Can mark_geoshape () be used for Canadian Provinces/cities?

I'm looking to somehow figure out a way to insert a geographic graph of British Columbia which is a part of Canada in my data analysis.

I have made this image here explaining what tree is being planted the most in Vancouver

Now I want to make a geograph kind of like this https://altair-viz.github.io/gallery/airports_count.html

to answer: how the density/distribution of species planted different in different neighbourhoods look like.

This is what I'm having trouble with.

Thus

from vega_datasets import data


world_map = alt.topo_feature(data.world_110m.url, 'countries')

alt.Chart(world_map).mark_geoshape().project()

and it's giving me a world map! Great! I tried zooming into just British Columbia but it's not really working out.

Can anyone give me any direction on where to go and how I should go about answering my question? I really wanted to use geoshape

I also found this if it's helpful

https://global.mapit.mysociety.org/area/960958.html

Thank you and I appreciate everyones advice!

Looks like you got your data from here

import pandas as pd
import numpy as np
import plotly.express as px

#loading data
df = pd.read_csv('street-trees.csv', sep=';')
#extracting coords
df['coords'] = df['Geom'].str.extract('\[(.*?)\]')
df['lon'] = df['coords'].str.split(',').str[0].astype(float)
df['lat'] = df['coords'].str.split(',').str[1].astype(float)

#getting neighborhood totals
df2 = pd.merge(df[['NEIGHBOURHOOD_NAME']].value_counts().reset_index(), df[['NEIGHBOURHOOD_NAME', 'lon', 'lat']].groupby('NEIGHBOURHOOD_NAME').mean().reset_index())

#drawing figure
fig = px.scatter_mapbox(df2,
        lat='lat',
        lon='lon',
        color=0,
        opacity=0.5,
        center=dict(lon=df2['lon'].mean(),
                lat=df2['lat'].mean()),
        zoom=11,
        size=0)

fig.update_layout(mapbox_style='open-street-map')

fig.show()