(JPSS) Journal of Probability and Statistical Science
The Moran's index is a statistic that measures spatial autocorrelation; it quantifies the degree of dispersion (or clustering) of objects in space. However, when investigating data over a general area, a single global Moran statistic may not give a sufficient summary of the spread, behavior, features or latent surfaces shared by neighboring areas; rather, by partitioning the area and taking the Moran statistic of each divided subareas, we can discover patterns of the local neighbors not otherwise apparent. In this paper, we present a simulation experiment where the local Moran values are computed and a time variable is added to a spatial Poisson point process. Changes in the Moran statistics over the neighboring areas are investigated and ideas on how to perform the analysis are proposed.
Original Publication Citation
Bu, N., Lorio, J., Diawara, N., Das, K., & Waller, L. (2018). New approaches to model simulated spatio-temporal Moran's index. JPSS, 16(1), 11-24.
Bu, Nhan; Lorio, Jennifer; Diawara, Norou; Das, Kumar; and Waller, Lance, "New Approaches to Model Simulated Spatio-Temporal Moran's Index" (2018). Mathematics & Statistics Faculty Publications. 206.