Iz stranih časopisa

U tekstu je dan popis radova koji su objavljeni u stranim časopisima

Iz stranih časopisa

U tekstu je dan popis radova koji su objavljeni u stranim časopisima

Establishing Robustness of a Spatial Dataset in a Tolerance-Based Vector Model

Spatial data are usually described through a vector model in which geometries are rep- resented by a set of coordinates embedded into an Euclidean space. The use of a finite representation, instead of the real numbers theoretically required, causes many robustness problems which are well-known in literature. Such problems are made even worst in a distributed context, where data is exchanged between different systems and several perturbations can be introduced in the data representation. In this context, a spatial dataset is said to be robust if the evaluation of the spatial relations existing among its objects can be performed in different systems, producing always the same result.In order to discuss the robustness of a spatial dataset, two implementation models have to be distinguished, since they determine different ways to evaluate the relations existing among geometric objects: the identity and the tolerance model. The robustness of a dataset in the identity model has been widely discussed in [Belussi et al., 2012, Belussi et al., 2013, Belussi et al., 2015a] and some algorithms of the Snap Rounding (SR) family [Hobby, 1999, Halperin and Packer, 2002, Packer, 2008, Belussi et al., 2015b] can be successfully applied in such context. Conversely, this problem has been less explored in the tolerance model. The aim of this paper is to propose an algorithm inspired by the ones of SR family for establishing or restoring the robustness of a vector dataset in the tolerance model. The main ideas are to introduce an additional operation which spreads instead of snapping geometries, in order to preserve the original relation between them, and to use a tolerance region for such operation instead of a single snapping location. Finally, some experiments on real-world datasets are presented, which confirms how the proposed algorithm can establish the robustness of a dataset

Impact of data representation rules on the robustness of topological relation evaluation

A spatial object is characterized not only by its geometric extents, but also by the spatial relations existing with its surrounding objects. An important kind of spatial relations is represented by topological relations. Many models have been defined in literature for formalizing the semantics of topological relations between spatial objects in the Euclidean 2D and 3D space. Nevertheless, when these relations are evaluated in available systems many robustness problems can arise, which are essentially related to the discrete representations adopted by such systems. In a Spatial Data Infrastructure (SDI) the perturbations introduced by the exchange of data between different systems can increase the robustness problems.This paper deals with a set of rules for the representation of spatial datasets which allow to evaluate topological relations in a robust way using existing systems. These rules are well-known and described in literature and are based on a few basic assumptions on the system behavior which are fulfilled by today\u2019s systems. The main contribution of this paper is to determine in detail which rules are sufficient in order to make each topological relation robust; it turns out that the rules depend not only on the topological relation being considered, but also on the geometric types of the involved geometries and on the dimension of the space in which they are embedded, thus giving rise to a very large number of possible combinations. The paper analyses the topological relations and a significant subset of the geometric types defined in the most recent version of the Simple Feature Access (SFA) model published by OGC, considering both a 2D and a 3D space