Hi there :),

I’ve been using RasterConvolver in FME Form 2023.0.1 to calculate the standard deviation with **a round** kernel, so some weights were equal to 0. The results were always incorrect, so to understand what’s happening I simplified the problem and used a raster with all values = 1 and one weight in kernel (3x3) = 0.

From what I understand, the formulas used in RasterConvolver for calculating the weighted mean and weighted SD are a bit off. Here is what I deducted:

**The weighted mean** is divided by a number of values (kernel cells) instead of by the sum of them.

**The weighted SD: **in the sum equation the weight is multiplied by a cell value instead of by a square of the difference between a cell value and a weighted mean AND the sum equation is divided by a number of values instead of a sum of weights.

This may seem pretty convoluted (pun intended :)), so I have attached a pdf with a visual explanation. I additionally attach a fmwt file with two examples of RasterConvolver misscalculations. One with raster with only 1s and second with my data.

**Questions:**

- Firstly to FME folks: is there a possibility of changing/patching those equations in the RasterConvolver Transformer?
- And to everyone: Is there any way you recommend to convolve raster without RasterConvolver? Ideas that came to me are:
- changing raster to vector, saving kernel size neighbourhood values in lists and later doing calculations using list values in attributes
- dividing the initial raster into a number of overlapping kernel size rasters for each cell, doing calculations on each of them separately and merging them afterwards

Both ideas seem a bit clumsy as well as time and RAM absorbing. I’m more of a beginner in FME, so I’ll welcome any advice you can give me in this matter :)