[jhj]

Usually a Morton ordering is used for things like improving average memory locality of N-dimensional data (e.g., loop iteration order, data layout, ...). But it is average locality that is being improved, because of huge jumps between many neighbors.

In a small number of dimensions, without knowing what search algorithm is being used, this is just more work than comparing the original values. It doesn't mention what "k-NN algorithm" is being used, beyond brute force search.

Lossily compressing N-dimensional data (from 2 to 1000s of dimensions) into a representation that requires fewer bits can be done via quantization as well, either scalar quantization, vector quantization (aka k-means) or product quantization, if your data has known statistics.

It also matters if you are building a static data structure that is queried many times, versus one that needs continual updating.