I am currently having some trouble finding a good interpolation technique for unevenly distributed spatial data points. Let's give a quick example :
I have a 1000 points dataset. As several surveys were made in the past, several spatial spacing were used. Some of them use a 200 meter grid (red bounding box), whereas other used a 1 km grid ( blue bounding box, see image below for a quick drawing). I merged all the datasets and now i would like to interpolate the data in an even grid. The data here just have spatial components (x and y in projected system) associated with the variable of interest, there is not time component to this data.
I usually use ordinary kriging or IDW, but they do not feel quite appropriate in this case, as they will be influenced by the proximity change of neighbouring data points. My search on the internet did not lead me to a solution.
Edit : Those methods seem inappropriate for me at the moment as a search algorithm based on a diameter will use more samples in areas of high density compared to low density regions, that may bias interpolation, in my opinion.
Did you already encounter such a problem with spatial data ? How did you manage to handle it ?