Spatial Uncertainty Analysis in Ecological Biology

2013 ◽  
Vol 2 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Stelios Zimeras ◽  
Yiannis Matsinos
Keyword(s):  
Uncertainty Analysis ◽  
Incomplete Data ◽  
Quantitative Estimation ◽  
Stochastic Modelling ◽  
Spatial Uncertainty ◽  
Spatial Homogeneity ◽  
Spatial Connectivity ◽  
Analysis Methodology ◽  
Spatial Investigation ◽  
Neighbourhood Structure

Uncertainty analysis is the part of risk analysis that focuses on the uncertainties in the data characteristics. Important components of uncertainty analysis include qualitative analysis that identifies the uncertainties, quantitative analysis of the effects of the uncertainties on the decision process, and communication of the uncertainty. (Funtowwicz & Ravetz 1990; Petersen, 2000; Regan et a1., 2002; Katz 2002). The analyses include simple descriptive procedures till quantitative estimation of uncertainty, and decision-based procedures. The analysis may be qualitative or quantitative, depending on the stage of analysis required and the amount of information available. When a neighbourhood structure lattice system is applied, a spatial connectivity between regions is defined where investigation of that structure includes modelling of the spatial homogeneity is introduced. Spatial investigation involves stochastic modelling especially in cases where the incomplete data involves hide information’s. In this work a spatial analysis methodology was introduced and procedures to solve the problem with spatial variability are described.

Modelling Spatial Medical Data

2016 ◽  
pp. 75-89 ◽  
Author(s):  
S. Zimeras ◽  
Y. Matsinos
Keyword(s):  
Parameter Estimation ◽  
Incomplete Data ◽  
Real Data ◽  
Spatial Modelling ◽  
Spatial Process ◽  
Spatial Homogeneity ◽  
The Real ◽  
Other Information ◽  
The One ◽  
Neighbourhood Structure

Models are sometimes incomplete, especially in scaling data where other information of large regions needs to be predicted by smaller ones. Uncertainty analysis is the process of assessing uncertainty in modelling or scaling to identify major uncertainty sources, quantify their degree and relative importance, examine their effects on model output under different scenarios, and determine prediction accuracy. Especially for large dimensional data where spatial process in regional investigation are difficult to applied due to incompleteness leading us to spatial heterogeneity and non-linearity of our data. Modelling the uncertainty particular in scaling data starts with a general structure (linear most of the time) that explains as accurate as it is the real data and the model is built through adding variables, which are significant or which aid in prediction (hierarchical modelling). Parameter estimation is an important issue for the evaluation of these proposed models. Statistical techniques based on the spatial modelling could be proposed to overcome the problem of dimensionality and the spatial homogeneity between different grains levels based on the neighbourhood structure of the regions with similar characteristics. Investigation of the neighbourhood structure analysis could be applied using kriging or variogram techniques. In this work, we introduce and analyse methodologies for scaling data under uncertainty where incomplete data can be explained by spatial modelling at different scales. Incomplete data of uncertainties in regions involve spatial homogeneity upon neighbourhood structure between regions. The last could be illustrated by using spatial modelling techniques (like spatial autocorrelation, partition functions, and multilevel models). Parameter estimation of these models could be achieved by using stochastic (spatial hierarchical models, kriging, auto-correlation) methods. Comparison between different models could be achieved by considering statistical measures like log-likelihood ratio test. The best model is the one, which explains better the real data.

Spatial uncertainty analysis of population models

2005 ◽  
Vol 185 (1) ◽  
pp. 13-27 ◽  
Author(s):  
Henriette I. Jager ◽  
Anthony W. King ◽  
Nathan H. Schumaker ◽  
Tom L. Ashwood ◽  
Barbara L. Jackson
Keyword(s):  
Uncertainty Analysis ◽  
Population Models ◽  
Spatial Uncertainty

Development of an Uncertainty Analysis Methodology Associated with the Characterization of the Geological Environment: Study on its Applicability in the Tono area

2006 ◽  
Vol 932 ◽  
Author(s):  
K. Yanagizawa ◽  
S. Takeda ◽  
H. Osawa ◽  
Y. Suyama ◽  
H. Takase ◽  
...  
Keyword(s):  
Probability Theory ◽  
Uncertainty Analysis ◽  
Possibility Theory ◽  
Geological Environment ◽  
New Approach ◽  
Subsequent Investigation ◽  
Analysis Methodology ◽  
Environment Study ◽  
Parameter Ranges

ABSTRACTJNC has developed an uncertainty analysis methodology for application to the spatially heterogeneous characteristics of a geological environment. The developed methodology adopts a new approach that identifies all the possible options in concepts and parameter ranges that cannot be excluded in the light of evidence available. This approach enables uncertainties associated with the understanding at a given stage of the site characterization to be made explicit, using probability theory and possibility theory. The uncertainties could be reduced by screening, to exclude concepts and parameter ranges that can be denied in the light of additional evidence obtained in subsequent investigation stages. This paper describes an outline of the developed methodology and its applicability in the Tono area.

A Methodology for Spatial Uncertainty Analysis Of Remote Sensing and GIS Products

2005 ◽  
Vol 71 (12) ◽  
pp. 1423-1432 ◽  
Author(s):  
Guangxing Wang ◽  
George Z. Gertner ◽  
Shoufan Fang ◽  
Alan B. Anderson
Keyword(s):  
Remote Sensing ◽  
Uncertainty Analysis ◽  
Remote Sensing And Gis ◽  
Spatial Uncertainty

Treatment of Uncertainties: Output Updating in Complex Thermal-Hydraulics (TH) Computational Codes

2006 ◽  
Author(s):  
Mohammad Pourgol-Mohamad ◽  
Ali Mosleh ◽  
Mohammad Modarres
Keyword(s):  
Uncertainty Analysis ◽  
Statistical Approach ◽  
Comprehensive Treatment ◽  
Paired Data ◽  
Sources Of Uncertainty ◽  
Uncertainty Sources ◽  
Analysis Methodology ◽  
Integrated Methodology ◽  
New Information ◽  
Different Types

Uncertainty analysis methodology in complex system models requires a comprehensive treatment of many different types and sources of uncertainty sources. Proposed methodology as part of a broader research by the authors for an integrated methodology for uncertainty analysis of TH computational codes, allows updating of uncertainties upon availability of new information about inputs, models or output. This methodology is a statistical approach for output-based updating, with non-paired data. The proposed method employs a Gibbs MCMC sampler to estimate output distribution. Winbugs code was developed to implement the Bayesian solution proposed in this paper. The methodology is applied to the data from 200% cold leg LBLOCA test of LOFT facility [4].

scholarly journals An uncertainty analysis methodology associated with the characterization of the geological environment

2006 ◽  
Vol 48 (3) ◽  
pp. 149-167
Author(s):  
Koichi YANAGIZAWA ◽  
Seietsu TAKEDA ◽  
Naotaka SIGETA ◽  
Takeshi SEMBA ◽  
Yasuhiro SUYAMA ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Geological Environment ◽  
Analysis Methodology

Uncertainty analysis and evaluation of a complex, multi-specific weed dynamics model with diverse and incomplete data sets

2016 ◽  
Vol 86 ◽  
pp. 184-203 ◽  
Author(s):  
Nathalie Colbach ◽  
Michel Bertrand ◽  
Hugues Busset ◽  
Floriane Colas ◽  
François Dugué ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Incomplete Data ◽  
Data Sets ◽  
Dynamics Model ◽  
Analysis And Evaluation ◽  
Weed Dynamics
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