Finding nearest station to each shop using BallTree - location

Question

I've got 2 datasets, a list of shops with UK coordinates and train station also, with coordinates.
I'm using BallTree to get the nearest station to each shop with a distance, using a a code from this website and I've swapped in my dataframes appropriately.
https://automating-gis-processes.github.io/site/notebooks/L3/nearest-neighbor-faster.html
Code:
import pandas as pd
import numpy as np
import geopandas as gpd
from sklearn.neighbors import BallTree
df_pocs = pd.read_csv(r'C:\Users\FLETCHWI\Desktop\XX\shops.csv', encoding = "ISO-8859-1", engine='python')
df_stations = pd.read_csv(r'C:\Users\FLETCHWI\Desktop\xx\uk_stations.csv', encoding = "ISO-8859-1", engine='python')
gdf_pocs = gpd.GeoDataFrame(
df_pocs, geometry=gpd.points_from_xy(df_pocs.longitude, df_pocs.latitude))
gdf_stations = gpd.GeoDataFrame(
df_stations, geometry=gpd.points_from_xy(df_stations.longitude, df_stations.latitude))
def get_nearest(src_points, candidates, k_neighbors=1):
"""Find nearest neighbors for all source points from a set of candidate points"""
# Create tree from the candidate points
tree = BallTree(candidates, leaf_size=15, metric='haversine')
# Find closest points and distances
distances, indices = tree.query(src_points, k=k_neighbors)
# Transpose to get distances and indices into arrays
distances = distances.transpose()
indices = indices.transpose()
# Get closest indices and distances (i.e. array at index 0)
# note: for the second closest points, you would take index 1, etc.
closest = indices[0]
closest_dist = distances[0]
# Return indices and distances
return (closest, closest_dist)
def nearest_neighbor(left_gdf, right_gdf, return_dist=False):
"""
For each point in left_gdf, find closest point in right GeoDataFrame and return them.
NOTICE: Assumes that the input Points are in WGS84 projection (lat/lon).
"""
left_geom_col = left_gdf.geometry.name
right_geom_col = right_gdf.geometry.name
# Ensure that index in right gdf is formed of sequential numbers
right = right_gdf.copy().reset_index(drop=True)
# Parse coordinates from points and insert them into a numpy array as RADIANS
left_radians = np.array(left_gdf[left_geom_col].apply(lambda geom: (geom.x * np.pi / 180, geom.y * np.pi / 180)).to_list())
right_radians = np.array(right[right_geom_col].apply(lambda geom: (geom.x * np.pi / 180, geom.y * np.pi / 180)).to_list())
# Find the nearest points
# -----------------------
# closest ==> index in right_gdf that corresponds to the closest point
# dist ==> distance between the nearest neighbors (in meters)
closest, dist = get_nearest(src_points=left_radians, candidates=right_radians)
# Return points from right GeoDataFrame that are closest to points in left GeoDataFrame
closest_points = right.loc[closest]
# Ensure that the index corresponds the one in left_gdf
closest_points = closest_points.reset_index(drop=True)
# Add distance if requested
if return_dist:
# Convert to meters from radians
earth_radius = 6371000 # meters
closest_points['distance'] = dist * earth_radius
return closest_points
# Find closest public transport stop for each building and get also the distance based on haversine distance
# Note: haversine distance which is implemented here is a bit slower than using e.g. 'euclidean' metric
# but useful as we get the distance between points in meters
closest_stations = nearest_neighbor(gdf_pocs, gdf_stations, return_dist=True)
Upon running the code, it returns the same station for every shop that I have. However I'd like it to find the nearest station for every shop and the distance to it.
Any help appreciated, thanks!

Answer 1

I did some testing of the functions and indeed lat/long needs to be reversed for it to work.
Notice the warning:
NOTICE: Assumes that the input Points are in WGS84 projection (lat/lon).
Hence, when defining the point simple change
gdf_pocs = gpd.GeoDataFrame(
df_pocs, geometry=gpd.points_from_xy(df_pocs.longitude, df_pocs.latitude))
gdf_stations = gpd.GeoDataFrame(
df_stations, geometry=gpd.points_from_xy(df_stations.longitude, df_stations.latitude))
to
gdf_pocs = gpd.GeoDataFrame(
df_pocs, geometry=gpd.points_from_xy(df_pocs.latitude, df_pocs.longitude))
gdf_stations = gpd.GeoDataFrame(
df_stations, geometry=gpd.points_from_xy(df_stations.latitude, df_stations.longitude))