通过经纬度计算给定半径内的点数

Question

I have a dataframe of points with its id-name and latitude/longitude:

df = pd.DataFrame({'id':list('abcde'),'latitude': [38.470628, 37.994155, 38.66937, 34.119578, 36.292307],'longitude': [-121.404586, -121.802341, -121.295325, -117.413791, -119.804074]})  #sample

For each id I need to count the number of points (of the same dataset) that are located within a radius of 2 miles from it.

Question: how to do this in the simplest way in Python?

Answer 1

The question is somewhat ambiguous. The first component you need is a function to calculate distance between two coordinates, this requires some trigonometry and has several implementations in the following questions .

After you have the function simply loop over all points and calculate. There might be more efficient ways than two nested loop but this is the simplest.

Answer 2

import numpy as np
import pandas as pd
from sklearn.neighbors import BallTree

Sample Data

df = pd.DataFrame({'id':list('abcde'),'latitude': [38.470628, 37.994155, 38.66937, 34.119578, 36.292307],'longitude': [-121.404586, -121.802341, -121.295325, -117.413791, -119.804074]})  #sample

Extract lat,long and convert to radians. Calculate the needed radius when converted to unit sphere.

coords = df[["latitude","longitude"]]

distance_in_miles = 50
earth_radius_in_miles = 3958.8

radius = distance_in_miles / earth_radius_in_miles

tree = BallTree( np.radians(coords), leaf_size=10, metric='haversine')

tree.query_radius( np.radians(coords), r=radius, count_only=True)

Which gives array([3, 2, 2, 1, 1])

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If you want to return the indici and use them for aggregates; one way is to

df = pd.DataFrame({'id':list('abcde'),'latitude': [38.470628, 37.994155, 38.66937, 34.119578, 36.292307],'longitude': [-121.404586, -121.802341, -121.295325, -117.413791, -119.804074], 'saleprice_usd_per_sqf': [200, 300, 700, 350, 50]})

coords = df[["latitude","longitude"]]

distance_in_miles = 50
earth_radius_in_miles = 3958.8

radius = distance_in_miles / earth_radius_in_miles

Note we use indici here and not only count;

tree = BallTree( np.radians(coords), leaf_size=10, metric='haversine')
indici = tree.query_radius( np.radians(coords), r=radius, count_only=False)

And use list comprehension to for instance get the median value for each radius. Be aware the the point itself is always included in its own radius.

[np.median(df.saleprice_usd_per_sqf.values[idx]) for idx in indici]