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opengeohub2023

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Content for lecture at OpenGeoHub 2023 on spatial data and the tidyverse

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.devcontainer.json
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_quarto.yml
blog.qmd
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DESCRIPTION
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index.qmd
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osm.qmd
osm.R
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`osmium`

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Multipurpose tool for handling OpenStreetMap (OSM) files.

osmium fileinfo {{path/to/input.osm}}

Show file information:

osmium show {{path/to/input.osm}}

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osmium cat {{path/to/input.osm.pbf}} {{[-o|--output]}} {{path/to/output.osm}}

Convert file format from PBF into XML:

// repository documentation

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<div>

[![](https://github.com/Robinlovelace/opengeohub2023/actions/workflows/publish.yml/badge.svg)](https://github.com/Robinlovelace/opengeohub2023/actions/workflows/publish.yml)

Quarto Publish

</div>

This repo contains code to support sessions that I will deliver for the
OpenGeoHub Summer School 2023.

As shown in the course
[schedule](https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/schedule/)
I will deliver the following sessions:

- [Tidy geographic data with sf, dplyr, ggplot2, geos and
  friends](https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/7JN3FV/)
  - 2023-08-28, 11:00–12:30, Room 21 (Sala 21)  
- [Processing large OpenStreetMap datasets for
  geocomputation](https://pretalx.earthmonitor.org/opengeohub-summer-school-2023/talk/SRMZVJ)
  - 2023-09-01, 09:00–10:30, Room 18 (Sala 18)

Abstracts for these sessions are provided below.

## Reproducibility

You can reproduce all of the results and run all the code in this repo.
The quickest way to get started if you don’t already have the
dependencies is with codespaces.

<a href='https://codespaces.new/Robinlovelace/opengeohub2023'><img src='https://github.com/codespaces/badge.svg' alt='Open in GitHub Codespaces' style='max-width: 100%;'></a>

If it works you should see something like this:

![](https://raw.githubusercontent.com/Robinlovelace/opengeohub2023/main/images/paste-11.png)

To run the code locally download the code, e.g. with the following
command after installing GitHub’s CLI tool:

``` bash
gh repo clone Robinlovelace/opengeohub2023
```

Then open the project in RStudio, VS Code, and start looking at and
running the code. You should be able to reproduce the rendered website
interactively with the following command:

``` bash
quarto preview
```

You can also run the code in this repo in the Docker image hosted at
https://github.com/Robinlovelace/opengeohub2023/pkgs/container/opengeohub2023
as follows, with the -v flag to mount the current directory in the
container:

``` bash
docker run -it -v $(pwd):/workspace opengeohub2023:latest
```

If you have VS Code installed you should be able to ‘Reopen in
Container’ (not fully tested locally).

## Tidy geographic data with sf, dplyr, ggplot2, geos and friends

This lecture will provide an introduction to working with geographic
data using R in a ‘tidy’ way. It will focus on using the `sf` package to
read, write, manipulate, and plot geographic data in combination with
the `tidyverse` metapackage. Why use the `sf` package with the
`tidyverse`? The lecture will outline some of the ideas underlying the
`tidyverse` and how they can speed-up data analysis pipelines, while
making data analysis code easier to read and write. We will see how the
following lines

``` r
library(sf)
library(tidyverse)
```

can provide a foundation on which the many geographic data analysis
problems can be solved. The lecture will also cover on more recently
developed packages that integrate with the `tidyverse` to a greater and
lesser extent. We will look at how the `geos` package, which provides a
simple and high-performance interface to the GEOS library for performing
geometric operations on geographic data, integrates with the
`tidyverse`. The `tidyverse` is not the right tool for every data
analysis task and we touch on alternatives for working with raster data,
with reference to the `terra` package, and alternative frameworks such
as `data.table`. Finally, we will also look at how the ‘tidy’ philosophy
could be implemented in other programming languages, such as Python.

The focus throughout will be on practical skills and using packages
effectively within the wider context of project management tools,
integrated development environments (we recommend VS Code with
appropriate extensions or RStudio), and version control systems.

## Processing large OpenStreetMap datasets for geocomputation

OpenStreetMap (OSM) is a free and openly editable map of the world. Like
Wikipedia and unlike government or corperation maintained datasets, OSM
is created and maintained by a community of volunteers, making it the
premier decentralized and fastest evolving source of geographic vector
data focussed on features relevant to human activity (e.g. roads,
buildings, cafes) on planet Earth. Unlike Wikipedia, every data point in
OSM has a geographic location and attributes must be structured as
key-value pairs. OSM is a rich source of data for geocomputation, but
the decentralized nature of the project and the sheer volume of data.
‘Planet.osm’ now has more nodes than there are people on Earth, with
more than 8 billion [nodes](https://wiki.openstreetmap.org/wiki/Node),
and the rate of data creation is increasing as the community grows, to
[10 million users](https://wiki.openstreetmap.org/wiki/Stats) in early
2023. The size and rapid evolution of OSM are great strengths,
democratising geographic knowledge and ensuring resilience. However,
these features can make it difficult to work with OSM data.

This lecture will provide an introduction to working with OSM and will
cover the following:

- How and where to download OSM data
- How to process small amounts of OSM data using the `osmdata` R package
- How to process large OSM ‘extracts’ data with the `osmextract` R
  package
- Other command line tools for working with OSM data, including the
  mature and widely used `osmium` tool, the `pyrosm` Python package and
  the [`osm2streets`](https://github.com/a-b-street/osm2streets) web
  application and Rust codebase

Finally, the lecture will outline ideas for using OSM data. It will
conclude with a call to action, inspiring the use of this rich resource
to support policy objectives such as the fast and fair decarbonisation
of the global economy as societies transition away from inefficient,
polluting and costly fossil fuels.

## Reproducibility

To install the dependencies for this repo, run the following command:

``` r
remotes::install_github("robinlovelace/opengeohub2023")
```

This repo also uses the `renv` package to manage dependencies. We saved
the dependencies with the following command:

``` r
renv::snapshot()
```

Code was generated from the `.qmd` files using the following commands:

``` r
f = c("tidy.qmd", "osm.qmd")
f_r = gsub(".qmd", ".R", f)
for(i in seq_along(f)) {
  knitr::purl(f[i], f_r[i])
}
```