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Course information

  • Introduction to the course
  • Install Python + libraries
  • Recommended readings
  • License and terms of usage
  • Attribution

Contents

  • SDS & Sustainable mobility
  • Tutorial 1.1 - Meet Git
  • Tutorial 1.2 - Spatial analysis with Python
  • Tutorial 2 - Spatial Network analysis
  • Exercises 1-2
  • Repository
  • Suggest edit
  • .rst

Recommended readings

Contents

  • Sustainability
  • Spatial Data Science

Recommended readings#

Thinking of the breadth of the scope in this course (sustainability + SDS), there are countless of valuable articles, books and other resources that are “good reads”. Here, we list only a fraction of resources that we have found useful or which have been important in these fields in one way or another. Note: These are not obligatory readings for the registered students, but good places to start if the topics interest you more broadly. We will specify in the Exercises if there are any articles that you should read in addition to the lecture materials (unlikely during this year).

Sustainability#

  • Rockström, J. et al. (2009). A safe operating space for humanity. Nature.

  • Steffen, W. et al. (2015). Planetary boundaries: Guiding human development on a changing planet. Science (80-. ). 347, 1259855.

  • Steffen, W. et al. (2015). The trajectory of the Anthropocene: The Great Acceleration. Anthr. Rev. 2, 81–98.

  • Steffen, W. et al. (2018). Trajectories of the Earth System in the Anthropocene. Proc. Natl. Acad. Sci. U. S. A.

  • Meyer, K. & Newman, P. (2018). The Planetary Accounting Framework: a novel, quota-based approach to understanding the impacts of any scale of human activity in the context of the Planetary Boundaries. Sustain. Earth 1, 4.

  • Sachs, J. et al. (2019). Six Transformations to achieve the Sustainable Development Goals. Nat. Sustain. 2, 805–814.

Spatial Data Science#

  • Singleton, A. & Arribas‐Bel, D. (2019). Geographic Data Science. Geogr. Anal. 1–15.

  • Wolf, L. et al. (2020). Quantitative geography III: Future challenges and challenging futures. Prog. Hum. Geogr. 1–13.

  • Yuan, M. (2020). Geographical information science for the United Nations’ 2030 agenda for sustainable development. Int. J. Geogr. Inf. Sci. 1–8.

  • Dodge, S. (2021). A Data Science Framework for Movement. Geogr. Anal. 53, 92-112.

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Install Python + libraries

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License and terms of usage

Contents
  • Sustainability
  • Spatial Data Science

By Henrikki Tenkanen

© Copyright 2022, Henrikki Tenkanen, Dept. of Built Environment, Aalto University.