GeoKDTree
Ultra-fast nearest-neighbor lookup for latitude/longitude data
GeoKDTree is a lightweight, high-performance spatial indexing library for Python designed to find the nearest geographic coordinate from massive datasets in nanoseconds.
It wraps a highly optimized KD-Tree with a geographic interface, allowing you to work directly with (latitude, longitude) pairs. No projections, no external dependencies, and no heavy GIS stacks.
Documentation
- Docs: https://connor-makowski.github.io/geokdtree/geokdtree.html
- Git Repo: https://github.com/connor-makowski/geokdtree
Installation
Standard Installation (with C++ acceleration)
pip install geokdtree
Pure Python Installation (Skip Binary Builds)
If you want to install geokdtree in pure Python mode without compiling or using binary extensions (or in environments without a C++ compiler):
-
Linux / macOS / WSL2:
GEOKDTREE_NO_BUILD=1 pip install --no-binary geokdtree geokdtree
Or with
uv:GEOKDTREE_NO_BUILD=1 uv pip install --no-binary geokdtree geokdtree
-
Windows (PowerShell):
$env:GEOKDTREE_NO_BUILD="1" pip install --no-binary geokdtree geokdtree
-
Windows (CMD):
set GEOKDTREE_NO_BUILD=1 pip install --no-binary geokdtree geokdtree
Automatic Fallback
When building from source, if a C++ compiler is not available or compilation fails, geokdtree will automatically fall back to the pure Python implementation.
Getting Started
from geokdtree import GeoKDTree
example_points = [
(34.0522, -118.2437), # Los Angeles
(40.7128, -74.0060), # New York
(37.7749, -122.4194), # San Francisco
(51.5074, -0.1278), # London
(48.8566, 2.3522), # Paris
]
geo_kd_tree = GeoKDTree(points=example_points)
test_point = (47.6062, -122.3321) # Seattle
# Find the index of the closest point in the original dataset
closest_idx = geo_kd_tree.closest_idx(test_point) # => 2
# Find the closest point itself
closest_point = geo_kd_tree.closest_point(test_point) # => (37.7749, -122.4194)
# Find the closest point in each quadrant (ne, nw, se, sw)
closest_quadrants = geo_kd_tree.closest_point_per_quadrant(test_point)
# => {'ne': (51.5074, -0.1278), 'nw': None, 'se': (40.7128, -74.006), 'sw': (37.7749, -122.4194)}
# Find the index of the closest point in each quadrant (ne, nw, se, sw)
closest_quad_indices = geo_kd_tree.closest_idx_per_quadrant(test_point)
# => {'ne': 3, 'nw': None, 'se': 1, 'sw': 2}
Why Use GeoKDTree?
GeoKDTree is designed to solve one focused problem extremely well:
Fast nearest-neighbor and quadrant based lookups for latitude/longitude data at scale.
Extremely Fast Lookups
Once constructed, nearest-neighbor queries consistently complete in hundreds of nanoseconds to single-digit microseconds, even with 1,000,000 coordinates.
Typical benchmark results (see benchmark.md for full benchmarks):
| Number of Points ($N$) | C++ Build Time | Python Build Time | C++ Query Time | Python Query Time |
|---|---|---|---|---|
| 1,000 | ~0.35 ms | ~1.4 ms | ~0.39 µs | ~7.6 µs |
| 10,000 | ~4.9 ms | ~18 ms | ~0.48 µs | ~10.5 µs |
| 100,000 | ~57 ms | ~275 ms | ~0.90 µs | ~15.8 µs |
| 1,000,000 | ~670 ms | ~5.4 s | ~1.9 µs | ~23.4 µs |
This makes GeoKDTree well-suited for:
- Real-time proximity queries
- Matching incoming coordinates against large reference datasets
- High-throughput geospatial APIs
- Pre-filtering before more expensive geospatial calculations
Exact timings depend on hardware, Python version, and data distribution. These values reflect typical results from
utils/benchmark.py.
Built for Geographic Coordinates
GeoKDTree works directly with (latitude, longitude) pairs.
You do not need to:
- Project coordinates into planar space
- Use heavyweight GIS libraries
- Maintain custom spatial indexing code
Just pass geographic coordinates and query.
Simple API, Zero Runtime Dependencies
GeoKDTree intentionally keeps the API small and focused.
- Build once from a list of coordinates
- Query nearest neighbors with a single method call
- Retrieve indices or points directly from your original dataset
- Zero external runtime dependencies, with C++ acceleration via
nanobindand automatic pure Python fallback.
Deterministic and Predictable Performance
- Tree construction scales at approximately $O(N \log N)$
- Query performance scales at approximately $O(\log N)$
- No probabilistic approximations
- No background indexing or caching
This predictability is valuable for production systems where latency and reproducibility matter.
Supported Features
See: https://connor-makowski.github.io/geokdtree/geokdtree.html
Contributing
Issues, feature requests, and pull requests are welcome. Please open an issue to discuss changes or enhancements.
Development
Setup & Testing
-
Clone the repository and install development dependencies:
uv sync --extra dev --reinstall-package geokdtree
-
Run the test suite:
uv run pytest
-
Run full test matrix across Python versions (3.11–3.14 & PyPy3.11):
uv run nox
-
Run performance benchmarks:
uv run python utils/benchmark.py
-
Prettify and format code:
uv run python utils/prettify.py
1 maintainer
Metadata
1 maintainer
Release files for geokdtree 1.3.0
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