index.qmd

# Welcome {.unnumbered}

*Geospatial Data Science with Julia* presents a fresh
approach to data science with geospatial data and the
[![](images/julia.svg){fig-alt="Julia" width=40px}](https://julialang.org)
programming language.
It contains best practices for writing *clean*, *readable*
and *performant* code in geoscientific applications
involving sophisticated representations of the (sub)surface
of the Earth such as unstructured meshes made of 2D and
3D geometries.

By reading this book, you will:

1. Get a broader perspective on geospatial data
2. Learn advanced geostatistical algorithms
3. Reproduce practical **open source** examples

Most importantly, you will learn a set of geospatial features
that is much richer than the
[simple features](https://en.wikipedia.org/wiki/Simple_Features)
implemented in traditional geographic information systems (GIS).

<a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.

## How to contribute?

First off, thank you for considering contributing to this book.
It's people like you that make this project so much fun. Below
are a few suggestions to facilitate the review process:

- Please be polite, we are here to help and learn from each other
- Try to explain your contribution with simple language
- References to textbooks and papers are always welcome
- Follow the code style in the examples as much as possible

This book is [open source](https://github.com/juliohm/geospatial-data-science-with-julia)
and fully reproducible thanks to the amazing
[Quarto](https://quarto.org) project. You can edit the pages
directly on GitHub and submit a pull request for review.
If you are not familiar with this process, consider reading
the [first contributions](https://github.com/firstcontributions/first-contributions) guide.

Alternatively, you can render the book locally with the
[Quarto VS Code Extension](https://marketplace.visualstudio.com/items?itemName=quarto.quarto),
which is the recommended method for reviewing more elaborate changes.

## Getting involved

If you would like to get involved with the project, you can start by

- Joining our [community channel](https://julialang.zulipchat.com/#narrow/stream/276201-geostats.2Ejl):
  ![zulip](images/zulip.png)
- Starring or sponsoring our [book](https://github.com/JuliaEarth/geospatial-data-science-with-julia) and
  [software](https://github.com/JuliaEarth/GeoStats.jl) on GitHub:
  ![geostats-stars](images/geostats-stars.png)
- Sharing the book on social media (LinkedIn, Twitter, ...)
- Asking questions and making suggestions
- Organizing training courses and workshops
- Citing the work in publications:
  ```bibtex
  @book{Hoffimann2023,
    title = {Geospatial Data Science with {{Julia}}},
    author = {Hoffimann, Júlio},
    year = {2023},
    doi = {10.5281/zenodo.10150870},
    url = {https://juliaearth.github.io/geospatial-data-science-with-julia}
  }
  ```