Modeling Uncertainty Based on Spatial Models in Spreading Diseases: Spatial Uncertainty in Spreading Diseases

Modeling Uncertainty Based on Spatial Models in Spreading Diseases: Spatial Uncertainty in Spreading Diseases

Stelios Zimeras (Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Mytilene, Greece) and Yiannis G. Matsinos (Department of Environment, University of the Aegean, Mytilene, Greece)
Copyright: © 2019 |Pages: 12
DOI: 10.4018/IJRQEH.2019100103

Abstract

Lately, spatial models have become a powerful, necessary statistical tool to estimate parameters where data are represented by regions of interests using the window method . Estimation processes based on the high dimensionality of the data have become difficult to implement especially in cases where variability in the spatial models is the main task to investigate. Variability between spatial models considering hierarchical levels of scale, most of the time, involves errors leading to uncertainty in spatial regions. Solving the problem with uncertainty via the estimation of errors in spatial models, complex models could be simplified in easiest ones and important decisions for the quality of data could be taken.
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Spatial Uncertainty

Environmental biology has been defined as the study of the effect of biological processes (Turner, 1989) on landscape pattern since landscape patterning strongly affects biological changes. For this reason, it is needed to develop spatial methods to estimate landscape structure (O’Neill et al., 1988; Dale, 1999), especially where high dimensionality is the main issue for the definition of spatial homogeneity.

The main concept for the use of spatial analysis is the definition of landscape areas for the estimation of patterns based on smaller ones. The hypothesis behind this is that landscapes are composed of a mosaic of textures. Small areas due to the definition of the region include less information that many times is unclear in defining variability between neighboring areas denoting uncertainty as part of the estimation procedure (Scheidt, 2018).

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