CP_101_2023_Summer_Syllabus

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1 CYPLAN 101: Introduction to Urban Data Analytics Summer 2023 Department of City and Regional Planning, University of California, Berkeley Instructors ● Flavia Leite, PhD Student, DCRP ( flavia_leite@berkeley.edu ) ○ Office Hours: by appointment ● Taesoo Song, PhD Student, DCRP ( taesoo.song@berkeley.edu ) ○ Office Hours: by appointment Reader ● Cheng-Kai Hsu, PhD Student, DCRP ( ckh4618@berkeley.edu ) When: Mondays, Wednesdays, and Fridays 9:00 a.m.-11:00 a.m. Where: Zoom (class is held virtually) Link: https://berkeley.zoom.us/j/92483776565?pwd=aXpkVTVGZjRwL2FTdUdzUWNlK0 hIdz09 Course Description: CYPLAN 101 introduces students to the systematic analysis of urban data. This relies on critical thinking with regard to economic, social, and environmental outcomes, from air pollution to housing density. Accordingly, this course will teach students approaches to collecting, analyzing, modeling, and interpreting quantitative and qualitative data used to inform urban planning and policymaking. Students will also be introduced to theory and critical discourses on topics such as big data, open data, privacy, and modern-municipal governance. Students will be expected to engage with the technical, theoretical, and ethical implications associated with these subjects in both lecture and laboratory sections. The course will introduce students to Excel and programming in R, an open-source software, as well as other tools and techniques for urban data analysis.

2 Prerequisites: CYPLAN 101 reserves seats for CED majors and others from around campus (e.g., Data Science majors); others can enroll with the instructor's permission. No prior statistics coursework is assumed. This class provides a foundation to pursue further undergraduate data science courses at UC Berkeley. For Urban Studies, CYPLAN 101 satisfies one of the four additional City Planning courses for Upper Division Urban Studies Core. For the City Planning minor, CYPLAN 101 satisfies one of the four additional City Planning courses for Upper Division courses under List 1. For SED, CYPLAN 101 can count as an Upper Division outside SED major for Fall 2016 admits and later. Course Requirements Assignments will involve the use of data and software available online, with links or files provided by the teaching staff. You will need to use your personal computer for labs and assignments, and have reliable internet access. If you have any issues with hardware or internet access, please consider Berkeley’s Student Technology Equity Program (https://studenttech.berkeley.edu/devicelending). Labs sessions will introduce students to Excel, Social Explorer, CARTO, and R programming using RStudio, among other resources. Statement on Academic Integrity Any test, visualization, discussion post, assignment, or written material submitted by you and that bears your name is presumed to be your original work that has not previously been submitted for credit in another course. You may use words or ideas written by other individuals in publications, websites, or other sources, but only with proper attribution. If you are not clear about the expectations for completing an assignment, taking an exam, or citing work, be sure to ask the teaching staff and/or consult the UC Library guide . You should also keep in mind that as a member of the campus community, you are expected to demonstrate integrity in all of your academic work and be evaluated on your own merits. The consequences of cheating and academic misconduct — including a formal discipline record and possible loss of future opportunities — are not worth the risks. Anyone caught cheating on a quiz in this course will receive a failing grade in the course and will also be reported to the University Center for Student Conduct. Note on ChatGPT You may not use ChatGPT or other AI software for any work during the course, unless explicitly asked to do so by the instructors . Statement on Accommodations for Students with Disabilities If you have been issued a letter of accommodation from the Disabled Students Program (DSP), please contact Flavia and Taesoo as soon as possible to work out the necessary arrangements. If you need an accommodation and have not yet seen a Disability

