EOmaps: An open-source python package for geographic data visualization and analysis.

EOmaps is a free and open-source python package specifically tailored for geographic data visualization and analysis.

The main goals of the package are twofold:

• Speed up and simplify the daily struggle of geographic data visualization
• Directly use the figures as fully customisable interactive data-analysis widgets

EOmaps is built on top of matplotlib and cartopy and integrates well with the scientific python infrastructure (numpy, pandas, xarray, geopandas, datashader, etc.). It provides a flexible and well-documented API to create publication-ready figures and it can be used to visualize (potentially large) structured (e.g. raster) or unstructured (e.g. unordered lists) datasets provided in arbitrary projections. 

In addition, EOmaps comes with many useful features to help with scientific geo-data analysis:

• Maps can have multiple layers to interactively compare and (transparently) overlay datasets, web-maps etc.
• Once a dataset is plotted, you can assign arbitrary callback functions to interactively run your analysis-workflow on selected datapoints (e.g. load data from a database, plot underlying timeseries, histograms etc.)

Figures created with EOmaps can be exported as images (png, jpeg, ...), vector-graphics (svg) or embedded in Jupyter Notebooks, web-pages (html) or in GUI frameworks such as Qt or tkinter.

In this presentation we will highlight the capabilities of EOmaps and show how it can be used in a variety of different situations to aid your scientific data analysis workflow.

EOmaps source-code: https://github.com/raphaelquast/EOmaps  
EOmaps documentation: https://eomaps.readthedocs.io/