04-18, 14:55–15:30 (US/Eastern), Auditorium 4
Geospatial data can unlock valuable insights. OpenStreetMap includes electric power and telecommunication infrastructure geospatial data, and it is already “open”. This presentation will demonstrate how to use Python to “unlock the insights” available in OSM power and telecommunications geospatial data.
Commercial real estate organizations are avid consumers of geospatial data. These organizations have already identified the value in particular of power and telecommunications infrastructure spatial data to make business decisions. Examples of these data include: the locations of power plants, transmission lines, fiber backbone cables, and submarine fiber cables.
One rich source for these datasets is OpenStreetMap (OSM), however natively OSM does not streamline access to data, especially at scale. Because OSM data are open, we can use Python to query, download, and transform OSM power and telecommunications spatial data for use within Open Source and commercial Geographic Information Systems (GIS) software applications, models built in Python and other languages, and really any other tools and processes which can read GIS data.
This presentation will present a high-level overview of the overall data flow, and then dive into individual steps and how each step was implemented in Python. Examples will be provided, and maps and analyses based on the resulting spatial data will be demonstrated. This presentation will also explain one approach to download very large OSM datasets, for example data spanning continents and including many different themes. Along the way this presentation will also touch on how to avoid “gotchas” and how this approach could be adopted to different types of OSM data supporting other use cases and business requirements.
No previous knowledge expected
Cory Eicher is the founder of Eichcorp, a software consulting and implementation practice based in Charlottesville, Virginia... Developer/Mapper/Reader/Biker/Hiker/Skier/Soccer-er