plotnine.geom_map

geom_map(
    mapping=None,
    data=None,
    *,
    stat="identity",
    position="identity",
    na_rm=False,
    inherit_aes=True,
    show_legend=None,
    raster=False,
    **kwargs
)

Draw map feature

The map feature are drawn without any special projections.

Parameters

mapping : aes = None

Aesthetic mappings created with aes. If specified and inherit_aes=True, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

Aesthetic	Default value
geometry
alpha	`1`
color	`'#111111'`
fill	`'#333333'`
group
linetype	`'solid'`
shape	`'o'`
size	`0.5`
stroke	`0.5`

The bold aesthetics are required.

data : DataFrame = None

The data to be displayed in this layer. If None, the data from from the ggplot() call is used. If specified, it overrides the data from the ggplot() call.

stat : str | stat = "identity"

The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

position : str | position = "identity"

Position adjustment. If it is a string, it must be registered and known to Plotnine.

na_rm : bool = False

If False, removes missing values with a warning. If True silently removes missing values.

inherit_aes : bool = True

If False, overrides the default aesthetics.

show_legend : bool | dict = None

Whether this layer should be included in the legends. None the default, includes any aesthetics that are mapped. If a bool, False never includes and True always includes. A dict can be used to exclude specific aesthetis of the layer from showing in the legend. e.g show_legend={'color': False}, any other aesthetic are included by default.

raster : bool = False

If True, draw onto this layer a raster (bitmap) object even ifthe final image is in vector format.

**kwargs : Any = {}

Aesthetics or parameters used by the stat.

Notes

This geom is best suited for plotting a shapefile read into geopandas dataframe. The dataframe should have a geometry column.

Examples

import geopandas as gp
import geodatasets
from plotnine import (
    ggplot,
    aes,
    coord_fixed,
    facet_wrap,
    geom_map,
    geom_text,
    labs,
    scale_fill_brewer,
    scale_fill_continuous,
    scale_x_continuous,
    scale_y_continuous,
    scale_size_continuous,
    stage,
    coord_cartesian,
    element_line,
    element_rect,
    element_text,
    theme_void,
    theme,
)

Define some useful functions

def good_centroid(geometry):
    """
    Calculate "good" centroids for polygons in the geometry

    The polygon is projected onto the Equal Area Cylindrical projection,
    the centroids are computed, then mapped back onto the original
    coordinate system.
    """
    return geometry.to_crs("+proj=cea").centroid.to_crs(geometry.crs)


def overlay_color(rgb_hex_colors, light="white", dark="black", threshold=0.5):
    """
    Decide which color is suitable to write onto the given colors
    """
    def luminance(rgb_hex):
        """
        Calculate the Luminance ([0, 1]) of a hex color
        """
        r = int(rgb_hex[1:3], 16)
        g = int(rgb_hex[3:5], 16)
        b = int(rgb_hex[5:], 16)
        luma = (r*0.299 + g*0.587 + b*0.0722) / 256
        return luma
    return [light if luminance(x) < threshold  else dark for x in rgb_hex_colors]

A Map from `geodatasets`

We are going to use a dataset from the geodatasets package. We can explore the dataset providers at geodatasets.data, each of which has one or more datasets:

We choose the dataset of Sudden Infant Death (SIDS) counts from counties in North Carolina. We can see some meta information about that dataset. You can view the details with geodatasets.data.geoda.sids.

Download the dataset we want (it is automatically cached).

geodatasets.fetch("geoda sids")

Read data

sids = gp.read_file(geodatasets.get_path("geoda.sids"))
sids.head()

	AREA	PERIMETER	CNTY_	CNTY_ID	NAME	FIPS	FIPSNO	CRESS_ID	BIR74	SID74	NWBIR74	BIR79	SID79	NWBIR79	geometry
0	0.114	1.442	1825	1825	Ashe	37009	37009	5	1091.0	1.0	10.0	1364.0	0.0	19.0	POLYGON ((-81.47276 36.23436, -81.54084 36.272...
1	0.061	1.231	1827	1827	Alleghany	37005	37005	3	487.0	0.0	10.0	542.0	3.0	12.0	POLYGON ((-81.23989 36.36536, -81.24069 36.379...
2	0.143	1.630	1828	1828	Surry	37171	37171	86	3188.0	5.0	208.0	3616.0	6.0	260.0	POLYGON ((-80.45634 36.24256, -80.47639 36.254...
3	0.070	2.968	1831	1831	Currituck	37053	37053	27	508.0	1.0	123.0	830.0	2.0	145.0	MULTIPOLYGON (((-76.00897 36.3196, -76.01735 3...
4	0.153	2.206	1832	1832	Northampton	37131	37131	66	1421.0	9.0	1066.0	1606.0	3.0	1197.0	POLYGON ((-77.21767 36.24098, -77.23461 36.214...

The sids data has two periods 1974 - 1978 and 1979 - 1985. For each period there is a number of births and a number of deaths. We want to visually compare the deaths per 1000 in each county for the two periods.

data = sids.copy()

# Calculate the death per 1000
data["1974 - 1978"] = (data["SID74"] / data["BIR74"]) * 1000
data["1979 - 1984"] = (data["SID79"] / data["BIR79"]) * 1000

# Calculate center coordinates for the counties
data["center_lon"] = good_centroid(data.geometry).x
data["center_lat"] = good_centroid(data.geometry).y

