Data Visualization
We introduce the foundational concepts and best practices of visualizing data for exploratory analysis: looking visually for summaries, patterns, and trends.
Readings to be completed prior to class:
- Tufte, Visual Display of Quantitative Information, ch. 1-3
- Rost, blog post on choosing colors
Reading response 3 due the night before class
Group assignment 3 due the following Wed
Oct 7 - Spatial Data
We introduce the foundational concepts of loading spatial data, projecting them, analyzing them, and mapping them.
Readings to be completed prior to class:
- Gimond, Intro to GIS and Spatial Analysis, ch. 1, 2, 9. Direct link.
- Wheelan, Naked Statistics, ch. 5, 7
Reading response 4 due the night before class
Oct 14 - No class
Group assignment 4 due October 13
Oct 21 - Mid-Term Exam
Exam will comprise a mix of multiple choice and short-answer questions, including some asking you to write short snippets of code.
Oct 28 - Qualitative Methods in Practice
We introduce qualitative methods including study design, implementation, qualitative analysis, and the role of qualitative methods in urban planning.
Readings to be completed prior to class:
- Creswell, J. W. and Cheryl N. Poth. 2018 (4th ed). "Chapter 2: Philosophical Assumptions and Interpretive Frameworks" in Qualitative Inquiry and Research Design: Choosing from Among Five Traditions . Thousand Oaks: Sage pp. 15-40.
- Isoke, Z. 2011. The politics of homemaking: Black feminist transformations of a cityscape. Transforming Anthropology, 19(2), 117-130.
- Optional: Miner, Horace. 1956. Body Ritual Among the Nacirema. De Gruyter.
Nov 4 - Social Science and the Scientific Method
We introduce social science, the scientific method, inference, prediction and explanation, and instrumentalism. We discuss the roles of qualitative and quantitative methods in constructing actionable knowledge.
Readings to be completed prior to class:
- Okasha, Philosophy of Science, ch. 1-4
Reading response 5 due before the start of class
Nov 11 - Inference and Uncertainty
We introduce a statistical framework for hypothesis testing, inference, confidence, and uncertainty. We discuss the limitations of this framework and how other methods, such as qualitative research, can help us build knowledge.
Readings to be completed prior to class:
- Wheelan, Naked Statistics, ch. 8-10
- Urdan, Statistics in Plain English, ch. 4-7. USC link
- Jurjevich et al, Navigating Statistical Uncertainty. Journal of the American Planning Association. Direct link. USC link.
- WSJ article (available on Blackboard) + CityObservatory response
Reading response 6 due before the start of class
Nov 18 - Statistical Models
We introduce specifying, estimating, interpreting, and reporting regression models.
Readings to be completed prior to class:
- Wheelan, Naked Statistics, ch. 4 + 11-13
- Urdan, Statistics in Plain English, ch. 8, 9, 13. USC link.
Reading response 7 due before the start of class
Nov 25 - No class
Group assignment 5 due Nov 24
Dec 2 - Smart Cities, Ethics, and Evidence-Based Planning
We introduce the social context and limitations of science as it applies to real-world urban planning practice, then critically engage the smart cities paradigm and the roles of civic tech, techno-utopianism, politics, power, and ethics. We discuss how qualitative and quantitative methods work together for evidence-based planning.
Readings to be completed prior to class:
- Okasha, Philosophy of Science, ch. 5, 7
- Kitchin, The Ethics of Smart Cities and Urban Science. Direct link. USC link.
- Mattern, A City Is Not a Computer. Places. Direct link.
Reading response 8 due before the start of class
In class: complete course evaluations and group peer assessments
Dec 8 - Final Group Projects Due
See the assignments folder for details and deadlines.
Any student requesting academic accommodations based on a disability or ongoing mental health concern is required to register with the Office of Student Accessibility Services (OSAS) each semester. A letter of verification for approved accommodations can be obtained from OSAS. Please be sure the letter is delivered to the instructor as early in the semester as possible, as the accommodation can only be implemented upon receipt of the letter. Visit OSAS's web site for more details. For further support, I encourage you to contact USC Support and Advocacy (uscsupport@usc.edu). To maintain fairness and equality for all students, extensions to due dates are only granted in accordance with these official accommodation letters. If you need to request a one-time emergency extension to an assignment (e.g., due to a major illness or to a death in the family) you must do the following proactively prior to its due date: 1) provide written documentation, such as an official doctor's note, explaining that you are unable to complete the assignment by its due date and 2) work out an extension with the instructor.
Recording a university class is forbidden without the express permission of the instructor and announcement to the class. Recording can inhibit future free discussion and thus infringe on the academic freedom of other students as well as the instructor.
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Make sure you review the student handbook for expectations on academic integrity, and never commit plagiarism. It is serious academic misconduct. In all your assignments, make sure you do not copy/paste any words, images, code, or other content written by another author (including the author of the piece to which you are responding) without quote marks and citation. If you use someone else's words, you must always use quote marks and cite them. If you refer to their ideas in your own words, you must cite them to make it clear whose ideas you're referring to. In a reading response, citing the reading's author inline is sufficient for us to understand the citation. In other contexts, use a formal reference to make your citation clear.
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