airports_map#
This example shows the same data on two separate tile plots. The left plot uses the built-in CartoDB tile source, and the right plot uses a customized tile source configured for OpenStreetMap.
Details
- Sampledata:
- Bokeh APIs:
- More info:
- Keywords:
tile, map, field, elevation, geo
import xyzservices.providers as xyz
from bokeh.layouts import column, gridplot
from bokeh.models import Div, Range1d
from bokeh.plotting import figure, show
from bokeh.sampledata.airports import data as airports
title = "US Airports: Field Elevation > 1500m"
def plot(tile_source):
# set to rough extents of points
x_range = Range1d(start=airports['x'].min() - 10000, end=airports['x'].max() + 10000, bounds=None)
y_range = Range1d(start=airports['y'].min() - 10000, end=airports['y'].max() + 10000, bounds=None)
# create plot and add tools
p = figure(tools='hover,wheel_zoom,pan,reset', x_range=x_range, y_range=y_range, title=title,
tooltips=[("Name", "@name"), ("Elevation", "@elevation (m)")],
width=400, height=400)
p.axis.visible = False
p.add_tile(tile_source)
# create point glyphs
p.scatter(x='x', y='y', size=10, fill_color="#F46B42", line_color="white", line_width=2, source=airports)
return p
carto = plot("CartoDB Positron")
mq = plot(xyz.OpenTopoMap)
# link panning
mq.x_range = carto.x_range
mq.y_range = carto.y_range
div = Div(text="""
<p>This example shows the same data on two separate tile plots.
The left plot uses the built-in CartoDB tile source, and the right plot uses
a customized tile source configured for OpenStreetMap.</p>
""", width=800)
layout = column(div, gridplot([[carto, mq]], toolbar_location="right"))
show(layout)