@@ -254,7 +254,7 @@ Here we lookup the field ``rate`` from the ``df_us_unemp`` DataFrame, where the
254254 from vega_datasets import data
255255 import geopandas as gpd
256256
257- gdf_us_counties = gpd.read_file(data.us_10m.url, driver='TopoJSON', layer=' counties' )
257+ gdf_us_counties = gpd.read_file(data.us_10m.url, layer=" counties" )
258258 df_us_unemp = data.unemployment()
259259
260260 alt.Chart(gdf_us_counties).mark_geoshape().transform_lookup(
@@ -470,7 +470,7 @@ regular faceting will not work for geographic visualization:
470470.. altair-plot ::
471471
472472 source = data.population_engineers_hurricanes().melt(id_vars=['state', 'id'])
473- us_states = gpd.read_file(data.us_10m.url, driver='TopoJSON', layer=' states' )
473+ us_states = gpd.read_file(data.us_10m.url, layer=" states" )
474474 gdf_comb = gpd.GeoDataFrame(source.join(us_states, on='id', rsuffix='_y'))
475475
476476 alt.Chart(gdf_comb).mark_geoshape().encode(
@@ -524,7 +524,7 @@ populous states. Using an ``alt.selection_point()`` we define a selection parame
524524 import geopandas as gpd
525525
526526 # load the data
527- us_states = gpd.read_file(data.us_10m.url, driver="TopoJSON", layer="states")
527+ us_states = gpd.read_file(data.us_10m.url, layer="states")
528528 us_population = data.population_engineers_hurricanes()[["state", "id", "population"]]
529529
530530 # define a pointer selection
@@ -583,8 +583,8 @@ We use here an elegant way to access the nested point coordinates from the geome
583583 import geopandas as gpd
584584
585585 # load data
586- gdf_quakies = gpd.read_file(data.earthquakes.url, driver="GeoJSON" )
587- gdf_world = gpd.read_file(data.world_110m.url, driver="TopoJSON ")
586+ gdf_quakies = gpd.read_file(data.earthquakes.url)
587+ gdf_world = gpd.read_file(data.world_110m.url, layer="countries ")
588588
589589 # define parameters
590590 range0 = alt.binding_range(min=-180, max=180, step=5, name='rotate longitude ')
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