xarray-leaflet combines xarray and Jupyter’s ipyleaflet to render large geographical datasets interactively on maps. In this poster, we show how users can visualize a dataset using a data pipeline to generate static and dynamic maps.
In this walk-through video, we first show how xarray is used to visualize a dataset with its built-in plotting capabilities, and what the limitations are with this approach. We then show how we can use xarray-leaflet to easily build a data pipeline in order to average data at a global scale, or to refine it and reveal details at a lower scale.
In xarray-leaflet, ipyleaflet is used to display map tiles in a Jupyter notebook, generated on the fly with xarray, and served with the Jupyter server. It allows for some interactivity, like panning and zooming, and gives access to a large number of publicly available base maps, on top of which rasters can be overlaid.
Because xarray and Leaflet share the ability to work with pieces of data (through chunks and tiles), xarray-leaflet is very efficient for visualizing big data.