Web Map / Feature Services Cookbook

nightly-build Binder DOI

This Project Pythia Cookbook covers retrieving and using web map / feature services to help provide the necessary spatial context to your data.


By leveraging web map / feature services, users can easily access pre-processed data layers, utilize ready-to-use tiles, and benefit from production-level data that is continuously updated. This streamlines the data acquisition process and enables users to focus on their analysis tasks rather than data processing.

  • Pre-processed Data: Web map services provide access to a wide range of pre-processed geospatial data layers. This eliminates the need for users to perform data processing tasks themselves, saving time and effort.

  • Ready-to-Use Tiles: Users can simply fetch the tiles from the web map services and use them as a reference or overlay in their analysis. This makes it convenient to integrate the data into their own applications without the need to handle complex data processing workflows.

  • Production-Level Data: Web map services are often deployed at production level, ensuring that the data is up-to-date and near real-time. This is particularly advantageous for applications that require the latest information, such as weather monitoring or real-time asset tracking.


Andrew Huang



This cookbook is broken up into two main sections - “Foundations” and “Example Workflows.”


The foundational content includes:

  • Web Map Services

  • Web Feature Services

Example Workflows

Example workflows include:

  • NASA Earthdata GIBS Explorer

Running on Binder

The simplest way to interact with a Jupyter Notebook is through Binder, which enables the execution of a Jupyter Book in the cloud. The details of how this works are not important for now. All you need to know is how to launch a Pythia Cookbooks chapter via Binder. Simply navigate your mouse to the top right corner of the book chapter you are viewing and click on the rocket ship icon, (see figure below), and be sure to select “launch Binder”. After a moment you should be presented with a notebook that you can interact with. I.e. you’ll be able to execute and even change the example programs. You’ll see that the code cells have no output at first, until you execute them by pressing Shift+Enter. Complete details on how to interact with a live Jupyter notebook are described in Getting Started with Jupyter.

Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

(Replace “cookbook-example” with the title of your cookbooks)

  1. Clone the repository:

     git clone
  2. Move into the web-map-feature-services-cookbook directory

    cd web-map-feature-services-cookbook
  3. Create and activate your conda environment from the environment.yml file

    conda env create -f environment.yml
    conda activate web-map-feature-services-cookbook-dev
  4. Move into the notebooks directory and start up Jupyterlab

    cd notebooks/
    jupyter lab