Thank you for your questions! You've raised some excellent points that deserve clarification:
The choice of H3 resolution 10 in the example was primarily for demonstration purposes. The resolution choice should be tailored to the specific use case. For broad area analysis like finding buildings within a suburb, a coarser resolution might suffice. For more precise operations, a finer resolution would be appropriate.
H3 indexing provides an estimation that can be useful for quick spatial joins and proximity analysis. However, it's not intended to replace exact geometric operations.
A hybrid approach can be highly effective. Databricks supports combining H3 indexing with precise spatial functions like st_contains(). This allows for initial filtering using H3 followed by exact spatial operations where needed.
Maintaining the original WKT/WKB representation alongside H3 indexes offers the best of both worlds. It allows for flexible querying strategies depending on the specific requirements of each analysis.
