CP_101_2023_Summer_Syllabus

.pdf

School

De Anza College *

*We aren’t endorsed by this school

Course

36A

Subject

Civil Engineering

Date

Oct 30, 2023

Type

pdf

Pages

Uploaded by BaronScorpion15204

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.

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.

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version