Discrete Urban Space + Cognitive Urbanism:
6ft rules of public space in the Age of COVID-19
Main Lecturers: Namju Lee + Jung Hyun Woo
Unit Instructors: Seung Ah Choi + Soomeen Hahm
1. Unit Brief
This course investigates diverse quantitative methods to measure and analyze emerging urban spatial issues of COVID-19 relevant to contemporary urban planning and design practice. The course is based on spatial network analytics approaches that aim to offer students learning tools (Rhino GH and Python) and understanding the data and the process for integrating pedestrian flow information and decision making with urban planning and design solutions. It structures into four experiments:
Pedestrian flows by the shortest path
Pedestrian route choice analysis: how far people are willing to walk
Pedestrian route choice analysis: attractiveness vs. deviation
Spatial activity patterns, indoor & outdoor: safe distance of 6ft (2m)
NNA(Numerical Network Analysis) Toolbox, Rhino Grasshopper
3. Aim and Scope
After the lectures and workshop, students can do:
Develop computational design concepts and language for basics.
Use network analysis techniques for pedestrian flows, route choice, location accessibility, mapping, and data visualization.
Apply these techniques into practices in architecture, urban planning, design, and policy.
4. Expected Workshop Outcome
Each of the four experiments applies different analytic technologies and produces a group presentation. Here are the lists that students are required to develop during the workshop.
Address the given problems and research questions in reports. (500 words)
Explain your analytic method and 2-3 hypotheses with clarity. (250 words)
Analyze the strengths and weaknesses of computed spatial analysis and findings. (500 words)
Submit on-site counting data (Rhino, csv)
Visualize your understanding of data collection, computational analysis results, and design ideas with creating your cartographies (image)