New Methods For Analyzing Spatio-Temporal Simulation Results Of Moran’s Index

Moran’s index is a statistic that measures spatial autocorrelation; it quantifies the degree of dispersion (or clustering) of objects in space. In data analysis across two dimensions over a general area, a single Moran statistic proves insufficient for identifying the spread, behavior, features, or latent surfaces shared by neighboring areas. An alternative method divides the general area and uses the Moran statistic of each resulting sub-area to identify features of neighboring areas. In this paper, we add a time variable to a spatial Poisson point process. On the basis of the results of this simulation, we investigate variations in Moran statistics of neighboring areas and put forward approaches for the related analysis. Results of this work emphasize the importance of observing caution in handling spatiotemporal data when using methods involving implicit normality assumptions.