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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

Recommended readingsΒΆ

Todo

Add literature.

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Tutorial 3 - Trajectory data mining in Python

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Exercise 3

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