My observations are points along a transect, irregularly spaced.
I aim at finding the distance values that maximize the clustering of my observation attribute, in order to use it in the following LISA analysis (Local Moran I).
I iteratively run Global Moran I function with PySAL 2.0, recreating a different distance-based weight matrix (binary, assigning 1 to neighbors and 0 to not neighbors) with a search radius 0.5m longer at every iteration.
At every iteration, I save z_sim,p_sim, I statistics, together with the distance at which these stats have been computed.
From these information, what strategy is best to find distances that potentially show underlying spatial processes that (pseudo)-significantly cluster my point data?
- Esri style: ArcMap Incremental Global Moran I tool identify peaks of z-values where p is significant as interesting distances
- Literature: I found many papers that simply choose the distance with the higher absolute significant value of I
Because with varying search radius the number of observations considered in the neighborhood change, thus, the weight matrix also change, the I value is not comparable.