Visualization Geospatial


Libraries for Geospatial Data Visualization in Python


PyViz/HoloViz(Geoviews, Datashader, HvPlot)

Holoviz maintained libraries have all data visualisations you might need, including dashboards and interactive visualisation. Geoviews, in particular, with its dedicated Geospatial data visualisation library, provides an easy to use and convenient geospatial data.

  • Geoviews

    Explore and visualize geographical, meteorological, and oceanographic datasets, such as those used in weather, climate, and remote sensing research.

  • HvPlot

    Allows users to work with different data types and can extend the usage of other Python libraries including Pandas, Geopadnas, Dask and Rapids.

  • Datashader

    It breaks the process into multiple steps and runs parallel to create a visualisation for large datasets quickly.

  • Panel

    A high-level app and dashboarding solution for Python provide an easy to use interface on creating interactive web apps and dashboards using Jupyter notebooks.

import geoviews as gv
imporg 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()
)

Folium

  • It is built on top of Leaflet.js and can cover most of your mapping needs in Python with its great plugins
import foliumm 
m= folium.Map(location=[45.5236, -122.6750])

KeplerGL

  • kepler.gl is a web-based visualisation tool for large Geospatial datasets built on top of deck.gl. Uber made it an open-source in 2018, and its functionality is impressive. You can easily drag and drop your dataset and tweak it immediately on the web to visualise large scale geospatial datasets with ease.
  • It combines a world-class visualisation tool, an easy to use User interface (UI), and flexibility of python and Jupyter notebooks.

IpyLeaflet

  • It is built on top of Jupyter Widgets and Leaflet visualisation library.
  • The interactive functionality in IpyLeaflet is unparalleled as Widgets enable bidirectional interactions. Therefore, your maps are not only interactive but also can capture user inputs to trigger new computations.

Geopandas

  • Only for static maps
  • However, recent advances and additions of Contextily for base maps and IPYMPL for interactive matplotlib plots makes it straightforward to create interactive maps with Geopandas.