ceteri

proj = gplt.crs.AlbersEqualArea(
  central_longitude=-98,
  central_latitude=39.5
)

ax = gplt.voronoi(
  continental_usa_cities,
  hue="ELEV_IN_FT",
  clip=contiguous_usa,
  projection=proj,

ceteri

melbourne = gpd.read_file(gplt.datasets.get_path("melbourne"))
df = gpd.read_file(gplt.datasets.get_path("melbourne_schools"))
melbourne_primary_schools = df.query('School_Type == "Primary"')

ax = gplt.voronoi(
  melbourne_primary_schools,
  clip=melbourne,
  linewidth=0.5,
  edgecolor="white",
  projection=gcrs.Mercator()

ceteri

import warnings
warnings.filterwarnings("ignore", "GeoSeries.isna", UserWarning)

ceteri

gplt.pointplot(
  continental_usa_cities,
  projection=gcrs.AlbersEqualArea(),
  hue="ELEV_IN_FT",
  scheme=scheme,
  cmap="inferno_r",
  legend=True
)

ceteri

scheme = mc.Quantiles(continental_usa_cities["ELEV_IN_FT"], k=10)

ceteri

gplt.cartogram(
  geo_obesity,
  scale="Percent",
  projection=gcrs.AlbersEqualArea()
)

ceteri

geo_obesity = contiguous_usa.set_index("state").join(obesity.set_index("State"))
geo_obesity.head()

ceteri

obesity = pd.read_csv(gplt.datasets.get_path("obesity_by_state"), sep="\t")
obesity.head()

ceteri

ax = gplt.polyplot(contiguous_usa, projection=gcrs.AlbersEqualArea())

gplt.kdeplot(
  continental_usa_cities, 
  cmap="Reds", 
  shade=True, 
  clip=contiguous_usa, 
  ax=ax
)

ceteri

boroughs = gpd.read_file(gplt.datasets.get_path("nyc_boroughs"))
collisions = gpd.read_file(gplt.datasets.get_path("nyc_collision_factors"))
ax = gplt.polyplot(boroughs, projection=gcrs.AlbersEqualArea())
gplt.kdeplot(collisions, cmap="Reds", shade=True, clip=boroughs, ax=ax)