Course information

Introduction to the course
General info
Grading
Course environment
Communicating with Slack
Pair programming
License and terms of usage
Attribution

Lesson 1

Lesson overview
What is Sustainability?
What is Spatial Data Science?
Tutorial 1.1 - Meet Git
Tutorial 1.2 - Spatial analysis with Python
Recommended readings
Exercise 1

Lesson 2

Lesson overview
Human wellbeing and capabilities
Network analytics and spatial accessibility modelling
Tutorial 2 - Spatial Network analysis
Recommended readings
Exercise 2

Lesson 3

Lesson overview
Sustainable Cities and Mobilities
Mobility analytics
Tutorial 3 - Trajectory data mining in Python
Recommended readings
Exercise 3

Lesson 4

Lesson overview
Economic inequalities, growth and green economy
Spatial Regression
Tutorial 4 - Spatial Regression in Python
Recommended readings
Exercise 4

Lesson 5

Lesson overview
Agent-based simulation with spatial data
Recommended readings
Exercise 5

Lesson 6

Lesson overview
Conservation planning & Spatial prioritization
Tutorial/Exercise 6 - Spatial prioritization with Zonation
Recommended readings

Final Assignment

Instructions
Grading criteria for the final assignment

Theme by the Executable Book Project

Mobility analytics¶

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Sustainable Cities and Mobilities

next

Tutorial 3 - Trajectory data mining in Python

By Henrikki Tenkanen
© Copyright 2020, Henrikki Tenkanen, Dept. of Built Environment, Aalto University.