New ask Hacker News story: The offline geocoder we wanted
The offline geocoder we wanted
4 by gipsyjaeger | 2 comments on Hacker News.
What is this? This is an offline reverse geocoder written in Python. Given a latitude–longitude pair, it returns the correct administrative region such as country, state, or district without calling any external APIs. This avoids API costs, rate limits, and network dependency. Why build another reverse geocoder? Most offline reverse geocoders rely on nearest-neighbor lookups. While fast, this approach often fails near borders because the closest location is not always the correct administrative region. This project focuses on correctness over proximity by verifying which boundary a coordinate actually falls inside. How does it work? A KD-Tree is used to quickly shortlist nearby administrative boundaries. For those candidates, the system performs polygon containment checks to confirm the true region. It supports both single-process execution for small workloads and multiprocessing for large batch processing. Performance The system processes 10,000 coordinates in under 2 seconds, with an average polygon validation time below 0.4 milliseconds per coordinate. Who is this for? Anyone who needs reverse geocoding, predictable costs, large-scale batch processing. Implementation notes This started as a toy implementation to explore boundary-aware reverse geocoding, but it turned out to be reliable enough for real production use. The dataset covers more than 210 countries with over 145,000 administrative boundaries. Links Source code: https://ift.tt/lZEr4jO Documentation: https://ift.tt/1wpWfQD Feedback is welcome, especially around the approach, performance trade-offs, and edge cases.
4 by gipsyjaeger | 2 comments on Hacker News.
What is this? This is an offline reverse geocoder written in Python. Given a latitude–longitude pair, it returns the correct administrative region such as country, state, or district without calling any external APIs. This avoids API costs, rate limits, and network dependency. Why build another reverse geocoder? Most offline reverse geocoders rely on nearest-neighbor lookups. While fast, this approach often fails near borders because the closest location is not always the correct administrative region. This project focuses on correctness over proximity by verifying which boundary a coordinate actually falls inside. How does it work? A KD-Tree is used to quickly shortlist nearby administrative boundaries. For those candidates, the system performs polygon containment checks to confirm the true region. It supports both single-process execution for small workloads and multiprocessing for large batch processing. Performance The system processes 10,000 coordinates in under 2 seconds, with an average polygon validation time below 0.4 milliseconds per coordinate. Who is this for? Anyone who needs reverse geocoding, predictable costs, large-scale batch processing. Implementation notes This started as a toy implementation to explore boundary-aware reverse geocoding, but it turned out to be reliable enough for real production use. The dataset covers more than 210 countries with over 145,000 administrative boundaries. Links Source code: https://ift.tt/lZEr4jO Documentation: https://ift.tt/1wpWfQD Feedback is welcome, especially around the approach, performance trade-offs, and edge cases.
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