From the course: Advanced Spatial Data Visualization in Python
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Using Datashader for visualizing large geospatial datasets - Python Tutorial
From the course: Advanced Spatial Data Visualization in Python
Using Datashader for visualizing large geospatial datasets
- [Instructor] In this video, we will explore new ways to visualize large scale global roster population data sets coming from the EU's global human settlement layer. First, we will bring in two new key libraries for working with roster data, which are going to extend the capabilities of our values and well-known roster IO library. The first one is called xarray, which provides us a data model to work with large scale multi-dimensional arrays. It's a general purpose library, so it's not inherently geospatial. This is where rioxarray comes into the picture, which breaches roster IO and xarray by wrapping roster IO into the xarray ecosystem. So it allows us to open roster data into xarray data arrays by keeping all the geospatial metadata of the input file. This way we can take advantage of xarray's powerful array manipulation tools by working with geospatial roster data. Next, we open the previously downloaded GHSFL roster…
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