Module 3 — Time

Richard Chou
Data Mining the City 2022
3 min readMar 22, 2022
Pedestrian Crowding Simulation of Penn Station Manhattan | KPF UI

Module #3 — Simulating Change

Designers and planners often presents “visions” of the future. While good storytelling and beautifully crafted renderings or maps help us convey our ideas, there is a lot of room for improvement in how we present the “change” that’s envisioned. Typical data visualization techniques such as graphs and heatmaps can document positive (or negative) changes through time clearly, yet the static and abstract nature can fail to adequately show the whole picture of an urban condition. This is where simulations and models shine — they allow us to perform analysis and visualize contextual data in a consistent, continuous timeframe.

For this module, you will create a simulation showing a temporal change related your final project, depicting a set of data through a period of time (a 15-minute walk, an hour of a busy neighborhood, transit usage over throughout the course of a day, a population growth within a decade, etc.) Working in your final project groups, this temporal visualization should be part of your project deliverable. The next three classes will cover multiple types of simulation and visualization methods, and will be expected to adopt one of the techniques to your project.

Due Date: April 13

Modules

Post

Submit your post to the class publication by April 13 (Weds) before class and include the following:

  • A Catchy Title + Authors Name
  • A short description of the simulation that you’ve created.
  • An “animated gif” of your simulation — take advantage of Grasshopper’s “Animate slider” functionality to create an animation that demonstrates change — whether its a continuous simulation over time (a hour, a day) or comparative analysis across larger time frames (before, after).
  • Simulation Model — briefly explain the type of simulation model used (trip-based, activity-based, agent-based), and why the model was chosen for your project. In addition, explain the assumptions or behaviors that are used (amenity demand, routing factor, agent behavior, etc.)
  • Data Sources — as always, list all your data sources for your reference in the future!

Inspiration

Commuter Trip Model | Pedestrian Flow Simulation (KPF UI)
Adaptive Transit Landscape | Emily Po
Urban Flooding Simulation (Urban Systems Lab) | Wind Simulation (Procedural)
Fish Schooling (Violet Whitney) | Agent Movement (MIT)
Travel Isochrone (Graphhopper)

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Richard Chou
Data Mining the City 2022

I am passionate about developing data-driven design strategies for urban design and city building of the next century.