Course Syllabus

Instructor: Gayatri Kawlra (gk2444@columbia.edu)
Teaching Assistant: Lance Mikhail Punay (lance.punay@columbia.edu)
Lecture and hands-on lab: Tuesdays 3:00pm – 5:00pm
Location: UP Planning Computer Lab
Office hours:  Instructor: Tuesdays: 12.00pm - 1pm at the UP lounge. Please check the calendar and sign up here https://calendly.com/gkawlra/office-hours-iudi-fall-2023 or by appointment via email. TA: Thursdays: 4pm – 5pm, no appointment needed. 

Course Description

Digital information makes up the fabric of spaces we inhabit. As cities undergo technological transformations, we are presented with new opportunities to measure, understand and predict changes to our communities—places where we live, work, and interact. This has led to a host of novel concepts and disciplines such as urban science, big data, smart cities, urban informatics, and civic analytics—all of which seek to further our understanding of digitizing human societies. Planners today have the potential to make digital information actionable through computational analysis and visualization, allowing them to engage in new types of practices including founding urban startups and participating in data advocacy. Given these ever-present shifts, it is imperative for planners to develop the capabilities to collect, analyze, and communicate new urban challenges using stores of big data. This course seeks to facilitate an inquiry into our digitally encoded geographies and give students an insight into the potential of big data and analytics to support responsive, effective and critically engaging urban frameworks.

The curriculum engages the role of technology and computational methodologies within the planning process. The primary goals of the course are to familiarize students with cutting-edge computational methods and their applications in addressing real-world challenges—such as climate adaptation or managing public health emergencies like the COVID-19 outbreak. Central to the course are themes of data acquisition, numerical analysis, mapping and spatialization, data visualization, interactive techniques, and civic technology applications. Students will be immersed in key concepts, questions, software applications, and analytical techniques vital for deciphering various data sources and applications. In parallel, we will engage policy and design questions surrounding the creation and use of urban data. Beyond theory, students will embark on a final project, synthesizing these technical and theoretical concepts into a comprehensive study based on an informed research question.

Learning Objectives

Students taking this class will spend most of their time learning tools essential for working with big data, including the Python programming language. We will learn the technical skills of data acquisition, including querying APIs and data scraping, create and manage databases, utilize common libraries to analyze data (Pandas), and design compelling visualizations. Assignments will challenge you to think about urban problems through an analytical lens, and importantly, they are designed to encourage you to consider the communicability of these findings for the communities in which we engage through professional practice. While the course does not aim to produce experts, its vision is to equip students with the foundational knowledge to begin to formulate urban planning questions, and to navigate the methods by which to answer them.

Throughout the semester, students will consider the ethical implications of new urban technologies and data, fostering a reflective approach to urban informatics and future practice. Critical perspectives will be introduced through weekly readings and responses. The final project will test students’ ability to pose a nuanced research question using a contemporary case study within the context of these debates.

PLA6619 Introduction to Urban Data and Informatics I_Syllabus2023-1.pdf