From the course: Advanced Geospatial Data Analytics in Python
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Map elevation data points into hexagons - Python Tutorial
From the course: Advanced Geospatial Data Analytics in Python
Map elevation data points into hexagons
- [Instructor] Finally, to demonstrate how we can use H3 in practice, we are going to aggregate point data with continuous feature values. Luckily, in the previous session, we created a large point vector file, containing elevation data across Germany. Let's read it now, and then filter it down to the Berlin area. So first, be reading the file we created earlier and have a quick look on the number of records it has. Then let's filter it down by overlaying the previously defined GDF example, which is a geodata frame containing the admin boundaries of the Berlin nuts one region. After the overlay, let's also check the number of point records once again. So now we only have 1,400 points, which is fairly manageable. Then we'll also need a hexagon grid at, for instance, the resolution of seven. Then we will create the hexagon grid, using the previously defined split poly to hexagons function. And for the sake of curiosity, let's print the number of hexagons we have, which is still 368…
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