This course will focus on the fundamentals of procedural programming for generating spatial models. You will learn how to code, using functions, data structures and control-flow statements. You will create procedures to generate geometric models with attribute data. By the end of the course, you will be able to write your own procedures for generating spatial information models.
About this courseSkip About this course
The first in our “Spatial Computational Thinking” program, this “Procedural Modelling” course will focus on the fundamentals of procedural programming in 3D. You’ll learn to write computational procedures using data structures and control-flow statements to automate the production of 3D models.
During the course, you will learn a range of computational methods. These include general programming constructs such as using ‘while’ loops, ‘for-each’ loops, ‘if-else’ conditions, as well as writing your own custom functions. In addition, you will also learn to use two key data structures: list and dictionaries. And in the process, you will become familiar with the programming process: writing code, executing code and debugging code.
When creating modelling procedures, you will use a range of different modelling functions. You will also learn how geometric models can be augmented with an additional layer of semantic data. You will learn how geometric entities can be tagged with additional attribute values, and how these attributes can then be used for querying your models. You will also learn how to add attributes to define colour, materials, and other visual properties.
During this course, you will use Möbius Modeller, the modelling tool that is used throughout this “Spatial Computational Thinking” module. It is free and easy to use browser-based software to write algorithms for automatic generation and visualization of complex models with spatial information.
The modelling exercises and assignments during this course will start with a simple procedural approach to 2D and 3D patterns and will progress towards more complex geometries representing entities within the built environment such as building footprints, building facades and staircases.
The course prepares you for the next course in the “Spatial Computational Thinking” program, focusing on generative modelling of more complex types of spatial information models.
At a glance
What you'll learnSkip What you'll learn
Learning algorithmic thinking:
* What is Spatial Computational Thinking?
* The fundamentals of procedural programming
* How procedures can be used to manipulate spatial information models
* An understanding of the coding process: write, execute, and debug
* How semantics can be used to augment geometric models
* The difference between geometry, topology, and attributes
* How query languages can be used to extract data from models
Learning procedural modelling:
* Fundamentals of geometric objects: points, polyline and polygons
* Using modelling functions for creating and modifying geometric objects
* Managing connectivity between geometric objects
* Modelling with geometry, topology, and collections
* Attaching attribute data to geometry, topology, and collections
* Querying and filtering data in the model using attributes
* Coding using an Integrated Development Environment (IDE)
* Fundamentals of variables and simple data types
* Fundamentals of operators: assignment, arithmetic, relational, logical
* Fundamentals of programming with libraries of functions
* An introduction to the list and dictionary data structures
* Understanding function parameters, arguments and return values
* Manipulating control flow using ‘while loops’, ‘for loops’ and ‘if conditions’
* Understanding variable assignment and variable scope
* Creating custom local functions to avoid repetitive code
* Strategies for writing and debugging code
Learning Möbius Modeller
* Overview of Möbius Modeller as a development environment
* The Möbius Modeller user interface
* Creating computational flowcharts with parameters
* Working with the console and different type of viewers
* Creating flowcharts with multiple nodes and parameters
* Flowchart patterns, using in-series nodes or in-parallel nodes
* Strategies for communicating between nodes within a flowchart
* Submitting assignments using Möbius Modeller
About the instructors
Frequently Asked QuestionsSkip Frequently Asked Questions
What software will I need?
* The only software you need is a recent version of any Chromium-based web browser (such as Google Chrome, Microsoft Edge, Opera, or Brave). During the course, we will use a free and open-source software app called Möbius Modeller. Even after completing the course, you will be able to continue using this app for free.
What hardware will I need?
* You do not need any specialized hardware to complete the exercises in the course. A recent mid-range laptop will be sufficient. A laptop with a dedicated graphics card will result in a smoother user experience.
Do I need to know any programming languages before I start?
* No, this course is designed for beginners and we will step you through all the programming required.
Will I be able to write code after completing this program?
* Yes. You will learn procedural programming, using typical imperative programming-language constructs. You will also learn how to create computational procedures for manipulating spatial data in diverse ways.
Will I be able to share the computational models that I create?
* Yes. The models that you create (either during the course or after) can be shared either by exporting the models in other formats or by publishing them on the internet as interactive web pages. Publishing a model is straightforward and is one of the techniques that you will learn.
What is the passing grade for the course?
* An overall average for all assignments of 70% is required to pass the course.
Do I need to achieve 70% on each assignment?
* No, you need an average grade for all assignments of 70%. This means you can do poorly or miss an assignment as long as you do well enough on other assignments to achieve 70% overall.
How will my computational modelling assignments be graded?
* Most of your computational modelling assignments will be graded using an automated online grader. For each assignment, you will be given specific instructions on the model that you need to create. You will upload your answer model, and within a few seconds, you will receive the result, with feedback. If the model you uploaded is not correct, you will have multiple chances to try again. For the last assignment of the course, you will be required to create your own model from scratch. This final assignment will be manually graded.