# Make the data
data = data.melt(
    id_vars=["NAME", "geometry", "center_lon", "center_lat"],
    value_vars=["1974 - 1978", "1979 - 1984"],
    var_name="period",
    value_name="deaths_per_1k",
).rename(
    {"NAME": "county"},
    axis=1
)

data.head()

	county	geometry	center_lon	center_lat	period	deaths_per_1k
0	Ashe	POLYGON ((-81.47276 36.23436, -81.54084 36.272...	-81.498245	36.431263	1974 - 1978	0.916590
1	Alleghany	POLYGON ((-81.23989 36.36536, -81.24069 36.379...	-81.125138	36.490963	1974 - 1978	0.000000
2	Surry	POLYGON ((-80.45634 36.24256, -80.47639 36.254...	-80.685736	36.412381	1974 - 1978	1.568381
3	Currituck	MULTIPOLYGON (((-76.00897 36.3196, -76.01735 3...	-76.027392	36.407026	1974 - 1978	1.968504
4	Northampton	POLYGON ((-77.21767 36.24098, -77.23461 36.214...	-77.410484	36.422110	1974 - 1978	6.333568

Plot the data

# Gallery, maps

(
    ggplot(data)
    + geom_map(aes(fill="deaths_per_1k"))
    + geom_text(
        aes(
            "center_lon",
            "center_lat",
            label="county",
            color=stage("deaths_per_1k", after_scale="overlay_color(color)")
        ),
        size=6,
        show_legend=False
    )
    + scale_fill_continuous(
        name="Deaths Per 1000",
        cmap_name="plasma",
        breaks=[0, 2.5, 5, 7.5, 10],
        labels=["0", "2.5", "5", "7.5", "10"],
        limits=[0, 10],
    )
    + facet_wrap(
        "period",
        ncol=1,
        labeller=lambda s: f"Sudden Infant Deaths in the Period {s}"
    )
    + coord_fixed(expand=False)
    + theme_void()
    + theme(
        figure_size=(12, 8),
        legend_position=(0, 0),
        legend_direction="horizontal",
        legend_title_position="top",
        plot_margin=0.01,
        plot_background=element_rect(fill="white"),
        panel_spacing=0.025,
        legend_frame=element_rect(color="black"),
        legend_ticks=element_line(color="black"),
        strip_text=element_text(size=12),
    )
)

The Territories of Westeros

Layering different features on a Map

Read data and select features in Westeros only.

continents = gp.read_file("data/lands-of-ice-and-fire/continents.shp")
islands = gp.read_file("data/lands-of-ice-and-fire/islands.shp")
lakes = gp.read_file("data/lands-of-ice-and-fire/lakes.shp")
rivers = gp.read_file("data/lands-of-ice-and-fire/rivers.shp")
political = gp.read_file("data/lands-of-ice-and-fire/political.shp")
wall = gp.read_file("data/lands-of-ice-and-fire/wall.shp")
roads = gp.read_file("data/lands-of-ice-and-fire/roads.shp")
locations = gp.read_file("data/lands-of-ice-and-fire/locations.shp")

westeros = continents.query('name=="Westeros"')
islands = islands.query('continent=="Westeros" and name!="Summer Islands"')
lakes = lakes.query('continent=="Westeros"')
rivers = rivers.query('continent=="Westeros"')
roads = roads.query('continent=="Westeros"')

wg = westeros.geometry[0]
bool_idx = [wg.contains(g) for g in locations.geometry]
westeros_locations = locations[bool_idx]
cities = westeros_locations[westeros_locations["type"] == "City"].copy()

Create map by placing the features in layers in an order that limits obstraction.

The GeoDataFrame.geometry.centroid property has the center coordinates of polygons, we use these to place the labels of the political regions.

# Gallery, maps

# colors
water_color = "#a3ccff"
wall_color = "white"
road_color = "brown"


# Create label text by merging the territory name and
# the claimant to the territory
def fmt_labels(names, claimants):
    labels = []
    for name, claimant in zip(names, claimants):
        if name:
            labels.append("{} ({})".format(name, claimant))
        else:
            labels.append("({})".format(claimant))
    return labels


def calculate_center(df):
    """
    Calculate the centre of a geometry

    This method first converts to a planar crs, gets the centroid
    then converts back to the original crs. This gives a more
    accurate
    """
    original_crs = df.crs
    planar_crs = "EPSG:3857"
    return df["geometry"].to_crs(planar_crs).centroid.to_crs(original_crs)


political["center"] = calculate_center(political)
cities["center"] = calculate_center(cities)

(
    ggplot()
    + geom_map(westeros, fill=None)
    + geom_map(islands, fill=None)
    + geom_map(political, aes(fill="ClaimedBy"), color=None, show_legend=False)
    + geom_map(wall, color=wall_color, size=2)
    + geom_map(lakes, fill=water_color, color=None)
    + geom_map(rivers, aes(size="size"), color=water_color, show_legend=False)
    + geom_map(roads, aes(size="size"), color=road_color, alpha=0.5, show_legend=False)
    + geom_map(cities, size=1)
    + geom_text(
        political,
        aes("center.x", "center.y", label="fmt_labels(name, ClaimedBy)"),
        size=8,
        fontweight="bold",
    )
    + geom_text(
        cities,
        aes("center.x", "center.y", label="name"),
        size=8,
        ha="left",
        nudge_x=0.20,
    )
    + labs(title="The Political Territories of Westeros")
    + scale_fill_brewer(type="qual", palette=8)
    + scale_x_continuous(expand=(0, 0, 0, 1))
    + scale_y_continuous(expand=(0, 1, 0, 0))
    + scale_size_continuous(range=(0.4, 1))
    + coord_cartesian()
    + theme_void()
    + theme(figure_size=(8, 12), panel_background=element_rect(fill=water_color))
)

Credit: cadaei of the cartographersguild website forum.

Source: A Map from `geodatasets`

Parameters

Notes

Examples

A Map from geodatasets

The Territories of Westeros

A Map from `geodatasets`