Sensitivity Analysis
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Senses, Alternative
Future Directions Semivariogram modeling is susceptible to errors and it may not be considered the best solution for a given dataset. It is more of an explanatory tool which is useful for getting a basic idea about the properties of the dataset. Advanced techniques based on Bayesian analysis are becoming more popular than semivariogram modeling. Cross References Kriging Statistical Descriptions of Spatial Patterns
land use/land cover, erosion factor, and spatial data resolution. If a small change in input variables or model parameters results in a relatively large change in the output, the output is said to be sensitive to the variables or parameters. Sensitivity analysis is usually conducted via a series of tests in which the modeler uses different input values that vary around a central value within certain bounds to see how a change in input causes a change in the model output. By showing the dependence of a model on input variables or parameters, sensitivity analysis is an important tool in model design and model evaluation. Historical Background
References 1. Cressie, N.: Statistics for Spatial Data. Wiley-Interscience, (1993) 2. Clark, I.: Practical Geostatistics. Applied Science, (1979) 3. Banerjee, S., Carlin, B.P., Gelfand, A.E.: Hierarchical Modeling and Analysis for Spatial Data, Chapmann & Hall/CRC, (2004) 4. de Jong, S.M., van der Meer, F.D.: Remote Sensing Image Analysis Including the Spatial Domain (Remote Sensing and Digital Image Processing). Springer, (2006) 5. Ma, C.: The use of the variogram in construction of stationary time series models. J. Appl. Probability 41, 1093–1103 (2004) 6. Chica-Olmo, M., Abarca-Hernandez, F.: Computing geostatistical image texture for remotely sensed data classification. Comput. Geosci. (1999)
Senses, Alternative Uncertainty, Semantic
Sensitivity Analysis X IAOBO Z HOU , H ENRY L IN Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, PA, USA Synonyms Sensitivity test; Susceptibility analysis; Sampling-based methods Definition Sensitivity analysis is the process of ascertaining how the change in the outputs of a given model depends upon the changes in model input factors (variables or parameters). A spatial sensitivity analysis is to apportion the variance of outputs of a spatial analysis or spatial prediction model to the variation of model input attributes, such as elevation,
It is usually true that phenomena or processes in the real world are very difficult, or even impossible to quantitatively measure to absolute accuracy. Hence, parameters in dynamic spatial models characterizing such phenomena or processes have inherent uncertainty. Additionally, some parameters are not static properties, but dynamically change over time and/or space. Therefore, the parameter values of a spatial model are at least somewhat uncertain and eventually bring such uncertainty into the model output. Each model parameter usually contributes different uncertainties to the total uncertainty. It is good to know which pa
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