Numeric Network Analysis: Post-COVID-19 Urbanism, 6 ft rule

SA(Social Algorithms) 2020 Computational Design Workshop

INSTRUCTORS :
NJ Namju Lee / nj.namju@gmail.com / linkedin
Junghyun Woo / axuplatform@gmail.com / linkedin

Youtube Playlist — Eng

Youtube Playlist — Kor
Daum Brunch— link

SHORT DESCRIPTION:
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)

LEARNING OBJECTIVES:
After the lectures and workshop, students can do:

• Computational design concept 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.

KEY WORDS:
6ft, Daily Routine, Urban Mobility, Urban Design Quality, Walkability, COVID-19, Urbanism, Computational Geometry, Visualization, Urban and Architectural Data, …

TIMEZONE:
First Week
KST, 11 AM - 3 PM, 18(Tue) Aug, 2020
KST, 11 AM — 3 PM, 20(Thu) Aug, 2020

Second Week
KST, 9AM — 10AM, 24(Tue) — 28(Fri) Aug, 2020

PREREQUISITES:
We use multiple programs, including Rhino 3D (GH and Python), along with new experimental exercises. If you enjoy exploring quantitative analysis, this is the right course for you. After the workshop, you might discover a whole new view for urban planning, design, and built environments.

Please find the following link to access prerequisite materials about Python, data processing, and visualization:
—English Version — Long / Short playlist
— Korean Version — Daum Branch

For a short video tutorial on Correlation Coefficient to scatter plots, you may watch on Kahn Academy: — link

READINGS: — link
Gehl, J. (1987). Life between buildings: using public space. New York, Van Nostrand Reinhold. Pages .1–48.

Sevtsuk, A., Ratti, C., 2010, Does Urban Mobility Have a Daily Routine? Learning from the Aggregate

Data of Mobile Networks, Journal of Urban Technology, Volume: 17, Issue: 1, Pages: 41–60.

González, C. M., & Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453, 779–782.

Ewing, Reid, and Handy, Susan(2009). Measuring the Unmeasurable: Urban Design Qualities Related to Walkability. Journal of Urban Design, 14: 1, 65–84

WORKSHOP DESCRIPTION:

First Week
Lecture: 35% — 2.5 hours
Overview: 20% — 2 hours, overview projects and codes
Workshop: 45% — 3.5 hours, hand-on workshop

Second Week
Feedback -20mins
Homework -30mins

SYLLABUS:

First Week
Urban Network & Accessibility Analysis — link
1. Lecture, Urban Design Quality and Walkability
2. Lecture, Spatial Network Analysis in Transportation Geography
3. Workshop, NNA Toolbox — link/download

Discrete Urban Space & Connectivity
1. Lecture, Data and Design — link
2. Lecture, Computational Design Thinking for Designers — link
3. Lecture, Pipeline for Interaction, Data, and Geometry Visualization — link
4. Lecture, Discrete Urban Space and Connectivity — link
5. Lecture, Geometry as Data Structure and Visualization— link
6. Workshop Python Graph — download
7. Workshop, Pedestrian Volume Studies — link
Post-COVID-19 Urbanism — link

Second Week
1. Desk critic , Exercise, review
2. Workshop, trouble shooting … code support …

FINAL PROJECTS & EXERCISE:

1. Where is my nearest Park?
Keywords: shortest distance, partitioning, public space

2. Who visits the park the most? How long are you willing to walk?
Keywords: shortest distance, distance decay, number of visits, on-site survey, walking for leisure, Manhattan

3. How does the size of the park impact on the number of visitors?
Keywords: shortest distance, distance decay, number of visits, on-site survey, walking for leisure, Manhattan

4. The safe distance of 6ft (2m) for Park after COVID-19
Keywords: shortest distance, distance decay, number of visits, on-site survey, walking for leisure, Manhattan

REQUIREMENT:

Each of the four experiments applies different analytic technologies and produce a group presentation. Here are the lists that students are required to present 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 counting data (Rhino, CSV)
• Visualize your understanding of data collection, computational analysis results, and design ideas with creating your cartographies

POST-PRODUCTION:

• Publication
• Peer reviewed journal
• Online publication
• Online exhibition

TOOL
Numeric Network Analysis Tool — link/download

ADDITIONAL MATERIAL

Homework — Python Colab / Rhino Grasshopper
Python Graph — link
Seoul Data Visualization — link
Seoul Numerical Image — link

ThinkPython PDF
RhinoPython101

Data and Computational Design Materials

English Version — Long / Short playlist

Korean Version — Daum Branch

Example files — link