This workshop is aimed at everyone interested in using OpenStreetMap (OSM) to support sustainable transport planning, in professional or advocacy contexts. The focus will be on getting started, identifying, visualizing, and analyzing key tags and identifying gaps in walking, cycling, and wheeling networks. Participants will place OSM data in the context of other data sources to identify its unique advantages. The workshop will be practical and get users started with key ‘tools of the trade’ in the areas: R (used for live demo), Python, A/B Street, and osm2streets. By the end of the workshop, you will know how to add value to OSM data to support sustainable transport planning.
OSM data has been used in various public-facing transport-related contexts, such as routing services and navigation. However, the potential of OSM data in transport planning has yet to be fully realized. This workshop will provide insight into how OSM data, when combined with new and open source ‘tools of the trade’, can support more evidence-based and sustainable transport planning activities. OSM can also support citizen-led bottom-up approaches to understanding, prioritizing, and designing active travel infrastructure. This workshop, developed as part of the OpenInfra project at the University of Leeds, aims to address this gap and encourage OSM users to help release the full potential of OSM data for decision-making in the transport sector and beyond. Based on the ‘learning by doing’ ethos the bulk of the workshop will be practical, enabling participants to see how OSM data can be used to support better walking, cycling, and wheeling infrastructure and policies. Participants will be introduced to methods for getting started with OSM for transport planning, focusing on practical steps that can answer the following questions with live demo and practical in R (we will also discuss how to achieve the results in Python, A/B Street, and osm2streets): - What gaps in transport evidence can OSM data fill in different countries? - Which elements and tags are most important for transport planning (and how to get the data in reproducible data science environments)? - How to filter based on different tags and their values? - How to use OSM datasets to visualize transport systems in static and interactive maps? - How to add value to OSM data and communicate results using R, Python and A/B Street, and osm2streets? Besides practical examples, participants will be encouraged to think critically about the advantages and disadvantages of OSM data in sustainable transport planning with a particular focus on accessibility. By the end of the workshop, you will be able to apply the methods introduced in the workshop to your own unique contexts, potentially going beyond transport planning. Participants are not expected to be familiar with Python, R, A/B Street, osm2streets or active travel planning but experience with programming and an interest in active travel (walking, cycling, wheeling) will be advantageous. Before the workshop, everyone will be sent out installation instructions to complete before the start of the workshop.
Speakers: Greta Timaite James Hulse