Syllabus 2022

Data Mining the City

A4834–1 / Spring 2022
Richard Chou
Wednesday 7PM — 9 PM
Avery 114

The ubiquity of digital technologies embedded in cities and urban-dwellers makes urban data an integral part of the design and planning of cities. Urban analytics provides an opportunity to design cities that are responsive to the needs of its constituents.

This course will focus on the application of data tools to model, analyze, and simulate the urban environment. Students will develop a critical understanding of data science concepts and issues of practice of urban data models, as well as technical skills in data processing, analytical simulations, and visualization. As a class, we will explore new creative methods to better understand the dynamics between urban systems and create more insightful decisions for our future cities.

Course Topics

Problem Solvers not Data Crunchers

The main technical language of the course is Grasshopper in Rhino — a visual programming platform. The course will focus on leveraging geospatial analysis plug-in Urbano and DecodingSpaces, as well as revisit basic GIS concepts and workflows via ArcGIS/QGIS. Previous knowledge of these software are not required, though the content of the course requires perseverance. Students are required to regularly submit coursework, attend classes and submit a final comprehensive project. Class time will include lectures, discussions, and labs/tutorials.

Learning Objectives

• Basic understanding of working with geospatial data in Grasshopper
• Understanding of core data science concepts, methods, and limitations
• Experimentation and design in a code-driven environment
• Performing a goal-oriented design research project using urban data

Question, Method, Insights, Action

Students will learn methods and techniques of performing urban data analysis across 3 modules: Population, Space, and Time. Students will work individually for the first two modules to create workflows that model specific population behaviors, urban context, and spatial context. By module 3, students will team up to combine their analysis modules into a comprehensive final project based on a structured thesis.

Modules

• Population: Defining a population behaviors and personas
• Space: Constructing urban context and spatial characteristics
• Time: Performing analysis and creating design scenarios

Medium Submissions

Students will submit a Medium post to the course website for each module by midnight on the due date.

Course Schedule

Module #1 — Population (Due Feb. 09)

Week 1 | Jan 19 | Spatial Analysis & Urban Data

• Course Introduction
• Lecture: Data, Spatial Data, & Data Science

Week 2 | Jan 26 | Assumptions & Modeling

• Lab: Data Sources, Collecting & Processing Data, GIS Basics
• Module #1: Defining a Synthetic Population (Due Feb.09)

Week 3 | Feb 02 | Experience & Behavior

• Lab: Grasshopper Basics & Best Practices
• Workshop: Module #1

Week 4 | Feb 09 | Data Bias, Subjectivity, and Limitation

• Discussion: How do we use data models ethically?
• Guest Lecturer: Snoweria Zhang

Module #2 — Space (Due Mar. 09)

Week 5 | Feb 16 | Location, Location, Location

• Lab: Importing Spatial Data into Grasshopper, Data Processing
• Module #2: Constructing an Urban Sim. Environment (Due Mar. 09)

Week 6 | Feb 23 | Urban Network: Space Syntax & Graph Theory

• Lab: Network Analysis & Basic Routing
• M.Arch Midterm Review Feb 21 — Mar 4

Week 7 | Mar 02 | Experiential & Environmental Factors

• Workshop: Final Project (brief, brainstorm & form groups)
• Workshop: Module #2

Week 8 | Mar 09 | Student Presentation

• presentation moved due to studio travel. Instead class will be a work session.

Week 9 | Mar 16 | Spring Break

• No Class!

Module #3 — Time (Due Apr. 13)

Week 10 | Mar 23 | Mobility Modeling: Demand & Trip Generation

• Lab: Amenity Demand, Trip Simulation & Analysis
• Module #3: Performing Analysis and Simulations (Due Apr. 13)
• Presentation: Students groups present research topics and goals.

Week 11 | Mar 30 | Agent Based Modeling

• Workshop: Agent Based Simulation with PedSim
• Guest Lecturer: Peng Wang

Week 12 | Apr 06 | Visualizing Time

• Lab: Slider Animation, Export, Design Scenarios
• Workshop: Module #3

Week 13 | Apr 13 | Data in Practice

• Discussion: What is the role of data analytics in the design and planning practice?
• Presentation: Students groups present Module #3 Results.
• Guest Lecturer: Nico Azel

Final Project (Due May. 02)

Week 14 | Apr 20 | Desk Crit

• M.Arch Final Review May 21 — May 29

Week 15 | May 02 (Mon) | Final Presentation

• Location and Time TBD

Grading

• Attendance/ Participation: …………..20%
• Module #1: .……………………………15%
• Module #2: .……………………………15%
• Module #3: .……………………………15%
• Final Project: …………………………..35%

Welcome to the Class!