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Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

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Research Article, J Comput Eng Inf Technol Vol: 7 Issue: 2

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

Bassam S1*, Osman N1, Haitham S2

1Electrical and computer engineering, Altinbas University, Turkey

2Business Information Technology and Communication, Iraq

*Corresponding Author : Bassam Sabri
Electrical and computer engineering, Altinbas University, Turkey
E-mail: [email protected]

Received: February 19, 2018 Accepted: March 06, 2018 Published: March 12, 2018

Citation: Bassam S, Osman N, Haitham S (2018) New Methods for Analyzing Spatio-Temporal Simulation Results of Moran’s Index. J Comput Eng Inf Technol 7:2. doi: 10.4172/2324-9307.1000196

Abstract

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.

Keywords: Spatio-temporal autocorrelation; Monte carlo simulation; Moran’s I; Asymmetric eigenvector maps; Chesapeake bay; Local contributions to beta diversity; Moran’s eigenvector maps

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