2025-12-09 –, Data Engineering & Infrastructure
While commercial reverse geocoding APIs, such as Google Maps or Mapbox, are effective, they are also costly, have rate limitations, and are not appropriate for offline or privacy-sensitive settings.
Using available datasets and Python modules like cKDTree, shapely, and geopandas, we will demonstrate how to create a quick, scalable, offline-capable reverse geocoding system in Python in this session.
You will learn how to:
- Convert geographic shapefiles into effective spatial indices
- Perform location lookups in milliseconds using tree search and vector mathematics
- Handle edge cases like unclear borders, cities with identical names, and GPS noise
- Improve performance and memory usage through multiprocessing
The system is fully open source and has been production-tested in a high-throughput environment. Whether you are developing applications for edge inference, mapping, or logistics, this talk will help you take control of your geospatial infrastructure without depending on costly commercial APIs.
Reverse geocoding — converting coordinates into readable place names — is a core building block of applications in logistics, mapping, mobility, and location intelligence. Yet developers are often locked into commercial APIs that are expensive, rate-limited, and unsuitable for offline or privacy-first use cases.
In this talk, we’ll walk through the architecture and implementation of a fast reverse geocoding engine built entirely in Python using open-source tooling. You’ll see how spatial data (such as OpenStreetMap shapefiles) can be indexed efficiently using scipy's cKDTree, queried with millisecond latency, and integrated into real-world systems.
We’ll explore performance trade-offs, data preprocessing techniques, and methods for dealing with ambiguous or noisy GPS data. The session includes benchmarks and a live walkthrough of the code powering the reverse geocoder — which is lightweight enough to run on a laptop or edge device.
Attendees will leave with a clear understanding of how to build and adapt this system for their own needs — and gain insight into how geospatial systems work behind the scenes.
