Interactive maps with Bokeh

Interactive maps are used to visualize the data based on the geo-location category. any large dataset which contains a lot of geo-location data like cities, states, countries, etc can be plotted easily.  bokeh is an open-source package, which uses the Bokeh visualization tool. It gives a flexible declarative interface for dynamic web-based visualizations as well as an interactive dashboard. 

Prerequisite: Data Visualization using Bokeh

Example 1: In this example, we will create an exemplary dataset and then plot a Map using that Coordinates.

X-Coordinate	Y-Coordinate	Data
-100833	5211172	GeeksForGeeks
-100833	3086289	GeeksForGeeks
-9754910	5142738	GeeksForGeeks
1999900	12738	GeeksForGeeks
-7100000	-2425502	GeeksForGeeks

Approach:

1. Import Library.
2. Initialize the tile provider.
3. Provide the data needed to be displayed to the tuple.
4. Pass height, width, and ranged x,y coordinates to figure(width, height) function.
5. Add title.
6. Provide the required coordinates to the circle function.
7. Mention circle size and color.
8. After circling display using show() function.

Python3

# Python program to make an
# interactive map using bokeh library
from bokeh.tile_providers import get_provider, Vendors

# Including Positron Map
tile_provider = get_provider(Vendors.CARTODBPOSITRON)

# Provide the data tuple needed
# to be display while hovering.
tooltips = ("GeeksForGeeks")

# Creating Map object
m = figure(title='World Map', plot_width=650,
           plot_height=400, x_range=(-12000000, 9000000),
           y_range=(-1000000, 7000000), x_axis_type='mercator',
           y_axis_type='mercator', tooltips=tooltips)

# Adding title
m.add_tile(tile_provider)

# Circling the coordinates.
m.circle(x=-100833, y=5211172, size=15, color='red')
m.circle(x=-100833, y=3086289, size=15, color='blue')
m.circle(x=-9754910, y=5142738, size=15, color='orange')
m.circle(x=1999900, y=12738, size=15, color='green')
m.circle(x=-7100000, y=-2425502, size=15, color='black')

# Displaying the Map using show function
show(m)

Output:

image contributed by SR.Dhanush.

Example 2: Scatter Map for India Starbucks Stores Dataset.

Pyproj: Pyproj is used to Perform cartographic transformations. Converts from longitude, latitude to native map projection x,y coordinates.

Approach:

1. Import Required Libraries and functions.
2. Initialise the tile provider.
3. Preprocess the data needed to be displayed.
4. Initialize the outproj and inproj.
5. Convert the Respective Longitudes and Latitudes to MercatorX and MercatorY using Pyproj i.e x, y axis.
6. Pass height, width and ranged x,y coordinates to figure(width, height) function.
7. Add the title needed to be Displayed.
8. Provide required coordinates to the circle function.
9. Mention circle size and color.
10. After circling display using show() function.

Python3

# Python program to make an interactive map using bokeh library.
# Importing Dataset.
from bokeh.io import output_notebook, show
from bokeh.plotting import figure
from bokeh.tile_providers import get_provider, Vendors
from pyproj import Proj, transform
import pandas as pd
df = pd.read_csv("./starbucks.csv")

# Initializing pyproj for Converting from longitude,
# latitude to native map projection x, y coordinates.
inProj = Proj(init='epsg:3857')
outProj = Proj(init='epsg:4326')

# Subsetting for indian dataset.
df = df[df.Country == "IN"]
lons, lats = [], []

# Converting Longitude and Latitude to x,y coordinates.
for lon, lat in list(zip(df["Longitude"], df["Latitude"])):
    x, y = transform(outProj, inProj, lon, lat)
    lons.append(x)
    lats.append(y)

# Storing the coordinates.
df["MercatorX"] = lons
df["MercatorY"] = lats

# Renaming Columns for Tooltips
df = df.rename(columns={"Store Name": "Name", "State/Province": "State"})

# Importing all important functions
# for map creation and interaction.

# Including Positron Map.
tile_provider = get_provider(Vendors.CARTODBPOSITRON)

# Provide the data tuple needed to be display while hovering.
tooltips = [("State", "@State"), ('Name', '@Name')]

# Creating Map object.
m = figure(title='Starbucks Stores in India', plot_width=650,
           plot_height=400, x_range=(-12000000, 9000000),
           y_range=(-1000000, 7000000),
           x_axis_type='mercator', y_axis_type='mercator',
           tooltips=tooltips)

# Adding title.
m.add_tile(tile_provider)
m.circle(x='MercatorX', y='MercatorY', size=5, source=df)

# Displaying the Map using show function.
show(m)

Output:

Example 3: Connection Map for America Airport Dataset.

Python3

# Importing Airports2 Dataset which
# represent american flight travel data.
from bokeh.io import show
from bokeh.tile_providers import STAMEN_TERRAIN, STAMEN_TONER
from bokeh.plotting import figure
from pyproj import Proj, transform
import pandas as pd
df = pd.read_csv("./Airports2.csv")

# Hence Dataset is huge so selecting fewer rows.
df = df[1:1000]

# Converting Longitude and Latitudes to x,y coordinates
inProj = Proj(init='epsg:3857')
outProj = Proj(init='epsg:4326')
cols = ['Dest_airport_long', 'Dest_airport_lat',
        'Org_airport_long', 'Org_airport_lat']
lines_x, lines_y = [], []
lons, lats = [], []
for lon_dest, lat_dest, lon_orig, lat_orig in df[cols].values:
    lon_orig, lat_orig = transform(outProj, inProj, lon_orig, lat_orig)
    lon_dest, lat_dest = transform(outProj, inProj, lon_dest, lat_dest)

# Append converted Coordinates.
    lons.append(lon_dest)
    lats.append(lat_dest)

# Creating Source and Destination points for connections.
    lines_x.append([lon_orig, lon_dest])
    lines_y.append([lat_orig, lat_dest])

# Two way connection points
df["MercatorX"] = lons
df["MercatorY"] = lats

# Loading Important Functions and Libraries

# Hence Connections needed to be represented so,
# selecting STAMEN_TONER
stamen_toner = get_provider(STAMEN_TONER)

# Selecting world coordinates
lon1, lat1 = transform(outProj, inProj, -150, -75)
lon2, lat2 = transform(outProj, inProj, 140, 75)

# Pass source-destination connections,
# tooltips to be displayed and title
m = figure(plot_width=800, plot_height=700,
           x_range=(lon1, lon2), y_range=(lat1, lat2),
           x_axis_type="mercator", y_axis_type="mercator",
           tooltips=[("Origin_city", "@Origin_city"),
                     ("Destination_city", "@Destination_city")],
           title="Flight Travels in America")

# Add tile for stamen_toner
m.add_tile(stamen_toner)

# Drawing Multiple Lines.
m.multi_line(lines_x, lines_y, color="red")

# Circling the points and lines
m.circle(x="MercatorX", y="MercatorY", size=2,
         alpha=0.8, color="red", source=df)

# Displaying the map.
show(m)

Output:

Image contributed by SR.Dhanush.