A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> Modelling Uncertainties <s> This article reflects on the past 30 years of academic research in the field of spatial data quality and tries to identify the main achievements, failures, and opportunities for future research. Most of this reflection results from a panel discussion that took place during the Sixth International Symposium on Spatial Data Quality (ISSDQ) in July 2009. <s> BIB001 </s> A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> Modelling Uncertainties <s> GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights. Sensitivity and uncertainty analysis for GIS-Multicriteria Decision Making is underdeveloped in GIScience.Uncertainties associated with GIS-Multicriteria for landslide susceptibility mapping are systematically explored.We develop a novel spatially-explicit approach and combine it with Dempster-Shafer Theory.The results demonstrate that the accuracy of GIS-Multicriteria can be improved through this spatially-explicit approach. <s> BIB002
The process of handling uncertainty in spatial data involves three steps. The first step involves the identifying the kind of error information available in each of the GIS data layer. The type of error information will determine which assumptions have to be made when error is modelled  . The second step is the definition of a conceptual model of error. The choice of the error model depends mainly on the kind of error information that is available and also on the error propagation technique that will be applied. Finally an appropriate error propagation function has to be selected to verify how error is propagated through the GIS operations. One of the most important hurdles in handling uncertainty in spatial database is the lack of knowledge about the error which is present in the source data. The type of error information which is available will define what kind of error modelling can be applied, whether error can be differentiated thematically and/or spatially, and as such will largely determine the quality of the whole error modelling process. Due to a potentially large number of parameters and the heterogeneity of data sources, the uncertainty of the results are difficult to quantify BIB002 . With increasing interest in analysis of uncertainty, many methods have been developed to estimate the model uncertainties. Choice between the methods is according to the level of model's complexity and preferences of a modeller. Shrestha & Solomantine, (2008) proposed various methods including analytical methods, approximation methods, sampling based methods, bayesian methods and methods based on analysis of the model errors. Analytical methods compute probability distribution function of model outputs. They are applicable for simple models where the propagation of uncertainty through the model is straightforward and can be analytically expressed. Their applicability is generally limited to models with linear summation of independent inputs. Approximation methods provide only the moments of the distribution of the uncertainty output variable. Some of approximation based methods depend on the use of the Taylor series expansions to propagate the uncertainty through model. Furthermore, the main advantage of approximation methods is that it is enough to propagate the moments of each probability distribution of the model inputs instead of the entire probability distributions. On the other side, the main disadvantages of these methods are, firstly, that they cannot be applied to problems with discrete or discontinuous behaviours because the model function should be differentiable. Secondly, it is computationally intensive as they typically require the evaluation of second order (may be higher) derivatives of the model. Thirdly, although these techniques are capable of propagating central moment of input distributions, information regarding the tails of the input distributions cannot be propagated. Sampling based methods are the most common techniques to study the propagation of uncertainty. These methods involve running a set of model simulations at a set of sampled points from probability distributions of inputs and establishing a relationship between inputs and outputs using the model results. There are two types of sampling, the first one is simple random sampling that depends on the entire population and second is stratified random samples, which separate the population elements into non overlapping groups called strata. These methods are able to work with complex and non-linear model and are capable of solving a great variety of problems. They do not require access to the model equations. Monte Carlo methods and Latin Hypercube Sampling methods are the most common sampling based uncertainty analysis methods. Bayesian methods utilize Bayes' theorem to estimate or update the probability distribution function of the parameters of the model and consequently estimate the uncertainty of model results. Generalized Likelihood Uncertainty Estimation [GLUE] is one of the sampling methods that is also based on Bayesian methods. The idea in the methods based on the analysis of model errors is to analyse the model residuals that occurred in reproducing the observed historical data BIB001 .