diff --git a/examples/gallery/lines/roads.py b/examples/gallery/lines/roads.py
new file mode 100755
index 00000000000..7c1e75ef943
--- /dev/null
+++ b/examples/gallery/lines/roads.py
@@ -0,0 +1,46 @@
+# ruff: noqa: RUF003
+"""
+Roads
+=====
+
+The :meth:`pygmt.Figure.plot` method allows us to plot geographical data such
+as lines which are stored in a :class:`geopandas.GeoDataFrame` object. Use
+:func:`geopandas.read_file` to load data from any supported OGR format such as
+a shapefile (.shp), GeoJSON (.geojson), geopackage (.gpkg), etc. Then, pass the
+:class:`geopandas.GeoDataFrame` as an argument to the ``data`` parameter of
+:meth:`pygmt.Figure.plot`, and style the geometry using the ``pen`` parameter.
+"""
+
+# %%
+import geopandas as gpd
+import pygmt
+
+# Read shapefile data using geopandas
+gdf = gpd.read_file(
+    "http://www2.census.gov/geo/tiger/TIGER2015/PRISECROADS/tl_2015_15_prisecroads.zip"
+)
+# The dataset contains different road types listed in the RTTYP column,
+# here we select the following ones to plot:
+roads_common = gdf[gdf.RTTYP == "M"]  # Common name roads
+roads_state = gdf[gdf.RTTYP == "S"]  # State recognized roads
+roads_interstate = gdf[gdf.RTTYP == "I"]  # Interstate roads
+
+fig = pygmt.Figure()
+
+# Define target region around Oʻahu (Hawaiʻi)
+region = [-158.3, -157.6, 21.2, 21.75]  # xmin, xmax, ymin, ymax
+
+title = "Main roads of O`ahu (Hawai`i)"  # Approximating the Okina letter ʻ with `
+fig.basemap(region=region, projection="M12c", frame=["af", f"WSne+t{title}"])
+fig.coast(land="gray", water="dodgerblue4", shorelines="1p,black")
+
+# Plot the individual road types with different pen settings and assign labels
+# which are displayed in the legend
+fig.plot(data=roads_common, pen="5p,dodgerblue", label="CommonName")
+fig.plot(data=roads_state, pen="2p,gold", label="StateRecognized")
+fig.plot(data=roads_interstate, pen="2p,red", label="Interstate")
+
+# Add legend
+fig.legend()
+
+fig.show()
diff --git a/examples/gallery/maps/choropleth_map.py b/examples/gallery/maps/choropleth_map.py
new file mode 100755
index 00000000000..6c43d24d3dd
--- /dev/null
+++ b/examples/gallery/maps/choropleth_map.py
@@ -0,0 +1,57 @@
+"""
+Choropleth map
+==============
+
+The :meth:`pygmt.Figure.plot` method allows us to plot geographical data such
+as polygons which are stored in a :class:`geopandas.GeoDataFrame` object. Use
+:func:`geopandas.read_file` to load data from any supported OGR format such as
+a shapefile (.shp), GeoJSON (.geojson), geopackage (.gpkg), etc. You can also
+use a full URL pointing to your desired data source. Then, pass the
+:class:`geopandas.GeoDataFrame` as an argument to the ``data`` parameter of
+:meth:`pygmt.Figure.plot`, and style the geometry using the ``pen`` parameter.
+To fill the polygons based on a corresponding column you need to set
+``fill="+z"`` as well as select the appropriate column using the ``aspatial``
+parameter as shown in the example below.
+"""
+
+# %%
+import geopandas as gpd
+import pygmt
+
+# Read polygon data using geopandas
+gdf = gpd.read_file("https://geodacenter.github.io/data-and-lab/data/airbnb.zip")
+
+fig = pygmt.Figure()
+
+fig.basemap(
+    region=gdf.total_bounds[[0, 2, 1, 3]],
+    projection="M6c",
+    frame="+tPopulation of Chicago",
+)
+
+# The dataset contains different attributes, here we select
+# the "population" column to plot.
+
+# First, we define the colormap to fill the polygons based on
+# the "population" column.
+pygmt.makecpt(
+    cmap="acton",
+    series=[gdf["population"].min(), gdf["population"].max(), 10],
+    continuous=True,
+    reverse=True,
+)
+
+# Next, we plot the polygons and fill them using the defined colormap.
+# The target column is defined by the aspatial parameter.
+fig.plot(
+    data=gdf,
+    pen="0.3p,gray10",
+    fill="+z",
+    cmap=True,
+    aspatial="Z=population",
+)
+
+# Add colorbar legend
+fig.colorbar(frame="x+lPopulation", position="jML+o-0.5c+w3.5c/0.2c")
+
+fig.show()