GeoViews

GeoViews makes it easy to explore and visualise geographic, meteorological and oceanographic data sets. GeoViews is based on HoloViews and complements its visualisation of multidimensional data with geographic plot types based on Cartopy.

Installation

If Cartopy is already installed, GeoViews can be installed with pipenv:

$ pipenv install geoviews

Examples

GeoViews works well with Iris and xarray libraries for multidimensional arrays as stored in netCDF files. However, GeoViews also accepts data as NumPy arrays and pandas data frames. In either case, the data can be stored in its original, native format and packaged into a HoloViews or GeoViews object that enables interactive visualisations.

xarray example

For the following example we also need xarray. It can be installed with Spack with:

$ spack install py-xarray

import geoviews as gv
import geoviews.feature as gf
import xarray as xr

from cartopy import crs


gv.extension("bokeh", "matplotlib")

(gf.ocean + gf.land + gf.ocean * gf.land * gf.coastline * gf.borders).opts(
    "Feature", projection=crs.Geostationary(), global_extent=True, height=325
).cols(3)

We use geoviews-sample-data.zip as sample data and unpack it as the data directory.

!curl http://assets.holoviews.org/geoviews-sample-data.zip --output geoviews-sample-data.zip
!unzip geoviews-sample-data.zip

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 6246k  100 6246k    0     0  5679k      0  0:00:01  0:00:01 --:--:-- 5694k
Archive:  geoviews-sample-data.zip
  inflating: ensemble.nc
  inflating: ensembles.nc
  inflating: pre-industrial.nc

dataset = gv.Dataset(xr.open_dataset("./ensemble.nc"))
ensemble = dataset.to(
    gv.Image, ["longitude", "latitude"], "surface_temperature"
)

gv.output(
    ensemble.opts(
        cmap="viridis", colorbar=True, fig_size=200, backend="matplotlib"
    )
    * gf.coastline(),
    backend="matplotlib",
)

GeoPandas example

GeoViews also natively supports GeoPandas data structures, so we can easily display shapefiles and Choropleth:

import geopandas as gpd


gv.Polygons(
    gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")),
    vdims=["pop_est", ("name", "Country")],
).opts(tools=["hover"], width=600, projection=crs.Robinson())

/tmp/ipykernel_88986/2104909695.py:5: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.
  gpd.read_file(gpd.datasets.get_path("naturalearth_lowres")),