Calculate Distance to Nearest Feature with Geopandas

Question

I\'m looking to do the equivalent of the ArcPy Generate Near Table using Geopandas / Shapely. I\'m very new to Geopandas and Shapely and have developed a methodology that wo

Answer 1

If I understand correctly your issue, Blocks and Restaurants can have very different dimensions. For this reason, it's probably a bad approach to try to force into a table format by reindexing.

I would just loop over blocks and get the minimum distance to restaurants (just as @shongololo was suggesting).

I'm going to be slightly more general (because I already have this code written down) and do a distance from points to lines, but the same code should work from points to points or from polygons to polygons. I'll start with a GeoDataFrame for the points and I'll create a new column which has the minimum distance to lines.

%matplotlib inline
import matplotlib.pyplot as plt
import shapely.geometry as geom
import numpy as np
import pandas as pd
import geopandas as gpd

lines = gpd.GeoSeries(
    [geom.LineString(((1.4, 3), (0, 0))),
        geom.LineString(((1.1, 2.), (0.1, 0.4))),
        geom.LineString(((-0.1, 3.), (1, 2.)))])

# 10 points
n  = 10
points = gpd.GeoSeries([geom.Point(x, y) for x, y in np.random.uniform(0, 3, (n, 2))])

# Put the points in a dataframe, with some other random column
df_points = gpd.GeoDataFrame(np.array([points, np.random.randn(n)]).T)
df_points.columns = ['Geometry', 'Property1']

points.plot()
lines.plot()

Now get the distance from points to lines and only save the minimum distance for each point (see below for a version with apply)

min_dist = np.empty(n)
for i, point in enumerate(points):
    min_dist[i] = np.min([point.distance(line) for line in lines])
df_points['min_dist_to_lines'] = min_dist
df_points.head(3)

which gives

    Geometry                                       Property1    min_dist_to_lines
0   POINT (0.2479424516236574 2.944916965334865)    2.621823    0.193293
1   POINT (1.465768457667432 2.605673714922998)     0.6074484   0.226353
2   POINT (2.831645235202689 1.125073838462032)     0.657191    1.940127

---- EDIT ----

(taken from a github issue) Using apply is nicer and more consistent with how you'd do it in pandas:

def min_distance(point, lines):
    return lines.distance(point).min()

df_points['min_dist_to_lines'] = df_points.geometry.apply(min_distance, df_lines)

EDIT: As of at least 2019-10-04 it seems that a change in pandas requires a different input in the last code block, making use of the args parameters in .apply():

df_points['min_dist_to_lines'] = df_points.geometry.apply(min_distance, args=(df_lines,))