Course description
Performative Modelling
This course is the last in our “Spatial Computational Thinking” program. This “Performative Modelling” course focuses on evaluating alternative spatial models to support evidence-based decision making. You will learn methods for calculating various spatial performance metrics related to the built environment that can be used for comparative analysis of design options.
This course will build on the previous two courses that covered procedural and generative modelling. In this course, you will switch modes from generating to evaluating spatial performance. Thus, you will be creating procedures for evaluating alternative spatial models with respect to a set of performance indicators. This will once again require an increase in coding complexity, together with a new set of strategies for managing that complexity.
In this course, you will learn how to create your own reusable and customised function libraries. You will use this powerful technique to create a set of generative and performative functions. The generative functions will be used to generate alternative spatial models. The performative functions will be used to evaluate various performance metrics. You will then combine these functions, evaluating each spatial model against each performance metric.
The modelling exercises and assignments during this course will mainly focus on evaluating alternative spatial models for buildings within the urban environment. A site will be selected, and procedures will be developed for calculating performance metrics using morphological and raytracing analysis methods. The various metrics will then be weighted and aggregated, in order to allow alternative options to be easily compared.
Upcoming start dates
Who should attend?
Prerequisites:
- Completion of Course-1: Procedural modelling
- Course-2: Generative modelling of 'Spatial Computational Thinking' Professional Certificate program.
Course delivery details
This course is offered through The National University of Singapore, a partner institute of EdX.
4-6 hours per week
Costs
- Verified Track -$149
- Audit Track - Free
Certification / Credits
What you'll learn
Learning algorithmic thinking:
- How to evaluate spatial models using morphological attributes and performance indicators
- Use abstraction as a way of selectively exposing the parameters that are most relevant to the problem being investigated
- Use encapsulation as a way of managing problem complexity
Learning performative modelling:
- Analysing performance indicators using morphological analysis and raytracing analysis
- Understanding morphological analysis: plot ratio, compacity ratio, passive zone proportion, etc
- Understanding raytracing analysis: sky view factor, sun exposure factor, viewsheds, etc
- Evaluating alternative spatial models based on multiple performance metrics
- Strategies for supporting decision making using weighted performance metrics
- Integrating non-spatial data formats into spatial information modelling workflows
- Strategies for data visualization
Learning coding:
- Understanding how to break down large procedures into a set of smaller functions
- Understanding how to document functions to support reuse
- Understanding how to create and share libraries of functions that can be reused
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