I have a shapefile of points, defined by X and Y coordinates, ad the ID feature. I have at least 3 different points with the same ID number.
I would like to define, for each ID, the shapefile of a circle that circumscribes the points.
How can this be done in python environment?
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Answer
- there is a library that does it: https://pypi.org/project/miniball/
- it’s pretty forward to integrate in standard pandas pattern https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html
- solution really reduces to this:
def circle(points):
p, r = miniball.get_bounding_ball(np.array([points.x, points.y]).T)
return shapely.geometry.Point(p).buffer(math.sqrt(r))
col = "group"
# generate circles around groups of points
gdf_c = cities.groupby(col, as_index=False).agg(geometry=("geometry", circle))
- with sample example and visualisation, circles do become distorted due to epsg:4326 projection limitations
full working example
import geopandas as gpd
import numpy as np
import shapely
import miniball
import math
import pandas as pd
cities = gpd.read_file(gpd.datasets.get_path("naturalearth_cities"))
world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
# a semi-synthetic grouping of cities
world["size"] = world.groupby("continent")["pop_est"].apply(
lambda d: pd.cut(d, 2, labels=list("ab"), duplicates="drop").astype(str)
)
cities = cities.sjoin(world.loc[:, ["continent", "iso_a3", "size", "geometry"]])
cities["group"] = cities["continent"] + cities["size"]
def circle(points):
p, r = miniball.get_bounding_ball(np.array([points.x, points.y]).T)
return shapely.geometry.Point(p).buffer(math.sqrt(r))
col = "group"
# generate circles around groups of points
gdf_c = cities.groupby(col, as_index=False).agg(geometry=("geometry", circle))
# visualize it
m = cities.explore(column=col, height=300, width=600, legend=False)
gdf_c.loc[~gdf_c["geometry"].is_empty].explore(
m=m, column=col, marker_kwds={"radius": 20}, legend=False
)
output
