Document Type

Article

Publication Date

2-2018

Publication Title

(JPSS) Journal of Probability and Statistical Science

Volume

16

Issue

1

Pages

11-24 pp.

Abstract

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.

Comments

JPSS provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.

© 2018 Susan Rivers’ Cultural Institute, Hsinchu City, Taiwan, Republic of China.

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.

ORCID

0000-0002-8403-6793 (Diawara)

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