Riccardo, thanks for replying.
Let me provide more information , as I reckon I did a bad job the first time ;)
Thanks for the welcoming!
The tricky bit of analysis will handle angles, distances and deltas of those variables, since everything needs to be georeferenced, every observation takes a coordinate pair (X,Y or N,E) but also Z (X,Y,Z).
The data will take the form of a panel model, with a benchmark value for the base variables (the average of a few dozen observations ) and the recurring measurements shall be compared against the benchmark, and once enough data is collected, we'll try to understand various relationships amongst variables and their observed values.
My emphasis on the (XYZ) is that though rain, temp, exogenous impacts, etc play a role in the *seasonal* observations therefore recurring, the coordinates are observed down to a high precision (mm) and changes in them may flag changes in the observed values which are not related to any recurring cycle but a deterioration in the condition of a specific location, and many tests and hypothesis will be checked for each observation. There geo variable should be so interrelated that I expected
y= a + b*sin(x) is a generalization for an accumulation of a geographical position, such as Y = Yi + d*sine(alpha) for example. When I run an OLS the results seem to explain very little of what I expected to be a straightforward and linear expression, specially because it's a panel and I am comparing the same data which is expected to change very little between observations/measurements.
So first of all I need to script for checking 1mm change in coordinates (above or below each variable X, Y, Z) then check for season impacts etc.
I am attaching an example file with test measurements. Important detail (in order to make sense of data) is that I am creating a BENCHMARK value for XYZ (average value) and creating variables DeltaX/Y/Z which are the ones I will compare against 0.001m (1mm) change by doing ex. Delta_X(i) = X(i) - mean(X) etc. I also should say that the dataset used a less precsa equipment so it is very hard to check for 1mm changes, as per OLS , the results will tell.
My idea is to use tests to check if the changes breach a critical value and if they relate to seasonal factors (no big deal) or they are indeed red flags.
Any ideas how you'd run this?
Cheers from Brazil!