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https://github.com/robinlovelace/opengeohub2023

Content for lecture at OpenGeoHub 2023 on spatial data and the tidyverse
https://github.com/robinlovelace/opengeohub2023

course data-science opengeohub osgeo practical r reproducible summer-school tidy-data

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

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README 

        
# OpenGeoHub Summer School 2023






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



Quarto Publish






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.



Open in GitHub Codespaces



If it works you should see something like this:



![](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])

}

```