Imagine I have two sets of XY points, lets call them A and B, roughly coincident in space. Points A also have an attribute, lets call it N. I’d like to estimate (i.e. interpolate) the same attribute N for points B. I could simply “copy across” the value from the nearest corresponding points in A and B, but this is no good for the use case I have in mind. Instead I’d like to at least apply some summary statistic (e.g. mean), or better still something like IDW. So far all tools I can think of in Spatial Analyst take point attributes and interpolate them to a new raster (i.e. continuous surface, which I do not want), rather than interpolating to existing points. Does anyone know of any way to accomplish the latter please?
If the points are roughly coincident in space, on what basis is interpolation needed?
What is N a function of? The small differences in location?
Many thanks.
When I say coincident I mean in terms of approximately the same extent or coverage, not the same actual locations.
What about
IDW to raster followed by ExtractValuesToPoints
Many thanks.
Indeed I had thought of just such a work-around. However, the result will be sub-optimal, as the approximation (interpolated value) is assigned to a regular grid/array of pixels rather than discrete, randomly distributed points. Indeed creating a continuous surface (i.e. raster) is overkill in the first place, as it is doing waaaaay more computations for a much larger series of pixels compared to a [relatively] small number of discrete points.
In that case you could use IDW in Geostatistical Analyst to create a ga layer and then use GALayerToPoints which will use the interpolation parameters in the ga layer to predict a value at exactly the given locations. The ga layer, does no have a cell size, and is held in memory. You could also then do cross validation on your method.