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USA County Choropleth Maps in Python

How to create colormaped representations of USA counties by FIPS values in Python.

Required Packages

geopandas, pyshp and shapely must be installed for this figure factory.

Run the following commands to install the correct versions of the following modules:

In [1]:
!pip install geopandas==0.3.0
!pip install pyshp==1.2.10
!pip install shapely==1.6.3
Collecting geopandas==0.3.0
  Downloading https://files.pythonhosted.org/packages/0a/0e/8ae74743ed7915ddb7d70cc8dfa8fc0b9b9cc81205c6e288a01915a46192/geopandas-0.3.0-py2.py3-none-any.whl (888kB)
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Requirement already satisfied: cycler>=0.10 in /home/circleci/repo/venv/lib/python3.6/site-packages (from matplotlib->descartes->geopandas==0.3.0) (0.10.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/circleci/repo/venv/lib/python3.6/site-packages (from matplotlib->descartes->geopandas==0.3.0) (2.4.2)
Requirement already satisfied: kiwisolver>=1.0.1 in /home/circleci/repo/venv/lib/python3.6/site-packages (from matplotlib->descartes->geopandas==0.3.0) (1.1.0)
Requirement already satisfied: setuptools in /home/circleci/repo/venv/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->descartes->geopandas==0.3.0) (40.6.2)
Installing collected packages: descartes, geopandas
  Found existing installation: geopandas 0.5.0
    Uninstalling geopandas-0.5.0:
      Successfully uninstalled geopandas-0.5.0
Successfully installed descartes-1.1.0 geopandas-0.3.0
You are using pip version 18.1, however version 19.3.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Collecting pyshp==1.2.10
  Downloading https://files.pythonhosted.org/packages/c2/cc/1851049f2740d09c7bd8e4b464b1b78638723f6173e71d44aea12deca1f8/pyshp-1.2.10.tar.gz (176kB)
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Installing collected packages: pyshp
  Found existing installation: pyshp 2.1.0
    Uninstalling pyshp-2.1.0:
      Successfully uninstalled pyshp-2.1.0
  Running setup.py install for pyshp ... - done
Successfully installed pyshp-1.2.10
You are using pip version 18.1, however version 19.3.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Collecting shapely==1.6.3
  Downloading https://files.pythonhosted.org/packages/70/89/15016679ee1a327a798aacb3b9de709bfdeef679c10fc3e786202256049c/Shapely-1.6.3-cp36-cp36m-manylinux1_x86_64.whl (1.5MB)
    100% |████████████████████████████████| 1.5MB 20.3MB/s 
Installing collected packages: shapely
  Found existing installation: Shapely 1.6.4.post2
    Uninstalling Shapely-1.6.4.post2:
      Successfully uninstalled Shapely-1.6.4.post2
Successfully installed shapely-1.6.3
You are using pip version 18.1, however version 19.3.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.

If you are using Windows, follow this post to properly install geopandas and dependencies: http://geoffboeing.com/2014/09/using-geopandas-windows/. If you are using Anaconda, do not use PIP to install the packages above. Instead use conda to install them:

conda install plotly conda install geopandas

FIPS and Values

Every US state and county has an assined ID regulated by the US Federal Government under the term FIPS (Federal Information Processing Standards) codes. There are state codes and county codes: the 2016 state and county FIPS codes can be found at the US Census Website.

Combine a state FIPS code (eg. 06 for California) with a county FIPS code of the state (eg. 059 for Orange county) and this new state-county FIPS code (06059) uniquely refers to the specified state and county.

ff.create_choropleth only needs a list of FIPS codes and a list of values. Each FIPS code points to one county and each corresponding value in values determines the color of the county.

Simple Example

A simple example of this is a choropleth a few counties in California:

In [2]:
import plotly.figure_factory as ff

fips = ['06021', '06023', '06027',
        '06029', '06033', '06059',
        '06047', '06049', '06051',
        '06055', '06061']
values = range(len(fips))

fig = ff.create_choropleth(fips=fips, values=values)
fig.layout.template = None
fig.show()

Change the Scope

Even if your FIPS values belong to a single state, the scope defaults to the entire United States as displayed in the example above. Changing the scope of the choropleth shifts the zoom and position of the USA map. You can define the scope with a list of state names and the zoom will automatically adjust to include the state outlines of the selected states.

By default scope is set to ['USA'] which the API treats as identical to passing a list of all 50 state names:

['AK', 'AL', 'CA', ...]

State abbreviations (eg. CA) or the proper names (eg. California) as strings are accepted. If the state name is not recognized, the API will throw a Warning and indicate which FIPS values were ignored.

Another param used in the example below is binning_endpoints. If your values is a list of numbers, you can bin your values into half-open intervals on the real line.

