Showing Alaska and Hawaii in Cartopy map

The following code creates a map of continental US states that is shaded by population density. I want to create a similar map (my data is not actually pop density, but this is an easy example), except that it also includes the states of Alaska and Hawaii.

Specifically I would like to have Alaska/Hawaii show up in the figure, but be moved so that they are below the part of the figure showing the continental US. Or something along those lines.

Any idea how I would create such a map using Cartopy?

import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader

fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal())

ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic())

shapename = 'admin_1_states_provinces_lakes_shp'
states_shp = shpreader.natural_earth(resolution='110m',
                                     category='cultural', name=shapename)

popdensity = {
    'New Jersey':  438.00,
    'Rhode Island':   387.35,
    'Massachusetts':   312.68,
    'Connecticut':    271.40,
    'Maryland':   209.23,
    'New York':    155.18,
    'Delaware':    154.87,
    'Florida':     114.43,
    'Ohio':  107.05,
    'Pennsylvania':  105.80,
    'Illinois':    86.27,
    'California':  83.85,
    'Virginia':    69.03,
    'Michigan':    67.55,
    'Indiana':    65.46,
    'North Carolina':  63.80,
    'Georgia':     54.59,
    'Tennessee':   53.29,
    'New Hampshire':   53.20,
    'South Carolina':  51.45,
    'Louisiana':   39.61,
    'Kentucky':   39.28,
    'Wisconsin':  38.13,
    'Washington':  34.20,
    'Alabama':     33.84,
    'Missouri':    31.36,
    'Texas':   30.75,
    'West Virginia':   29.00,
    'Vermont':     25.41,
    'Minnesota':  23.86,
    'Mississippi':   23.42,
    'Iowa':  20.22,
    'Arkansas':    19.82,
    'Oklahoma':    19.40,
    'Arizona':     17.43,
    'Colorado':    16.01,
    'Maine':  15.95,
    'Oregon':  13.76,
    'Kansas':  12.69,
    'Utah':  10.50,
    'Nebraska':    8.60,
    'Nevada':  7.03,
    'Idaho':   6.04,
    'New Mexico':  5.79,
    'South Dakota':  3.84,
    'North Dakota':  3.59,
    'Montana':     2.39,
    'Wyoming':      1.96}

ax.background_patch.set_visible(False)
ax.outline_patch.set_visible(False)

ax.set_title('State Population Density')

for state in shpreader.Reader(states_shp).records():

    edgecolor = 'black'

    try:
        # use the name of this state to get pop_density
        state_dens = popdensity[ state.attributes['name'] ]
    except:
        state_dens = 0

    # simple scheme to assign color to each state
    if state_dens < 40:
        facecolor = "lightyellow"
    elif state_dens > 200:
        facecolor = "red"
    else:
        facecolor = "pink"

    # `state.geometry` is the polygon to plot
    ax.add_geometries([state.geometry], ccrs.PlateCarree(),
                      facecolor=facecolor, edgecolor=edgecolor)

plt.show()

The figure this (currently) creates is as follows:

Solution

To plot inset maps as parts of a main map is challenging. You will need to create an axes for plotting each inset map and place it on the figure at proper location and relative scale. Here is a working code that you can experiment with.

import matplotlib.pyplot as plt
import cartopy
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader

import shapely.geometry as sgeom

# A function that draws inset map, ++
# ===================================
def add_insetmap(axes_extent, map_extent, state_name, facecolor, edgecolor, geometry):
    # create new axes, set its projection
    use_projection = ccrs.Mercator()     # preserve shape well
    #use_projection = ccrs.PlateCarree()   # large distortion in E-W for Alaska
    geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))
    sub_ax = plt.axes(axes_extent, projection=use_projection)  # normal units
    sub_ax.set_extent(map_extent, geodetic)  # map extents

    # add basic land, coastlines of the map
    # you may comment out if you don't need them
    sub_ax.add_feature(cartopy.feature.LAND)
    sub_ax.coastlines()

    sub_ax.set_title(state_name)

