How do you display the scale in meters, the north arrow and the axes in latitude and longitude on a map with Geopandas?

Question

With reference to this issue , is it possible to have the scale bar (projected in meters, so 3857 for example) with the x,y axes in latitude, longitude projection (4326) and the north arrow?

I don't see a turnkey solution to do this with geopandas. While this seems to be basic settings for map display with GIS. Is there a technical reason for this?

import geopandas as gpd
from matplotlib_scalebar.scalebar import ScaleBar
import matplotlib.pyplot as plt

df = gpd.read_file(gpd.datasets.get_path('nybb'))
ax = df.to_crs(4326).plot()
ax.add_artist(ScaleBar(1)) #how add ScaleBar for df in 3857? 
plt.show()

Answer 1

From this , it looks like you have to compute the great circle distance between two locations A and B with coordinates A=[longitudeA,latitudeA] and B=[longitudeA+1,latitudeA], at the latitude you are interested in (in your case ~40.7°). To compute the great circle distance you can use the 'haversine_distances' from sklearn ( here ) and multiply it by the radius of the earth 6371000 to get the distance in meters. Once you get this distance dx , you can just pass it to your scalebar with ScaleBar(dx=dx,units="m") .

So overall, the code looks like that:

import numpy as np
import geopandas as gpd
from matplotlib_scalebar.scalebar import ScaleBar
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import haversine_distances

df = gpd.read_file(gpd.datasets.get_path('nybb'))
ax = df.to_crs(4326).plot()
A=[-74.5*np.pi/180.,40.7*np.pi/180.] #Latitude of interest here 40.7 deg, longitude -74.5
B=[-73.5*np.pi/180.,40.7*np.pi/180.] ##Latitude of interest here 40.7 deg, longitude -74.5+1
dx=(6371000)*haversine_distances([A,B])[0,1]
ax.add_artist(ScaleBar(dx=dx,units="m")) 
plt.show()

And the output gives: