I have four datasets - point layers with different measurement variables, covering the same extent.
I think a raster based approach makes sense (although will also be investigating a vector binning approach later on) so I have calculated interpolations upon these different datasets.
The results are 4 continuous surfaces (floating point).
They share the same area, but some have holes in the surfaces (which is expected).
- Quality of Experience (~0-20)
- Radio Metrics (~0-150)
- User density (~0-60)
High Strain on the network = High Quality of Experience + High Radio Metrics
Low Strain on the network = Low Quality of Experience + Low Radio Metrics + High User Density
I was initially thinking this would be a good candidate for a weighted overlay, but then realised that I would have to reclassify the input datasets into discrete bands. I want to avoid this and maintain the continuous values in the datasets, as I am hoping to output a strain layer that is also continuous (Strain will be a measure anywhere between 0-100).
I am now thinking I could potentially achieve what i want using standard raster calculator expressions within a model, but feel like I am missing something and there might be another set of overlay tools I should be looking at?
I was wondering if I could call upon the brains of this Analysis group to perhaps brainstorm some approaches for the above scenario.
This is just an initial investigation project. Longer term I would actually like to investigate how the new Raster Analytics Server might be able to play a part in this workflow as the underlying datasets are changing frequently.