Efficiently Finding The Closest Coordinate Pair From A Set In Python

The Problem Imagine I am stood in an airport. Given a geographic coordinate pair, how can one efficiently determine which airport I am stood in? Inputs A coordinate pair (x,y) rep

Solution 1:

Using a k-dimensional tree:

>>> from scipy import spatial
>>> airports = [(10,10),(20,20),(30,30),(40,40)]
>>> tree = spatial.KDTree(airports)
>>> tree.query([(21,21)])
(array([ 1.41421356]), array([1]))

Where 1.41421356 is the distance between the queried point and the nearest neighbour and 1 is the index of the neighbour.

See: http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.query.html#scipy.spatial.KDTree.query

Solution 2:

If your coordinates are unsorted, your search can only be improved slightly assuming it is (latitude,longitude) by filtering on latitude first as for earth

1 degree of latitude on the sphere is 111.2 km or 69 miles

but that would not give a huge speedup.

If you sort the airports by latitude first then you can use a binary search for finding the first airport that could match (airport_lat >= point_lat-tolerance) and then only compare up to the last one that could match (airport_lat <= point_lat+tolerance) - but take care of 0 degrees equaling 360. While you cannot use that library directly, the sources of bisect are a good start for implementing a binary search.

While technically this way the search is still O(n), you have much fewer actual distance calculations (depending on tolerance) and few latitude comparisons. So you will have a huge speedup.

Solution 3:

From this SO question:

import numpy as np
defclosest_node(node, nodes):
    nodes = np.asarray(nodes)
    deltas = nodes - node
    dist_2 = np.einsum('ij,ij->i', deltas, deltas)
    return np.argmin(dist_2)

where node is a tuple with two values (x, y) and nodes is an array of tuples with two values ([(x_1, y_1), (x_2, y_2),])

Solution 4:

The answer of @Juddling is great, but KDTree does not support haversine distance, which is better suited for latitude/longitude coordinates. For the haversine distance you can use BallTree. Please note, that you need to convert your coordinates to radians first.

from math import radians
from sklearn.neighbors import BallTree
import numpy as np

airports = [(10,10),(20,20),(30,30),(40,40)]
airports_rad = np.array([[radians(x[0]), radians(x[1])] for x in airports ])
tree = BallTree(airports_rad , metric = 'haversine')
result = tree.query([(radians(21),radians(21))])
print(result)

gives

(array([[0.02391369]]), array([[1]], dtype=int64))

To convert the distance to meters you need to multiply by the earth radius (in meters).

earth_radius = 6371000 # meters in earthprint(result[0][0] * earth_radius)
[152354.11114795]