Fuzzy Methods and Approximate Reasoning in Geographical Information Systems

This issue has been dedicated to the usage of fuzzy logic in the context of Geographical Information\udSystems (GIS) and were receveid the following papers whose contents are described below:\ud- in the paper of A. Hofmann, S. Hoskova-Mayerova, and V. Talhofer, the authors use a GIS tool\udwhich is useful to study the influence of geographic and climatic factors on the terrain passability of\udarmed forces and the Integrated Rescue System.\ud- In the first paper of S. Sessa and F. Di Martino, the authors propose the usage of the well known\udExtended Gustafson-Kessel clustering method, encapsulated in a GIS tool, for detecting hotspots in\udspatial analysis. The data consist of geo-referenced patterns corresponding to positions of Taliban’s\udattacks against civilians and soldiers in Afghanistan happened during the period 2004÷2010: the\udformation through time of new hotspots is observed as well.\ud- In the paper of M. Burda, P. Rusnok and M. Stepnicka, an application of the so-called fuzzy\udGUHA method is presented for good peak prediction which were used in order to mine for fuzzy\udassociation rules expressed in natural language. The provided data was firstly extended by a creation of\udartificial variables describing various features of the data. The resulting variables were later on\udtranslated into fuzzy GUHA tables with help of Evaluative Linguistic Expressions in order to mine\udassociations. The found associations were interpreted as fuzzy IF-THEN rules and used jointly with the\udPerception-based Logical Deduction inference method to predict expected time shift of low rate peaks\udforecasted by the given physical model.\ud- In the second paper of S. Sessa and F. Di Martino, a fuzzy process for evaluating the reliability of\uda spatial database is defined: the area of study is partitioned in iso-reliable zones, defined as\udhomogeneous zone in terms of data quality and environmental characteristics. This spatial database in\udthematic datasets of which everyone includes a set of layers. We estimate the reliability of each\udthematic dataset and therefore the overall reliability of the spatial database. This method is tested on the\udspatial dataset of the town of Cava dèTirreni (Italy) by means of a suitable GIS.\ud- In the paper F. Di Martino et al., an application of the Extended Fuzzy C-Means algorithm for\uddetecting spatial areas with high concentrations of events and tested to study their temporal evolution is\udproposed as well.This algorithm is implemented in a GIS tool. The data consist of geo-referenced\udpatterns corresponding to the residence of patients in the district of Naples (Italy) to whom was carried\udout a surgical intervention to the oto-laryngopharyngeal apparatus between the years 2008 ÷2012.\udThis special issue presents some noteworthy applications of the spatial analysis realized via GIS. Other\udapplications should be desiderable in the sterminated world of GIS. We are aware that the topics do not\udmeet easily desiderata of fuzzy authors, however we are at the beginning of a theory which is very\udpromising from an applicational point of view, mainly in the spatio-temporal evolution of events which\udeither difficult to evaluate in the future or are “fuzzy” for their same nature