- Introduction
- lexcubo
- Data
- Data cube with random numbers
- Data cube with climate data
- Raster layers to Xarray
- Xarray 3D visualization by Lexcube
- What else can we do with Lexcube?
- Conclusion
- References
Introduction
Visualizing data in three dimensions (latitude, longitude and time) is fascinating, right? As a geospatial data scientist, I always wanted to know what is the easiest way to plot a cubic data set created by merging hundreds of raster layers. While reading my feeds on LinkedIn, I came across a great Python library called Lexcube, which recently became available for Jupyter Notebook. For additional information on Lexcube, see this article and/or consult Lexcube on GitHub.
First of all, I would like to thank Miguel Mahecha for sharing that post on LinkedIn and also Maximilian Söchting and his team for developing a valuable tool for the geospatial data community. Second, here is a practical exercise that will help you use this package to visualize your cubic data in a 3D graph. All steps were coded in Python on Google Colab and at the end of this story, you will learn how to convert your raster layers to Xarray format and then use them in Lexcube to create a 3D graph of your data.
If, like me, you were looking for a package for 3D visualization of your data, this story is for you. I have no affiliation with Lexcube and just wanted to share my experience writing this blog post.
lexcubo
Leipzig Explorer of Earth Data Cubes, or Lexcube, is an interactive data visualization tool developed by Maximilian Söchting as a Ph.D. project under the supervision of Gerik Scheuermann and Miguel Mahecha at the University of Leipzig. The tool is designed to handle large cubes of Earth data. The project received funding from several institutions and agencies, including the European Space Agency (ESA). In May 2022, a web version of this tool was released…