In [3]:
import plotly.figure_factory as ff

import numpy as np
import pandas as pd

df_sample = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv')
df_sample_r = df_sample[df_sample['STNAME'] == 'California']

values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()

colorscale = [
    'rgb(193, 193, 193)',
    'rgb(239,239,239)',
    'rgb(195, 196, 222)',
    'rgb(144,148,194)',
    'rgb(101,104,168)',
    'rgb(65, 53, 132)'
]

fig = ff.create_choropleth(
    fips=fips, values=values, scope=['CA', 'AZ', 'Nevada', 'Oregon', ' Idaho'],
    binning_endpoints=[14348, 63983, 134827, 426762, 2081313], colorscale=colorscale,
    county_outline={'color': 'rgb(255,255,255)', 'width': 0.5}, round_legend_values=True,
    legend_title='Population by County', title='California and Nearby States'
)
fig.layout.template = None
fig.show()

Single State

In [4]:
import plotly.figure_factory as ff

import numpy as np
import pandas as pd

df_sample = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv')
df_sample_r = df_sample[df_sample['STNAME'] == 'Florida']

values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()

endpts = list(np.mgrid[min(values):max(values):4j])
colorscale = ["#030512","#1d1d3b","#323268","#3d4b94","#3e6ab0",
              "#4989bc","#60a7c7","#85c5d3","#b7e0e4","#eafcfd"]
fig = ff.create_choropleth(
    fips=fips, values=values, scope=['Florida'], show_state_data=True,
    colorscale=colorscale, binning_endpoints=endpts, round_legend_values=True,
    plot_bgcolor='rgb(229,229,229)',
    paper_bgcolor='rgb(229,229,229)',
    legend_title='Population by County',
    county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
    exponent_format=True,
)
fig.layout.template = None
fig.show()

Multiple States

In [5]:
import plotly.figure_factory as ff

import pandas as pd

NE_states = ['Connecticut', 'Maine', 'Massachusetts', 'New Hampshire', 'Rhode Island', 'Vermont']
df_sample = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv')
df_sample_r = df_sample[df_sample['STNAME'].isin(NE_states)]

values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()

colorscale = [
    'rgb(68.0, 1.0, 84.0)',
    'rgb(66.0, 64.0, 134.0)',
    'rgb(38.0, 130.0, 142.0)',
    'rgb(63.0, 188.0, 115.0)',
    'rgb(216.0, 226.0, 25.0)'
]

fig = ff.create_choropleth(
    fips=fips, values=values,
    scope=NE_states, county_outline={'color': 'rgb(255,255,255)', 'width': 0.5},
    legend_title='Population per county'

)
fig.update_layout(
    legend_x = 0,
    annotations = {'x': -0.12, 'xanchor': 'left'}
)

fig.layout.template = None
fig.show()

Simplify County, State Lines

Below is a choropleth that uses several other parameters. For a full list of all available params call help(ff.create_choropleth)

  • simplify_county determines the simplification factor for the counties. The larger the number, the fewer vertices and edges each polygon has. See http://toblerity.org/shapely/manual.html#object.simplify for more information.
  • simplify_state simplifies the state outline polygon. See the documentation for more information. Default for both simplify_county and simplif_state is 0.02

Note: This choropleth uses a divergent categorical colorscale. See http://react-colorscales.getforge.io/ for other cool colorscales.

In [6]:
import plotly.figure_factory as ff

import pandas as pd

scope = ['Oregon']
df_sample = pd.read_csv(
    'https://raw.githubusercontent.com/plotly/datasets/master/minoritymajority.csv'
)
df_sample_r = df_sample[df_sample['STNAME'].isin(scope)]

values = df_sample_r['TOT_POP'].tolist()
fips = df_sample_r['FIPS'].tolist()

colorscale = ["#8dd3c7", "#ffffb3", "#bebada", "#fb8072",
              "#80b1d3", "#fdb462", "#b3de69", "#fccde5",
              "#d9d9d9", "#bc80bd", "#ccebc5", "#ffed6f",
              "#8dd3c7", "#ffffb3", "#bebada", "#fb8072",
              "#80b1d3", "#fdb462", "#b3de69", "#fccde5",
              "#d9d9d9", "#bc80bd", "#ccebc5", "#ffed6f",
              "#8dd3c7", "#ffffb3", "#bebada", "#fb8072",
              "#80b1d3", "#fdb462", "#b3de69", "#fccde5",
              "#d9d9d9", "#bc80bd", "#ccebc5", "#ffed6f"]

fig = ff.create_choropleth(
    fips=fips, values=values, scope=scope,
    colorscale=colorscale, round_legend_values=True,
    simplify_county=0, simplify_state=0,
    county_outline={'color': 'rgb(15, 15, 55)', 'width': 0.5},
    state_outline={'width': 1},
    legend_title='pop. per county',
    title='Oregon'
)

fig.layout.template = None
fig.show()