I'm referring to the manual on how to query spatial data, but in my scenario, there is a pixel-wise raster dataset of climate data over 8000+ days.
In simple words, the query could look something like this:
select temperature data for 5000 days for the defined amorphous region and aggregate the values using spark
My questions are:
- Should all the pixels be converted into vector-like "points" to be applicable for masking operations?
- Can third dimension (time) be somehow added to the spatial index, or should date be just a regular column?
Grateful for any input!
Some visual representation:
