

In [1]:

    
import os
import folium

print(folium.__version__)









    



0.3.0.dev

GeoJSON and choropleth

A few examples of how to do that with folium.

Using `GeoJson`

Let us load a GeoJSON file reprenseting the US states.



In [2]:

    
import json

us_states = os.path.join('data', 'us-states.json')

geo_json_data = json.load(open(us_states))

It is a classical GeoJSON FeatureCollection (see https://en.wikipedia.org/wiki/GeoJSON) of the form :

{
    "type": "FeatureCollection",
    "features": [
        {
            "properties": {"name": "Alabama"},
            "id": "AL",
            "type": "Feature",
            "geometry": {
                "type": "Polygon",
                "coordinates": [[[-87.359296, 35.00118], ...]]
                }
            },
        {
            "properties": {"name": "Alaska"},
            "id": "AK",
            "type": "Feature",
            "geometry": {
                "type": "MultiPolygon",
                "coordinates": [[[[-131.602021, 55.117982], ... ]]]
                }
            },
        ...
        ]
    }

A first way of drawing it on a map, is simply to use folium.GeoJson :



In [3]:

    
m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson(geo_json_data).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_0.html'))

m









    Out[3]:

Note that you can avoid loading the file on yourself ; in simply providing a file object.



In [4]:

    
m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson(open(us_states)).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_1.html'))

m









    Out[4]:

If you provide only the filename, Folium will consider you don't want to embed the data in the map. You'll need to copy the file on the server if you want it to be rendered.



In [5]:

    
# FIXME: This example is broken!!!
import shutil

shutil.copyfile(us_states, os.path.join('results', 'states.json'))

m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson('us-states.json').add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_2.html'))

m









    Out[5]:

Now this is cool and simple, but we may be willing to choose the style of the data.

You can provide a function of the form lambda feature: {} that sets the style of each feature.

For possible options, see:

For Point and MultiPoint, see http://leafletjs.com/reference.html#marker
For other features, see http://leafletjs.com/reference.html#path-options and http://leafletjs.com/reference.html#polyline-options



In [6]:

    
m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson(
    geo_json_data,
    style_function=lambda feature: {
        'fillColor': '#ffff00',
        'color': 'black',
        'weight': 2,
        'dashArray': '5, 5'
    }
).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_3.html'))

m









    Out[6]:

What's cool in providing a function, is that you can specify a style depending on the feature. For example, if you want to visualize in green all states whose name contains the letter 'E', just do:



In [7]:

    
m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson(
    geo_json_data,
    style_function=lambda feature: {
        'fillColor': 'green' if 'e' in feature['properties']['name'].lower() else '#ffff00',
        'color': 'black',
        'weight': 2,
        'dashArray': '5, 5'
    }
).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_4.html'))

m









    Out[7]:

Wow, this looks almost like a choropleth. To do one, we just need to compute a color for each state.

Let's imagine we want to draw a choropleth of unemployment in the US.

First, we may load the data:



In [8]:

    
import pandas as pd

US_Unemployment_Oct2012 = os.path.join('data', 'US_Unemployment_Oct2012.csv')
unemployment = pd.read_csv(US_Unemployment_Oct2012)

unemployment.head(5)









    Out[8]:






  
    
      
      State
      Unemployment
    
  
  
    
      0
      AL
      7.1
    
    
      1
      AK
      6.8
    
    
      2
      AZ
      8.1
    
    
      3
      AR
      7.2
    
    
      4
      CA
      10.1

Now we need to create a function that maps one value to a RGB color (of the form #RRGGBB). For this, we'll use colormap tools from folium.colormap.



In [9]:

    
from branca.colormap import linear

colormap = linear.YlGn.scale(
    unemployment.Unemployment.min(),
    unemployment.Unemployment.max())

print(colormap(5.0))

colormap









    



#c2e798






    Out[9]:

We need also to convert the table into a dictionnary, in order to map a feature to it's unemployment value.



In [10]:

    
unemployment_dict = unemployment.set_index('State')['Unemployment']

unemployment_dict['AL']









    Out[10]:





7.0999999999999996

Now we can do the choropleth.



In [11]:

    
m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson(
    geo_json_data,
    style_function=lambda feature: {
        'fillColor': colormap(unemployment_dict[feature['id']]),
        'color': 'black',
        'weight': 1,
        'dashArray': '5, 5',
        'fillOpacity': 0.9,
    }
).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_5.html'))

m









    Out[11]:

Of course, if you can create and/or use a dictionnary providing directly the good color. Thus, the finishing seems faster:



In [12]:

    
color_dict = {key: colormap(unemployment_dict[key]) for key in unemployment_dict.keys()}



In [13]:

    
m = folium.Map([43, -100], zoom_start=4)

folium.GeoJson(
    geo_json_data,
    style_function=lambda feature: {
        'fillColor': color_dict[feature['id']],
        'color': 'black',
        'weight': 1,
        'dashArray': '5, 5',
        'fillOpacity': 0.9,
    }
).add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_6.html'))

m









    Out[13]:

Note that adding a color legend may be a good idea.



In [14]:

    
colormap.caption = 'Unemployment color scale'
colormap.add_to(m)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_7.html'))

m









    Out[14]:

GeoPandas

Note that you can also create a GeoJson object in using a GeoPandas DataFrame. There's another notebook example for this.

Using `choropleth` method

Now if you want to get faster, you can use the Map.choropleth method. Have a look at it's docstring, it has several styling options.

You can use it in providing a file name (geo_path) :



In [15]:

    
m = folium.Map([43, -100], zoom_start=4)

m.choropleth(
    geo_path=us_states,
    fill_color='red',
    fill_opacity=0.3,
    line_weight=2,
)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_7.html'))

m









    Out[15]:

Or in providing a GeoJSON string (geo_str) :



In [16]:

    
m = folium.Map([43, -100], zoom_start=4)

m.choropleth(geo_str=open(us_states).read())

m.save(os.path.join('results', 'GeoJSON_and_choropleth_8.html'))

m









    Out[16]:

Then, in playing with keyword arguments, you can get a choropleth in (almost) one line :



In [17]:

    
m = folium.Map([43, -100], zoom_start=4)

m.choropleth(
    geo_str=open(us_states).read(),
    data=unemployment,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
)


m.save(os.path.join('results', 'GeoJSON_and_choropleth_9.html'))

m









    Out[17]:

A cool thing: you can force the color scale to a given number of bins, in providing a threshold_scale argument.



In [18]:

    
m = folium.Map([43, -100], zoom_start=4)

m.choropleth(
    geo_str=open(us_states).read(),
    data=unemployment,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
    threshold_scale=[3, 4, 9, 10]
)


m.save(os.path.join('results', 'GeoJSON_and_choropleth_10.html'))

m









    Out[18]:

You can also enable the highlight function, to enable highlight functionality when you hover over each area.



In [19]:

    
state_data = pd.read_csv(US_Unemployment_Oct2012)

m = folium.Map(location=[48, -102], zoom_start=3)
m.choropleth(
    geo_path=us_states,
    data=state_data,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name='Unemployment Rate (%)',
    highlight=True
)

m.save(os.path.join('results', 'GeoJSON_and_choropleth_11.html'))

m









    Out[19]:

GeoJSON and choropleth

Using GeoJson

GeoPandas

Using choropleth method

Using `GeoJson`

Using `choropleth` method