Geographic Data

import xarray as xr
import hvplot.pandas  # noqa
import hvplot.xarray  # noqa
import cartopy.crs as ccrs

from bokeh.sampledata.airport_routes import airports

Installation

The plot API also has support for geographic data built on top of Cartopy and GeoViews. Both can be installed using conda with:

conda install -c pyviz geoviews

or if the cartopy dependency has been satisfied in some other way, GeoViews may also be installed using pip:

pip install geoviews

Usage

Only certain hvPlot types support geographic coordinates, currently including: ‘points’, ‘polygons’, ‘paths’, ‘image’, ‘quadmesh’, ‘contour’, and ‘contourf’. As an initial example, consider a dataframe of all US airports (including military bases overseas):

airports.head(3)

	AirportID	Name	City	Country	IATA	ICAO	Latitude	Longitude	Altitude	Timezone	DST	TZ	Type	source
0	3411	Barter Island LRRS Airport	Barter Island	United States	BTI	PABA	70.134003	-143.582001	2	-9	A	America/Anchorage	airport	OurAirports
1	3413	Cape Lisburne LRRS Airport	Cape Lisburne	United States	LUR	PALU	68.875099	-166.110001	16	-9	A	America/Anchorage	airport	OurAirports
2	3414	Point Lay LRRS Airport	Point Lay	United States	PIZ	PPIZ	69.732903	-163.005005	22	-9	A	America/Anchorage	airport	OurAirports

Plotting points

If we want to overlay our data on geographic maps or reproject it into a geographic plot, we can set geo=True, which declares that the data will be plotted in a geographic coordinate system. The default coordinate system is the PlateCarree projection, i.e., raw longitudes and latitudes. If the data is in another coordinate system, you will need to declare an explicit crs as an argument, in which case geo=True is assumed. Once hvPlot knows that your data is in geo coordinates, you can use the tiles option to overlay a the plot on top of map tiles.

airports.hvplot.points('Longitude', 'Latitude', geo=True, color='red', alpha=0.2,
                       xlim=(-180, -30), ylim=(0, 72), tiles='ESRI')

Declaring a CRS

To declare a geographic plot we have to supply a cartopy.crs.CRS (or coordinate reference system). Coordinate reference systems are described in the GeoViews documentation and the full list of available CRSs is in the cartopy documentation.

Geopandas

Since a GeoPandas DataFrame is just a Pandas DataFrames with additional geographic information, it inherits the .hvplot method. We can thus easily load shapefiles and plot them on a map:

import geopandas as gpd

cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))

cities.hvplot(global_extent=True, frame_height=450, tiles=True)

The GeoPandas support allows plotting GeoDataFrames containing 'Point', 'Polygon', 'LineString' and 'LineRing' geometries, but not ones containing a mixture of different geometry types. Calling .hvplot will automatically figure out the geometry type to plot, but it also possible to call .hvplot.points, .hvplot.polygons, and .hvplot.paths explicitly.

To draw multiple GeoDataFrames onto the same plot, use the * operator:

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

world.hvplot(geo=True) * cities.hvplot(geo=True, color='orange')

It is possible to declare a specific column to use as color with the c keyword:

world.hvplot(geo=True) + world.hvplot(c='continent', geo=True)

Spatialpandas

Spatialpandas is another powerful library for working with geometries and is optimized for rendering with datashader, making it possible to plot millions of individual geometries very quickly:

import spatialpandas as spd

spd_world = spd.GeoDataFrame(world)

spd_world.hvplot(datashade=True, project=True, aggregator='count_cat', c='continent', color_key='Category10')