Overture Maps Loader¶

loader = OvertureMapsLoader()
paris = geocode_to_region_gdf("Paris")
paris_features_gdf = loader.load(paris)
paris_features_gdf

ax = paris.plot(color="#F8EDED", figsize=(16, 16))

# plot water
water_columns = [c for c in paris_features_gdf.columns if "water" in c]
water_data = paris_features_gdf[paris_features_gdf[water_columns].any(axis=1)]
water_data.plot(ax=ax, color="#FF8225", markersize=0)

# plot_roads
roads_data = paris_features_gdf[paris_features_gdf["transportation|segment|road"]]
roads_data.plot(ax=ax, color="#B43F3F", markersize=0, linewidth=0.25)

# plot buildings
building_columns = [c for c in paris_features_gdf.columns if c.startswith("buildings")]
buildings_data = paris_features_gdf[paris_features_gdf[building_columns].any(axis=1)]
buildings_data.plot(ax=ax, color="#173B45", markersize=0)

paris.boundary.plot(ax=ax, color="#173B45", linewidth=2, alpha=0.5)

xmin, ymin, xmax, ymax = paris.total_bounds
ax.set_xlim(xmin - 0.001, xmax + 0.001)
ax.set_ylim(ymin - 0.001, ymax + 0.001)

ax.set_axis_off()

manhattan_bbox = box(-73.994551, 40.762396, -73.936872, 40.804239)
loader = OvertureMapsLoader(hierarchy_depth=None)
new_york_features_gdf = loader.load(manhattan_bbox)
new_york_features_gdf

new_york_features_gdf.drop(columns=GEOMETRY_COLUMN).sum().sort_values(ascending=False).head(20)

default_confidence_loader = OvertureMapsLoader(
    theme_type_pairs=[("places", "place")], places_use_primary_category_only=True
)
strict_confidence_loader = OvertureMapsLoader(
    theme_type_pairs=[("places", "place")],
    places_minimal_confidence=0.99,
    places_use_primary_category_only=True,
)
songpa = geocode_to_region_gdf("Songpa-gu, Seoul")
songpa_default_confidence_features_gdf = default_confidence_loader.load(songpa)
songpa_strict_confidence_features_gdf = strict_confidence_loader.load(songpa)

print(f"Default confidence score: {len(songpa_default_confidence_features_gdf)}")
print(f"Strict confidence score:  {len(songpa_strict_confidence_features_gdf)}")

songpa_strict_confidence_features_df = songpa_strict_confidence_features_gdf.drop(
    columns=GEOMETRY_COLUMN
)
songpa_strict_confidence_features_df.sum().loc[lambda x: x > 0].sort_values(ascending=False)

m = songpa_default_confidence_features_gdf.loc[
    songpa_default_confidence_features_gdf.index.difference(
        songpa_strict_confidence_features_gdf.index
    )
].geometry.explore(
    tiles="CartoDB Voyager",
    color="gray",
    tooltip=False,
    style_kwds=dict(opacity=0.25, stroke=False),
)
songpa.boundary.explore(m=m, color="black")

songpa_gdf_with_categories = songpa_strict_confidence_features_gdf.join(
    songpa_strict_confidence_features_df.dot(songpa_strict_confidence_features_df.columns).rename(
        "category"
    )
)
songpa_gdf_with_categories.geometry.explore(
    m=m,
    tooltip=False,
    marker_kwds=dict(radius=6),
    style_kwds=dict(color="black", fillOpacity=1),
)
songpa_gdf_with_categories[[GEOMETRY_COLUMN, "category"]].explore(
    m=m,
    column="category",
    tooltip=["feature_id", "category"],
    cmap="tab20",
    marker_kwds=dict(radius=4),
    style_kwds=dict(fillOpacity=1),
)