GeoTrees

Python module containing useful functions and classes for Spatial Analysis.

Tested on Python versions 3.9 to 3.14.

Installation

As a dependency with pip

pip install geotrees

The only dependency of the library is numpy. Optional dependencies required to run the example notebooks are ipykernel and polars.

Neighbours

1d neighbours

For example, finding the closest time value in a list of sorted time values:

def find_nearest(vals: list[Numeric], test: list[Numeric]) -> list[int]:
    ...

Example:

from geotrees import find_nearest
import numpy as np

# Generate random neighbours and test points
potential_neighbours = list(np.random.randn(100))
potential_neighbours.sort()
test_values = list(np.random.randn(3))

neighbour_i = find_nearest(potential_neighbours, test_values)

Returns the index of the value in potential_neighbours that is closest to each value in test_values. This function makes use of bisect.bisect from the python standard library and requires that the list potential_neighbours is sorted. No check on sorted-ness is performed, if the list is unsorted then the incorrect answer will be returned.

This calculation is performed in O(log(n)) time.

2d neighbours

An implementation of KDTree (specifically a 2DTree) using Haversine distance as the query distance and accounting for longitudinal wrapping at -180, 180.

Uses a Record class:

Record(
    lon: float,
    lat: float,
    datetime: datetime.datetime | None,
    uid: str | None,
    **data
)

Fast look-up of nearest neighbours in 2 spatial dimensions (longitude and latitude).

KDTree(
    points: list[Record],
    max_depth: int,
    depth: int = 0  # Internal parameter
)

from geotrees import KDTree, Record
from random import choice

lon_range = list(range(-180, 180))
lat_range = list(range(-90, 90))
N_samples = 1000

records: list[Record] = [Record(choice(lon_range), choice(lat_range)) for _ in range(N_samples)]
# Construct Tree
kdtree = KDTree(records)

test_value: Record = Record(lon=47.6, lat=-31.1)
neighbours: list[Record] = []
neighbours, dist = kdtree.query(test_value)

Points within distance (2d & 3d)

Also included is an implementation of QuadTree and OctTree using Haversine distance for querying with a test Record and distance.

QuadTree(
    boundary: Rectangle,
    capacity: int,
    max_depth: int,
    depth: int = 0  # Internal parameter
)

The boundary is defined by a Rectangle class, which is defined by the bounding box.

Rectangle(
    west: float,   # Western edge
    east: float,   # Eastern edge
    south: float,  # Southern edge
    north: float,  # Northern edge
)

The Rectangle class will raise an error if the northern or southern boundary go beyond the north or south pole.

from geotrees import QuadTree, Record, Rectangle
from random import choice

lon_range = list(range(-180, 180))
lat_range = list(range(-90, 90))
N_samples = 1000

# Construct Tree
boundary = Rectangle(-180, 180, -90, 90)  # Full domain
quadtree = QuadTree(boundary)

records: list[Record] = [Record(choice(lon_range), choice(lat_range)) for _ in range(N_samples)]
for record in records:
    quadtree.insert(record)

test_value: Record = Record(lon=47.6, lat=-31.1)
dist: float = 340  # km

neighbours: list[Record] = quadtree.nearby_points(test_value, dist)

OctTree - 3d QuadTree

Adds SpaceTimeRecord, SpaceTimeRectangle and OctTree classes. This allows for querying in a third dimension, specifically this adds a time dimension. Typically the time dimension is assumed to be of datetime.datetime type, however this is expected to work with numeric values, for example pentad, day of year. However, this non-datetime behaviour is not intended.

SpaceTimeRecord(
    lon: float,
    lat: float,
    datetime: datetime.datetime,  # datetime no longer optional
    uid: str | None,
    **data
)

As with the Rectangle class for the QuadTree, the SpaceTimeRectangle defines the boundary of an OctTree class, and is defined by the space-time bounding box.

SpaceTimeRectangle(
    west: float,               # Western edge
    east: float,               # Eastern edge
    south: float,              # Southern edge
    north: float,              # Northern edge
    start: datetime.datetime,  # Start datetime
    end: datetime.datetime,    # End datetime
)

Example

from geotrees import OctTree, SpaceTimeRecord, SpaceTimeRectangle
from datetime import datetime, timedelta
from random import choice
from pandas import date_range

lon_range = list(range(-180, 180))
lat_range = list(range(-90, 90))

dates = date_range(
    start=datetime(2009, 1, 1, 0, 0),
    end=datetime(2009, 2, 1, 0, 0),
    interval=timedelta(hours=1),
    inclusive="left",
)
N_samples = 1000

# Construct Tree
boundary = SpaceTimeRectangle(-180, 180, -90, 90, datetime(2009, 1, 1, 0), datetime(2009, 1, 2, 23))  # Full domain
octtree = OctTree(boundary)

records: list[SpaceTimeRecord] = [
    SpaceTimeRecord(choice(lon_range), choice(lat_range), choice(dates)) for _ in range(N_samples)]
for record in records:
    octtree.insert(record)

test_value: SpaceTimeRecord = SpaceTimeRecord(lon=47.6, lat=-31.1, datetime=datetime(2009, 1, 23, 17, 41))
dist: float = 340  # km
t_dist = timedelta(hours=4)

neighbours: list[Record] = octtree.nearby_points(test_value, dist, t_dist)