Loading Common Maps

Simple Example

Import MapScaler and load a map of the US Counties:

import mapscaler as ms
loader = ms.MapLoader()
df = loader.fetch_counties()['df']

That’s it! You now have a map of the counties.

import matplotlib.pyplot as plt
import geoplot as gplt

#Reduce to the lower 48 for an easier demonstration
df = df[df.STATE_FIPS != '02'] # remove AK
df = df[df.STATE_FIPS != '15'] # remove HI
df = df[df.STATE_FIPS != '72'] # remove PR

gplt.polyplot(
    df,
    projection=gplt.crs.AlbersEqualArea(),
    figsize=(15,8),
    )
plt.suptitle('Contiguous United States', fontsize=20, ha='center')
plt.show()
US Counties

Documentation

class mapscaler.MapLoader

Bases: object

Quickly load common maps of the US as GeoPandas Dataframes.

Note

In most cases, the least detailed map options are used for performance. All MapLoader methods return a sources item with links to find maps with higher detail, if available.

fetch_counties(state_fips=None)

Load a map of the US counties.

Parameters:state_fips (str) – Optional - State FIPS code as a string. Default is None which loads all states.
Returns:dict with 2 keys:

df is a GeoPandas DataFrame, including a column of shape objects.

sources is a dict of links to the original map source.

Return type:dict
fetch_states()

Load a map of the US states, including Puerto Rico.

Returns:dict with 2 keys:

df is a GeoPandas DataFrame, including a column of shape objects.

sources is a dict of links to the original map source.

Return type:dict