Calculating Point Density using Python

Question

I have a list of X and Y coordinates from geodata of a specific part of the world. I want to assign each coordinate, a weight, based upon where it lies in the graph.

Answer 1

If you have a lot of points, you may compute nearest neighbors more efficiently using a KDTree:

import numpy as np
import scipy.spatial as spatial
points = np.array([(1, 2), (3, 4), (4, 5), (100,100)])
tree = spatial.KDTree(np.array(points))
radius = 3.0

neighbors = tree.query_ball_tree(tree, radius)
print(neighbors)
# [[0, 1], [0, 1, 2], [1, 2], [3]]

tree.query_ball_tree returns indices (of points) of the nearest neighbors. For example, [0,1] (at index 0) means points[0] and points[1] are within radius distance from points[0]. [0,1,2] (at index 1) means points[0], points[1] and points[2] are within radius distance from points[1].

frequency = np.array(map(len, neighbors))
print(frequency)
# [2 3 2 1]
density = frequency/radius**2
print(density)
# [ 0.22222222  0.33333333  0.22222222  0.11111111]