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Course information

  • Introduction to the course
  • General info
  • Learning goals
  • Grading
  • Course environment
  • Communicating with Slack
  • License and terms of usage
  • Attribution

Week 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

Week 2

  • Lesson overview
  • Human wellbeing and capabilities
  • Network analytics and spatial accessibility modelling
  • Tutorial 2.1 - Shortest path analysis
  • Tutorial 2.2. Accessibility analysis: Calculating travel time matrices in Python
  • Recommended readings
  • Exercise 2

Week 3

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

Week 4

  • Lesson overview
  • Economic inequalities and growth
  • Spatial econometrics
  • Tutorial 4 - Spatial Regression in Python
  • Recommended readings
  • Exercise 4

Week 5

  • Lesson overview
  • Agent-based simulation with spatial data
  • SDS in Water resource management
  • Flood forecasting
  • Recommended readings

Final Assignment

  • Instructions
  • Repository
  • Suggest edit
  • .rst

Recommended readings

Recommended readings#

  • Banister (2008). The sustainable mobility paradigm. (link)

  • Creutzig et al. (2019). Upscaling urban data science for global climate solutions (link)

  • Dijst et al. (2018). Exploring urban metabolism—Towards an interdisciplinary perspective (link)

  • European Commission. (2018). Indicators for Sustainable Cities. (link)

  • Lobo et al. (2020). Urban Science: Integrated Theory from the First Cities to Sustainable Metropolises (link)

  • UN (2016) New Urban Agenda (link)

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Tutorial 3 - Trajectory data mining in Python

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Exercise 3

By Henrikki Tenkanen

© Copyright 2023, Henrikki Tenkanen, Dept. of Built Environment, Aalto University.