About this course

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.
Completing the three courses that make up the “Spatial Computational Thinking” program will provide you with the fundamental knowledge and skills required to tackle a wide variety computational design challenges using digital technologies.

At a Glance:
Institution: NUS
Subject: Design
Level: Advanced
Prerequisites:
Completion of Course-1: Procedural modelling, and Course-2: Generative modelling of ‘Spatial Computational Thinking’ Professional Certificate program.
Language: English
Video Transcript: English
Associated programs:
Professional Certificate in Spatial Computational Thinking
Associated skills:Business Metrics, Computational Thinking, Comparative Analysis, Decision Making, Built Environment, Viewshed Analysis, Computational Design, Ray Tracing, Performance Metric

What You’ll Learn:
About this course

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.
Completing the three courses that make up the “Spatial Computational Thinking” program will provide you with the fundamental knowledge and skills required to tackle a wide variety computational design challenges using digital technologies.