    # add map `geometry` here
    sub_ax.add_geometries([geometry], ccrs.PlateCarree(), \
                          facecolor=facecolor, edgecolor=edgecolor)
    # +++ more features can be added here +++

    # plot box around the map
    extent_box = sgeom.box(map_extent[0], map_extent[2], map_extent[1], map_extent[3])
    sub_ax.add_geometries([extent_box], ccrs.PlateCarree(), color='none', linewidth=0.05)


fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal())

ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic())

shapename = 'admin_1_states_provinces_lakes_shp'
states_shp = shpreader.natural_earth(resolution='110m',
                                     category='cultural', name=shapename)

popdensity = {
    'New Jersey':  438.00,
    'Rhode Island':   387.35,
    'Massachusetts':   312.68,
    'Connecticut':    271.40,
    'Maryland':   209.23,
    'New York':    155.18,
    'Delaware':    154.87,
    'Florida':     114.43,
    'Ohio':  107.05,
    'Pennsylvania':  105.80,
    'Illinois':    86.27,
    'California':  83.85,
    'Virginia':    69.03,
    'Michigan':    67.55,
    'Indiana':    65.46,
    'North Carolina':  63.80,
    'Georgia':     54.59,
    'Tennessee':   53.29,
    'New Hampshire':   53.20,
    'South Carolina':  51.45,
    'Louisiana':   39.61,
    'Kentucky':   39.28,
    'Wisconsin':  38.13,
    'Washington':  34.20,
    'Alabama':     33.84,
    'Missouri':    31.36,
    'Texas':   30.75,
    'West Virginia':   29.00,
    'Vermont':     25.41,
    'Minnesota':  23.86,
    'Mississippi':   23.42,
    'Iowa':  20.22,
    'Arkansas':    19.82,
    'Oklahoma':    19.40,
    'Arizona':     17.43,
    'Colorado':    16.01,
    'Maine':  15.95,
    'Oregon':  13.76,
    'Kansas':  12.69,
    'Utah':  10.50,
    'Nebraska':    8.60,
    'Nevada':  7.03,
    'Idaho':   6.04,
    'New Mexico':  5.79,
    'South Dakota':  3.84,
    'North Dakota':  3.59,
    'Montana':     2.39,
    'Wyoming':      1.96}

ax.background_patch.set_visible(False)
ax.outline_patch.set_visible(False)

ax.set_title('State Population Density')

for state in shpreader.Reader(states_shp).records():


    edgecolor = 'black'

    try:
        # use the name of this state to get pop_density
        state_dens = popdensity[ state.attributes['name'] ]
    except:
        state_dens = 0

    # simple scheme to assign color to each state
    if state_dens < 40:
        facecolor = "lightyellow"
    elif state_dens > 200:
        facecolor = "red"
    else:
        facecolor = "pink"

    # special handling for the 2 states
    # ---------------------------------
    if state.attributes['name'] in ("Alaska", "Hawaii"):
        # print("state.attributes['name']:", state.attributes['name'])

        state_name = state.attributes['name']

        # prep map settings
        # experiment with the numbers in both `_extents` for your best results
        if state_name == "Alaska":
            # (1) Alaska
            map_extent = (-178, -135, 46, 73)    # degrees: (lonmin,lonmax,latmin,latmax)
            axes_extent = (0.04, 0.06, 0.29, 0.275) # axes units: 0 to 1, (LLx,LLy,width,height)

        if state_name == "Hawaii":
            # (2) Hawii
            map_extent = (-162, -152, 15, 25)
            axes_extent = (0.27, 0.06, 0.15, 0.15)

        # add inset maps
        add_insetmap(axes_extent, map_extent, state_name, \
                     facecolor, \
                     edgecolor, \
                     state.geometry)

    # the other (conterminous) states go here
    else:
        # `state.geometry` is the polygon to plot
        ax.add_geometries([state.geometry], ccrs.PlateCarree(),
                          facecolor=facecolor, edgecolor=edgecolor)

plt.show()

The output plot will be: