geopandas/examples/plotting_with_folium.ipynb

Plotting with folium

What is Folium?

It builds on the data wrangling and a Python wrapper for leaflet.js. It makes it easy to visualize data in Python with minimal instructions.

Folium expands on the data wrangling properties utilized in Python language and the mapping characteristics of the Leaflet.js library. Folium enables us to make an intuitive map and are is visualized in a Leaflet map after manipulating data in Python. Folium results are intuitive which makes this library helpful for dashboard building and easier to work with.

Let's see the implementation of both GeoPandas and Folium:

In [1]:

# Importing Libraries
import pandas as pd
import geopandas
import folium
import matplotlib.pyplot as plt

from shapely.geometry import Point

In [2]:

df1 = pd.read_csv('volcano_data_2010.csv')
df = df1.loc[:, ("Year", "Name", "Country", "Latitude", "Longitude", "Type")]
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 63 entries, 0 to 62
Data columns (total 6 columns):
Year         63 non-null int64
Name         63 non-null object
Country      63 non-null object
Latitude     63 non-null float64
Longitude    63 non-null float64
Type         63 non-null object
dtypes: float64(2), int64(1), object(3)
memory usage: 3.0+ KB

In [3]:

geometry = geopandas.points_from_xy(df.Longitude, df.Latitude)
geo_df = geopandas.GeoDataFrame(df[['Year','Name','Country', 'Latitude', 'Longitude', 'Type']], geometry=geometry)

geo_df.head()

Out [3]:

	Year	Name	Country	Latitude	Longitude	Type	geometry
0	2010	Tungurahua	Ecuador	-1.467	-78.442	Stratovolcano	POINT (-78.44199999999999 -1.467)
1	2010	Eyjafjallajokull	Iceland	63.630	-19.620	Stratovolcano	POINT (-19.62 63.63)
2	2010	Pacaya	Guatemala	14.381	-90.601	Complex volcano	POINT (-90.601 14.381)
3	2010	Sarigan	United States	16.708	145.780	Stratovolcano	POINT (145.78 16.708)
4	2010	Karangetang [Api Siau]	Indonesia	2.780	125.480	Stratovolcano	POINT (125.48 2.78)

In [4]:

world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
df.Type.unique()

Out [4]:

array(['Stratovolcano', 'Complex volcano', 'Shield volcano',
       'Subglacial volcano', 'Lava dome', 'Caldera'], dtype=object)

In [5]:

fig, ax = plt.subplots(figsize=(24,18))
world.plot(ax=ax, alpha=0.4, color='grey')
geo_df.plot(column='Type', ax=ax, legend=True)
plt.title('Volcanoes')

Out [5]:

Text(0.5, 1.0, 'Volcanoes')

We will be using different icons to differentiate the types of Volcanoes using Folium. But before we start, we can see a few different tiles to choose from folium.

In [6]:

# Stamen Terrain
map = folium.Map(location = [13.406,80.110], tiles = "Stamen Terrain", zoom_start = 9)
map

Out [6]:

In [7]:

# OpenStreetMap
map = folium.Map(location = [13.406,80.110], tiles='OpenStreetMap' , zoom_start = 9)
map

Out [7]:

In [8]:

# Stamen Toner
map = folium.Map(location = [13.406,80.110], tiles='Stamen Toner', zoom_start = 9)
map

Out [8]:

We can use other tiles for the visualization, these are just a few examples.

Markers

Now, let's look at different volcanoes on the map using different Markers to represent the volcanoes.

In [9]:

#use terrain map layer to actually see volcano terrain
map = folium.Map(location = [4,10], tiles = "Stamen Terrain", zoom_start = 3)

In [10]:

# insert multiple markers, iterate through list
# add a different color marker associated with type of volcano

geo_df_list = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]

i = 0
for coordinates in geo_df_list:
    #assign a color marker for the type of volcano, Strato being the most common
    if geo_df.Type[i] == "Stratovolcano":
        type_color = "green"
    elif geo_df.Type[i] == "Complex volcano":
        type_color = "blue"
    elif geo_df.Type[i] == "Shield volcano":
        type_color = "orange"
    elif geo_df.Type[i] == "Lava dome":
        type_color = "pink"
    else:
        type_color = "purple"


    #now place the markers with the popup labels and data
    map.add_child(folium.Marker(location = coordinates,
                            popup =
                            "Year: " + str(geo_df.Year[i]) + '<br>' +
                            "Name: " + str(geo_df.Name[i]) + '<br>' +
                            "Country: " + str(geo_df.Country[i]) + '<br>'
                            "Type: " + str(geo_df.Type[i]) + '<br>'
                            "Coordinates: " + str(geo_df_list[i]),
                            icon = folium.Icon(color = "%s" % type_color)))
    i = i + 1

In [11]:

map

Out [11]:

Heatmaps

Folium is well known for it's heatmap which create a heatmap layer. To plot a heat map in folium, one needs a list of Latitude, Longitude.

In [12]:

# In this example, with the hep of heat maps, we are able to perceive the density of volcanoes
# which is more in some part of the world compared to others.

from folium import plugins

map = folium.Map(location = [15,30], tiles='Cartodb dark_matter', zoom_start = 2)

heat_data = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]

heat_data
plugins.HeatMap(heat_data).add_to(map)

map

Out [12]:

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