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san's Introduction

SAN - Spatial Analysis Notes

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Citation

If you use the material, code or processed data, you can give appropriate attribution by using the following citation:

@book{rowearribas-bel_smds21,
  author = {Rowe, Francisco and Arribas-Bel, Dani},
  title = {Spatial Modelling for Data Scientists},
  year = 2022,
  url = {gdsl-ul.github.io/san/},
  doi = {10.17605/OSF.IO/8F6XR},
}

The theme for the website was adapted from David Keyes' repository for his book R without Statistics

san's People

Contributors

darribas avatar fcorowe avatar matthew-law avatar

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san's Issues

[New] References

  • create a general reference list at the end of the document
  • remove references from the start of each chp

Assignment datasets

Decided that each assignment will be a compilation of micro-assignments based on each block. That way, the person responsible for each block prepares questions based on data that are different than those used in the chapter, but that can be recycled across the micro-assignments for different chapters.

Assignment datasets will include:

  1. AirBnb for Paris: Points, Spatial Econometrics - @darribas
  2. Census for area other than Liverpool (Introduction, MLM, GWR, Space-Time Analysis) - @fcorowe
  3. Alternative flows (provided by @fcorowe, prepared by @darribas)

[New chapter] Computational environment

To do:

  • Set up new chapter on "Computational Environment"
  • Move all dependency listings to new chapter
  • Derive new container (rocker_gds)
  • Add install guides for Rstudio
    • Docker for Mac
    • Docker for Windows Pro/WSL2
    • Docker for Linux
    • Native install
  • Remove dependencies from each chapter

Propose 2 new assignments

  • assignment 1 - add details on tasks and dataset to be used at the end of each relevant chapter
  • assignment 2 - add details on tasks and dataset to be used at the end of each relevant chapter

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