Using Python to Unlock Insights from OpenStreetMap Data at Scale
summary
Python code automates downloading OpenStreetMap power and telecom data via Overpass API, converts OSM JSON to GeoJSON, then loads into an ESRI geodatabase using a licensed heartpy toolset, chunking queries by spatial cells, tag, and geometry across Europe Middle East Africa. The corpus includes about 42 million power objects (≈35M nodes, 6M ways). Workflow deduplicates by OSM id, extracts nested tags to top‑level fields, runs serially (not parallel), and produces seamless GIS layers that validate against the client use case.
AI-written summary · about
chapters (17)
- 0:00Introduction
- 1:58Why OpenStreetMap matters
- 3:24Client problem and solution
- 5:33OpenStreetMap data structure
- 8:00Power data and tagging
- 9:46Overpass API for extraction
- 10:51OpenStreetMap to GIS formats
- 12:18GIS constraints
- 14:09Python for automated queries
- 15:35Scalability and encoding
- 16:43Data to ESRI GeoDatabase
- 17:45Merging spatial data
- 19:30Extracting attribution
- 21:16Visualizations and insights
- 22:41Challenges and solutions
- 23:44Optimizations and future
- 25:07Q&A
related talks
37mProcessing Open Street Map Data with Python and PostgreSQL
41mHow to GIS with Python
43mLocate all the things
05mSpatial Mapping in Python with GeoPandas and OpenStreetMap
32mRunning Every Street in Paris with Python and PostGIS
31mUnpacking the geospatial engineering toolbox – an overview of data science techniques for spatial da
29mGeospatial Analysis using Python
19mLearning rules and detecting errors in OpenStreetMap data