Union of 2+ point datasets with overlapping and non-overlapping lat/lon points

Question

I have three point layers with n1, n2, and n3 number of points. Each dataset shares with the others a common set of points that refer to the same spatial entities. The lat/lon values of these shared points are similar, but not identical though among datasets. For example, layers 1 and 2, share a subset of points m12, and the same applies to the other cases.

I want to create a layer that identifies the unique points, despite slight differences in lat/lon (in the rows of the table), and the specific layer in which that point is found (columns).

Answer 1

Try this:

1. Merge the point layers with geoprocessing tool merge vector layers
2. Buffer the merged layer with a distance that approximates to the furthest separation you expect in a cluster. This will create buffer around clusters of points that refer to the same spatial entity.
3. Dissolve the buffered layer - with Keep disjoint features separate ON.
4. Run the tool Centroids on the dissolved layer.
5. Create/update a field in the Centroids layer with this expression:

   array_first(overlay_nearest(layer:='merged',expression:="layer", max_distance:=100))
   

But substitute the max_distance with your buffer distance. The value returned by the expression is in field "layer" and is coming from layer merged - that's how you get to identify what layer the point came from - however, the location of the point will not be the same as the original point location as it is a centroid of the buffer process.