%0 Journal Article
%T Fuzzy Reliability in Spatial Databases
%A Ferdinando Di Martino
%A Salvatore Sessa
%J Advances in Fuzzy Systems
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/107358
%X Today it is very difficult to evaluate the quality of spatial databases, mainly for the heterogeneity of input data. We define a fuzzy process for evaluating the reliability of a spatial database: the area of study is partitioned in isoreliable zones, defined as homogeneous zones in terms of data quality and environmental characteristics. We model a spatial database in thematic datasets; each thematic dataset concerns a specific spatial domain and includes a set of layers. We estimate the reliability of each thematic dataset and therefore the overall reliability of the spatial database. We have tested this method on the spatial dataset of the town of Cava de' Tirreni (Italy). 1. Introduction Fuzzy rule-based models are applied in geographical information systems (GIS) [1¨C3] and we use our previous approach [4, 5] for estimating the reliability of spatial databases. There the concept of geodata ˇ°reliabilityˇ± was introduced as a fuzzy measure of the quality of geodata, based on the analysis of uncertainty and quality of the data. Strictly speaking, in [5] the authors implement a tool called (Fuzzy Spatial Reliability Analysis) Fuzzy-SRA [6] for studying the reliability of the intrinsic vulnerability of aquifers by utilizing the DRASTIC model, encapsulated in a GIS; in [4] Fuzzy-SRA is used for estimating the reliability of the aerophotogrammetric set of geographic layers of the island of Procida (near Naples, Italy) and in [7] Fuzzy-SRA is applied in a GIS tool for implementing a fuzzy rule-based system for analyzing the eruption risk of the famous vulcan Vesuvius. As the first step, we need to divide the geographic area of study in isoreliable zones, that is, in zones having (quasi) homogeneous data quality and geographical characteristics. An expert sets the characteristics related to the quality of each layer (e.g., the percent of uncoded spot elevation features). Each characteristic, called ˇ°parameter,ˇ± is a measurable entity that could affect the quality of the dataset. After calculating the value of a parameter, a fuzzification process is applied for estimating the quality of the set of layers, where each fuzzy set is given by a triangular fuzzy number (TFN), which in turn is identified from a linguistic label. In other words, an isoreliable zone is a subarea of the area of study in which the quality of the geodata is homogeneous; that is, the values of the parameters are similar and with the same geographical characteristics (e.g., a flat country). The expert creates a fuzzy partition, labelling the TFNs with linguistic labels (say) (see, e.g.,
%U http://www.hindawi.com/journals/afs/2013/107358/