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 geocomputational research, 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, and the rate of data creation is increasing as the community grows, to 10 million users 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 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.