Pay Notebook Creator: Salah Ahmed0
Set Container: Numerical CPU with TINY Memory for 10 Minutes 0


This notebook-tool showcases how to use KDTrees to query closest points to a location in miles

{lat_float: latitude} {lng_float: longitude}

In [4]:
target_folder = '.'
lat_float = 40.705735
lng_float = -73.814284
In [5]:
import os
import pandas as pd
import pysal
from import KDTree

def get_closest_spots(curr_location, locations):
    tree = KDTree(locations.values, distance_metric='Arc',
    distances, indices = tree.query(curr_location, k=10)
    return distances, indices
    # get all points within half mile
    # indices = tree.query_ball_point(curr, 0.5)
    # print(data.ix[indices]['ssid'])

if __name__ == '__main__':
    url = ''
    data = pd.read_json(url)
    locations = data[['lat', 'lon']]
    distances, indices = get_closest_spots((lat_float, lng_float), locations)
             'fill color',
             'radius in pixels']
    data = [[i[0], i[1], i[2], 'red', 10] for i in data.ix[indices][['location', 'lat', 'lon']].values]
    path = os.path.join(target_folder, 'geomap.csv')
    pd.DataFrame(data, columns=columns).to_csv(path, index=False)
    print("points_geotable_path = {0}".format(path))
points_geotable_path = ./geomap.csv