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Surfaces are a central to geographical analysis. Their generation and manipulation are a key component of geographical information systems (GISs). However, geographical surface data is often not precise. When surfaces are used to model geographical entities, the data inherently contains uncertainty in terms of both position and attribute. Fuzzy
Geographic information systems (GIS) can enhance historical research by providing tools to explore the spatial relationships of locations in historical sources. However, no widespread methods currently exist for translating vaguely defined historical spatial information into GIS data formats and producing a location estimate. Other GIS techniques do exist that can model the necessary process. Multi-criteria decision analysis with fuzzy measures can be applied to vague historical records to approximate location. The Wieslander Vegetation Type Map dataset is used to demonstrate the model effectiveness. Results show that this technique successfully translated written descriptions of location into raster, or grid-based, surfaces within a GIS. Given the uncertainty of the qualitative descriptions, the technique resolved the text into a collection of locations instead of a single location, with a probability assigned to each location conveying the ambiguity associated with the results and the probabilistic nature of its interpretation.
Geographic Information Systems are computer-based systems for geographic analysis. They have been developed over the past twenty five years and are now widely used. A recent research direction has been the development of geocomputation , representing computer-based geographical analysis beyond the traditional bounds of GIS. In geocomputation, the computer is the research environment itself, not merely a tool. A key to geocomputation it that highly powered computing can be used with sufficient data to avaid traditional parametric approaches altogether. The term geocomputation includes the use of computer-based techniques such as artificial neural networks, genetic programming and fuzzy logic, but in a geographical context. This new book in the prestigious Innovations in GIS series, presents the latest research in geocomputational techniques as presented in the GIS UK Annual Conference.
A revision of Openshaw and Abrahart's seminal work, GeoComputation, Second Edition retains influences of its originators while also providing updated, state-of-the-art information on changes in the computational environment. In keeping with the field's development, this new edition takes a broader view and provides comprehensive coverage across the
The capabilities of modern technology are rapidly increasing, spurred on to a large extent by the tremendous advances in communications and computing. Automated vehicles and global wireless connections are some examples of these advances. In order to take advantage of such enhanced capabilities, our need to model and manipulate our knowledge of the geophysical world, using compatible representations, is also rapidly increasing. In response to this one fundamental issue of great concern in modern geographical research is how to most effectively capture the physical world around us in systems like geographical information systems (GIS). Making this task even more challenging is the fact that uncertainty plays a pervasive role in the representation, analysis and use of geospatial information. The types of uncertainty that appear in geospatial information systems are not the just simple randomness of observation, as in weather data, but are manifested in many other forms including imprecision, incompleteness and granularization. Describing the uncertainty of the boundaries of deserts and mountains clearly require different tools than those provided by probability theory. The multiplicity of modalities of uncertainty appearing in GIS requires a variety of formalisms to model these uncertainties. In light of this it is natural that fuzzy set theory has become a topic of intensive interest in many areas of geographical research and applications This volume, Fuzzy Modeling with Spatial Information for Geographic Problems, provides many stimulating examples of advances in geographical research based on approaches using fuzzy sets and related technologies.
The International Symposium on Spatial Data Handling is the premier research forum for Geographic Information Science. The Symposium is particularly strong in respect to identifying significant new developments in this field. The papers published in this volume are carefully refereed by an international programme committee composed of experts in various areas of GIS who are especially renowned for their scientific innovation.
An authoritative and state-of-the-art book bringing together some of the most recent developments in remote sensing and GIS analysis with a particular emphasis on mathematical techniques and their applications. With contributions from academia, industry and research institutes, all with a high standing, this book covers a range of techniques including: fuzzy classification, artificial neural networks, geostatistical techniques (such as kriging, cokriging, stochastic simulation and regularization, texture classification, fractals, per-parcel classification, raster and vector data integration and process modelling. The range of applications includes land cover and land use mapping, cloud tracking, snow cover mapping and air temperature monitoring, topographic mapping, geological classification and soil erosion modelling. This book will be valuable to both researchers and advanced students of remote sensing and GIS. It contains several new approaches, recent developments, and novel applications of existing techniques. Most chapters report the results of experiment and investigation. Some chapters form broad reviews of recent developments in the field. In all cases, the mathematical basis is fully explained.