How to use Basemap (Python) to build a US with 50 states?

Question

I know that the powerful Basemap array can be used to build a map of the United States with state borders. I adapted this example from the GitHub database to display 48 states colored by their respective population density:

Now my question is: is there an easy way to add Alaska and Hawaii to this map and place them in a normal place, for example. bottom left corner? Something like that:

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap as Basemap from matplotlib.colors import rgb2hex from matplotlib.patches import Polygon # Lambert Conformal map of lower 48 states. m = Basemap(llcrnrlon=-119,llcrnrlat=22,urcrnrlon=-64,urcrnrlat=49, projection='lcc',lat_1=33,lat_2=45,lon_0=-95) # draw state boundaries. # data from US Census Bureau # http://www.census.gov/geo/www/cob/st2000.html shp_info = m.readshapefile('st99_d00','states',drawbounds=True) # population density by state from # http://en.wikipedia.org/wiki/List_of_U.S._states_by_population_density 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, 'Hawaii': 72.83, '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, 'Alaska': 0.42} # choose a color for each state based on population density. colors={} statenames=[] cmap = plt.cm.hot # use 'hot' colormap vmin = 0; vmax = 450 # set range. for shapedict in m.states_info: statename = shapedict['NAME'] # skip DC and Puerto Rico. if statename not in ['District of Columbia','Puerto Rico']: pop = popdensity[statename] # calling colormap with value between 0 and 1 returns # rgba value. Invert color range (hot colors are high # population), take sqrt root to spread out colors more. colors[statename] = cmap(1.-np.sqrt((pop-vmin)/(vmax-vmin)))[:3] statenames.append(statename) # cycle through state names, color each one. ax = plt.gca() # get current axes instance for nshape,seg in enumerate(m.states): # skip DC and Puerto Rico. if statenames[nshape] not in ['District of Columbia','Puerto Rico']: color = rgb2hex(colors[statenames[nshape]]) poly = Polygon(seg,facecolor=color,edgecolor=color) ax.add_patch(poly) plt.title('Filling State Polygons by Population Density') plt.show() 

Answer 1

For everyone who is interested, I was able to fix it myself. The coordinates (x, y) of each segment (for Alaska and Hawaii) must be translated. I also reduce Alaska to 35% before translating it.

The second for loop should be modified as follows:

 for nshape,seg in enumerate(m.states): # skip DC and Puerto Rico. if statenames[nshape] not in ['Puerto Rico', 'District of Columbia']: # Offset Alaska and Hawaii to the lower-left corner. if statenames[nshape] == 'Alaska': # Alaska is too big. Scale it down to 35% first, then transate it. seg = list(map(lambda (x,y): (0.35*x + 1100000, 0.35*y-1300000), seg)) if statenames[nshape] == 'Hawaii': seg = list(map(lambda (x,y): (x + 5100000, y-900000), seg)) color = rgb2hex(colors[statenames[nshape]]) poly = Polygon(seg,facecolor=color,edgecolor=color) ax.add_patch(poly) 

Here is the new US map (using the Greens color map).

Answer 2

The above answer is great and helped me a lot.

I noticed that there are many tiny islands that stretch for miles beyond the 8 main islands of the Hawaiian Islands. They create small dots in Arizona, California, and Oregon (or Nevada and Idaho) depending on how you translated Hawaii. To remove them, you need a condition for the polygon area. To do this, it is useful to make one loop through the states_info object to do this:

 # Hawaii has 8 main islands but several tiny atolls that extend for many miles. # This is the area cutoff between the 8 main islands and the tiny atolls. ATOLL_CUTOFF = 0.005 m = Basemap(llcrnrlon=-121,llcrnrlat=20,urcrnrlon=-62,urcrnrlat=51, projection='lcc',lat_1=32,lat_2=45,lon_0=-95) # load the shapefile, use the name 'states' m.readshapefile('st99_d00', name='states', drawbounds=True) ax = plt.gca() for i, shapedict in enumerate(m.states_info): # Translate the noncontiguous states: if shapedict['NAME'] in ['Alaska', 'Hawaii']: seg = m.states[int(shapedict['SHAPENUM'] - 1)] # Only include the 8 main islands of Hawaii so that we don't put dots in the western states. if shapedict['NAME'] == 'Hawaii' and float(shapedict['AREA']) > ATOLL_CUTOFF: seg = list(map(lambda (x,y): (x + 5200000, y-1400000), seg)) # Alaska is large. Rescale it. elif shapedict['NAME'] == 'Alaska': seg = list(map(lambda (x,y): (0.35*x + 1100000, 0.35*y-1300000), seg)) poly = Polygon(seg, facecolor='white', edgecolor='black', linewidth=.5) ax.add_patch(poly)