3 Specialist at the DSP, please do so as soon as possible. Statement on Scheduling Conflicts Please notify Flavia and Taesoo by the second week of the term about any known or potential extracurricular conflicts (such as religious observances, graduate or medical school interviews, or university-related travel). We will try our best to help you with making accommodations but cannot promise them in all cases. Course Structure Class Participation, Reading Responses, and Lab Exercises Lectures will be delivered synchronously on Mondays and Wednesdays. Students are expected to attend lectures and actively contribute to discussion. You should be prepared to share your thoughts during the class. Reading responses should react critically to the assigned material, and be posted to bCourses by 8pm the day before l ecture . The instructor will attempt to incorporate reading responses during lecture in order to stimulate discussion. There are eight assigned lab exercises, which include an asynchronous video tutorial, and a troubleshooting session with us held during class time on Fridays. Lab completion is optional but strongly recommended, particularly because they relate directly to the other course assignments. Lab troubleshooting sessions on Fridays can also be used for general office-hour questions. Module 1: Introduction to Urban Data During this module, students will explore fundamental data applications in urban planning, and gain skills in working with Census and economic data and static data visualization. Module 2: Mapping the City In the course’s second module, students will learn different tools to make digital maps, and gain an understanding of the essential elements of cartography (contrast, extent, legibility, etc.), how to use a Geographic Information System (GIS) platform - CARTO, and how to create story maps that combine multimedia content with maps. Students will also learn the fundamentals of the R programming language and how to visualize geographic data using R. Module 3: Big Data In the course's final module, students will use the knowledge acquired in earlier modules to explore data science in the context of contemporary urban issues. Sessions will cover topics such as ‘big data’, surveillance and privacy, and citizen-led participation. Students will also gain skills regarding real-time and crowd-sourced data collection and use, as well as in interactive data visualization.

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4 Course Materials Text: Singleton, Spielman, and Folch. 2018. Urban Analytics. Thousand Oaks, CA: Sage (recommended for reference but not required) . Readings, lecture slides, and other course materials will be posted on bCourses. You may also visit http://www.cp101.org , which includes material from previous versions of the course. Assignments Students are required to submit short reading responses prior to each lecture on the assigned readings (two paragraphs max.). A more detailed description of the other assignments is posted on bCourses. In the first assignment, students will explore neighborhood change through an in-depth analysis of U.S. Census data. The reports will feature both visualizations and text describing this change and putting it into broader contexts. This assignment will be conducted in groups of up to three students. Students will also work on a multi-part class project to answer an urban research question by identifying and analyzing multiple datasets. The class project will be conducted in groups of up to three students and is divided into the following three components: 1. A 2-page proposal outlining your research question, data, and analytical approach (Assignment 2) 2. A “story map” depicting preliminary findings with maps, accompanying text, and other visualizations (Assignment 3). 3. A final paper answering the research question(s) with a fully developed narrative supported by data analyses (Assignment 4). We will provide a list of research questions, data sources, and methods that students can choose from for their project. Due dates: ● Assignment 1: Friday, July 14 ● Assignment 2: Friday, July 21 ● Assignment 3: Monday, July 31 ● Assignment 4: Friday, August 11 ● Quiz: Friday, August 11 Quiz There will be one quiz at the end of the course. Students will answer a number of multiple choice and short-answer questions, as well as several open-ended essay questions. The quiz will assess students’ comprehension of the applications of urban data analytics, course readings and lecture material, potential ethical dilemmas, and critical thinking.

5 Grading Students will complete four group assignments and take one quiz at the end of the course. Students will be expected to read journal articles, book chapters, and watch and listen to podcasts/videos before lecture. Students are asked to submit reading responses on bCourses before lecture. Your grade will be calculated based on the following: ● Class Participation (5%) ● Assignment 1: Neighborhood profile (10%) ● Assignment 2: Class project (1st deliverable), Research Question, Data, & Methods (10%) ● Assignment 3: Class project (2nd deliverable), Story Map (15%) ● Assignment 4: Class project (final deliverable), Paper (25%) ● Reading Responses (15%) ● Quiz (20%) ——————————————————————————————————————— Module 1: Introduction to Urban Data Week 1, June 21-25 Lecture Session 1: Course Orientation & Introduction to Urban Analytics (Wednesday, June 21) / Instructor: Flavia Leite, Taesoo Song ● Singleton, Spielman, and Folch, 2018. Chapter 1, "Questioning the City through urban analytics.” ● Kim, A. M. (2018, June 5). Satellite images can harm the poorest citizens. The Atlantic. Boston . https://www.theatlantic.com/technology/archive/2018/06/satellite-images-can- harm-the-poorest-citizens/561920/ Lecture Session 2: US Census Data (Friday, June 23) / Instructor: Flavia Leite ● Singleton, Spielman, and Folch, 2018. Chapter 2, "Sensing the city.” ● US Census Bureau (Feb 27, 2020). The Importance of the American Community Survey and the 2020 Census. YouTube. https://youtu.be/gTY3402FZ-k ● Alba, Richard (Jun 11, 2015). The Myth of a White Minority. The New York Times. Week 2, June 26-30 Lecture Session 3: Statistics for the American Community Survey (Monday, June 26) / Instructor: Flavia Leite

6 ● Jurjevich et al., 2018. Navigating Statistical Uncertainty: How Urban and Regional Planners Understand and Work with the American Community Survey (ACS) Data for Guiding Policy. Journal of the American Planning Association, 84(2), 112-126. ● Cochran, Abby. 2020. Stats for CP 101 ( Video Link ) Optional: ● Field, A., Miles, J., Field, Z., 2012. "Chapter 2: Everything you ever wanted to know about statistics (well, sort of)." Discovering Statistics using R. Sage publications. [Read from pg 36-49] ● For reference: U.S. Bureau of the Census, TO. 2009. “A Compass for Using and Understanding American Community Survey Data.” https://www.census.gov/content/dam/Census/library/publications/2009/acs/AC SResearch.pdf Lecture Session 4: Static Data Visualization (Wednesday, June 28) / Instructor: Flavia Leite ● Tufte, 1983. The Visual Display of Quantitative Information. Graphics Press. Chapter 2, "Graphical Integrity," pg. 53-77 ● Few, 2012. Show Me the Numbers: Designing Tables and Graphs to Enlighten. 2nd ed. USA: Analytics Press. Chapters 11, 13, pp. 257-270; 295-306. Lab Session 1: Excel Fundamentals using ACS Data (Friday, June 30)/ Instructor: Flavia Leite Week 3, July 3 - July 7 Lecture Session 5: Intro to the Longitudinal Employment-Household Dynamics Data (Monday, July 3) / Instructor: Taesoo Song ● Abowd, Haltiwanger, & Lane, 2004. Integrated longitudinal employer-employee data for the United States. American Economic Review, 94(2), 224-229. ● Singleton, Spielman, and Folch (2018) Chapter 6 "Explaining the city," pg. 97-113 Lecture Session 6: Neighborhood Indicators: The Urban Displacement Project ( Wednesday, July 5) / Instructor: Taesoo Song ● Singleton, Spielman, & Folch, 2018. Chapter 5, “Differences Within Cities” ● Chapple & Zuk, 2016. “Forewarned: The Use of Neighborhood Warning Systems for Gentrification and Displacement” Cityscape: A Journal of Policy Development and Research, 18(3), 109-130 ● Urban Displacement Project ( link ) [Explore] Lab Session 2: LEHD data ( Friday, July 7)/ Instructor: Taesoo Song

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7 Module 2: Mapping the City Week 4, July 10-14 Lecture Session 7: Spatial Data & GIS Fundamentals (Monday, July 10) / Instructor: Taesoo Song ● Singleton, Spielman, and Folch, 2018. Chapter 4, "Visualizing the city." ● Lambert and Zanin, 2020. “Practical Handbook of Thematic Cartography.” Chapter 1, Geometrical Data. Lecture Session 8 – Introduction to Story Mapping (Monday, July 12) / Instructor: Taesoo Song ● Lambert and Zanin, 2020. “Practical Handbook of Thematic Cartography.” Chapter 7, Other Cartographic Presentations. ● Review the following Story Map examples: ○ Mapping Segregation in DC ( link ) ○ Creating a Neighborhood-Change Zoning Plan for Spruce Hill ( link ) ○ Katrina+10: A Decade of Change in New Orleans ( link ) Lab Session 3: Mapping with CARTO (Friday, July 14) / Instructor: Taesoo Song Week 5 - July 17-21 Lecture Session 9: Introduction to "Big Data" (Monday, July 17) / Instructor: Flavia Leite ● Foster et al., 2017. “Introduction.” Pp. 1-19 in Big Data and Social Science: A Practical Guide to Methods and Tools. Boca Raton, FL: Taylor & Francis Group. ● Boyd and Crawford. 2012. “Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon.” Information, Communication & Society 15 (5) Lab Session 4.1: Introduction to R (Wednesday, July 19) / Instructor: Flavia Leite Optional: ● Field, A., Miles, J., Field, Z., 2012. "Chapter 3: The R environment." Discovering Statistics using R. Sage publications. pp 62-92. Lab Session 4.2: Data Wrangling and Intro to Data Visualization in R (Friday, July 21) / Instructor: Flavia Leite

8 Week 6 - July 24-28 Lecture Session 10: Introduction to Mapping in R (Monday, July 24) / Instructor: Taesoo Song ● No readings assigned Lab Session 5-1 & 5-2: Mapping in R: Census API, `sf` package, and Projections (Wednesday, July 26) / Instructor: Taesoo Song Lab Session 5-3 & 5-4: Mapping in R: Spatial Queries and Data-driven Mapping (continued) (Friday, July 28) / Instructor: Taesoo Song Module 3: Big Data and Analytics Week 7, July 31-Aug 4 Lecture Session 11: Data and Ethics in Urban Studies/Planning (Monday, July 31) / Instructor: Flavia Leite ● Zook et al., 2017. "Ten Simple Rules for Responsible Big Data Research." PLoS Comput Biol 13(3) ● Crawford, 2013. “The hidden biases in big data.” Harvard Business Review Optional: ● Johnson J.A. From open data to information justice. Ethics Inf. Technol. 2014;16:263–274. doi: 10.1007/s10676-014-9351-8 Lecture Session 12: “Smart Cities” (Wednesday August 2) / Instructor: Flavia Leite ● Jacobs, K. (2022). “Toronto wants to kill the smart city forever.” MIT Technology Review. Link ● Batty, M. (2016). How disruptive is the smart cities movement? Environment and Planning B: Planning and Design, 43(3), 441–443 . Optional: ● Hollands, R. G. 2008. “Will the Real Smart City Please Stand up?: Intelligent, Progressive or Entrepreneurial?” City 12 (3): 303–20. Lab Session 6: Web Scraping / Instructor: Flavia Leite (Friday, August 4) Week 8, August 7-11 Lecture Session 13: Volunteered Geographic Information (Monday, August 7) / Instructor: Taesoo Song ● Jiang, Bin, and Jean-Claude Thill. 2015. “Volunteered Geographic Information: Towards the Establishment of a New Paradigm.” Computers, Environment and

9 Urban Systems, Special Issue on Volunteered Geographic Information, 53 ( September): 1–3. ● Boeing, Geoff, and Paul Waddell. 2016. “New Insights into Rental Housing Markets Across the United States: Web Scraping and Analyzing Craigslist Rental Listings.” Journal of Planning Education and Research. Lecture Session 14: Complex Urban Modeling: Machine Learning (Wednesday, August 9) / Instructor: Taesoo Song ● Domingos, 2012. “A few useful things to know about machine learning.” Communications of the ACM, 55(10), 78-87 ● Foster et al., 2017. "Machine Learning." Pp. 147-186 in Big Data and Social Science: A Practical Guide to Methods and Tools. Boca Raton, FL: Taylor & Francis Group. [skim] Session 15: Quiz (Friday, August 11) ● Quiz from 9am -12am (3 hours)

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