USA County Choropleth Maps in Python

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


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Deprecation warning

This page describes a legacy "figure factory" method for creating map-like figures using self-filled scatter traces. This is no longer the recommended way to make county-level choropleth maps, instead we recommend using a GeoJSON-based approach to making outline choropleth maps or the alternative Mapbox tile-based choropleth maps.

Required Packages

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

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

In [1]:
!pip install plotly-geo==1.0.0
!pip install geopandas==0.3.0
!pip install pyshp==1.2.10
!pip install shapely==1.6.3
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Installing collected packages: descartes, geopandas
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  Preparing metadata (setup.py) ... - error
  error: subprocess-exited-with-error
  
  × python setup.py egg_info did not run successfully.
   exit code: 1
  ╰─> [12 lines of output]
      Failed `CDLL(libgeos_c.so.1)`
      Failed `CDLL(libgeos_c.so)`
      Traceback (most recent call last):
        File "<string>", line 36, in <module>
        File "<pip-setuptools-caller>", line 34, in <module>
        File "/tmp/pip-install-3q0w65jy/shapely_7e3e907d0f0242e2b79b942aac3e8d89/setup.py", line 80, in <module>
          from shapely._buildcfg import geos_version_string, geos_version, \
        File "/tmp/pip-install-3q0w65jy/shapely_7e3e907d0f0242e2b79b942aac3e8d89/shapely/_buildcfg.py", line 167, in <module>
          fallbacks=['libgeos_c.so.1', 'libgeos_c.so'])
        File "/tmp/pip-install-3q0w65jy/shapely_7e3e907d0f0242e2b79b942aac3e8d89/shapely/_buildcfg.py", line 161, in load_dll
          libname, fallbacks or []))
      OSError: Could not find library geos_c or load any of its variants ['libgeos_c.so.1', 'libgeos_c.so']
      [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

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 assigned 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 simplify_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()