Although interest in Spatial Decision Support Systems (SDSS) continues to grow rapidly in a wide range of disciplines, students, planners, managers, and the research community have lacked a book that covers the fundamentals of SDSS along with the advanced design concepts required for building SDSS. Filling this need, Spatial Decision Support System

In the past half century, we have experienced two major waves of methodological development in the study of human behavior in space and time. The fIrst wave was the well known "quantitative revolution" which propelled geography from a mainly descriptive discipline to a scientifIc discipline using formalism such as probability, statistics, and a large-number of mathematical methods for analyzing spatial structures and processes under certainty and uncertainty. The second wave is the recent advancement of geographical information systems which equips geographers with automation in the storage, retrieval, analysis, and display of data. Both developments have significant impacts on geographical studies in general and solutions to real life spatio-temporal problems in particular. They have found applications in urban and regional planning, automated mapping and facilities management, transportation planning and management, as well as environmental planning and management, to name but a few examples. Both developments have one thing in common. They one way or the other use computer to process and analyze data. However, not until recently, there has been very little interaction between the two. Quantitative models have largely been developed independent of the underlying data models and structures representing the spatial phenomena or processes under study. Display of analysis results has been primitive in terms of the utilization of computer graphic technologies. Formal models, in addition to their technical difficulties, have poor capability in communication with users. Geographical information systems, on the other hand, have originally been developed with a slight intention to entertain powerful analytical models.

The management of data to understand complex and interwoven processes of sustainable development has been a great challenge for researchers, planners, and decision makers. Remote sensing and GIS-based policy decision support systems not only help them to solve spatially related environmental and socio-economic issues; they also provide a powerful tool for integrating spatial and non-spatial datasets with analytical and spatial models and knowledge domains. Recent advances in the modern spatial tools of remote sensing and GIS combined with advanced computation techniques have enhanced the efficiency and capabilities of policy development. This book expands the scientific knowledge base in various physical and socio-economic issues among scholars, planners, and decision makers for policy development and research regarding sustainable development. It also demonstrates the importance of modern spatial decision support tools of remote sensing and GIS to better understand sustainable development processes and policy development. Furthermore, the book discusses case studies providing new insights as to how remote sensing and GIS-based decision support systems contribute to understanding physical and socio-economic processes and developing pragmatic policy for sustainable development. This book covers land surface temperature, hydrological processes, terrain mapping, flood and landslide hazards, land use and land cover dynamics, crime hotspots, urban health issues, tourism, agriculture, forest management, flood mitigation, urban sprawl, and village information systems, among others. Readers will find this book to be an invaluable resource for understanding and solving diverse physical and human issues for policy development related to sustainable planning and management.

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference.

Inhaltsangabe:Introduction: The main goal of this thesis is to develop a framework for a Web based Spatial Decision Support System (WSDSS) for supporting the development of river restoration strategies. Due to their free availability, open source/free GIS tools and technology are used to develop this decision support framework supporting a multi-user decision support platform via the web. The integration of latest multi-scale river ecological knowledge is assured by the strong relatedness of this thesis to a project that has been run at the University of Natural Resources and Applied Life Sciences (BOKU) in Vienna between 2005 and 2008 the MIRR project (Model based Instrument for River Restoration). By order of the Austrian Ministry of Life, being responsible for the implementation of the EU-Water Framework Directive, a strategic instrument to identify hydro-morphological restoration measures for running waters to enhance the ecological status of rivers in Austria measured by fish was developed at the Institute of Hydrobiology and Aquatic Ecosystem Management (IHG) at the BOKU based on comprehensive multivariate analyses of fish/pressure relationships using data from Lower Austria. The identification and quantification of the effects of the main relevant pressure types allowed the development of flow charts for guiding restoration activities at multiple scales. Up to now no tool exists allowing an automated evaluation of the effects of different restoration strategies on the ecological status of the so called water bodies, the relevant scale for action and reporting with regard to the EU-WFD. Furthermore existing web based GIS tools related to rivers and the implementation of the EU WFD in Austria and on an EU level do not allow any interaction with the data, and are therefore not well suited for decision support (for example the Water Information system of Austria WISA , or the Water Information System of Europe WISE ). The following parts of the MIRR project conducted under the supervision of the author of this thesis as work package leader, and can be seen as part of this thesis, although mainly content relevant for the development of the WSDSS framework is reproduced here: Literature research and definition of relevant parameters to model fish/pressure relationships on multiple spatial and temporal scales, Investigation and collection of the available GIS datasets in Austria, that might be of relevance for the MIRR project, and matching [...]

First published in 1999, this volume consists of selected papers presented at the North American Meetings of the RSAI along with invited contributions from scholars active in the field of spatial multicriteria decision making and analysis. It is meant to present diverse lines of research in spatial multicriteria decision making and analysis under the multidisciplinary umbrella of Geographic Information Science. The first part explores selected theoretical and conceptual aspects of spatial multicriteria decision making and analysis not confined to any specific application domain. Part 2 consists of six chapters focusing on various forms of location decision and analysis problems. Finally, part 3 contains five chapters on various spatial decision problems whose systemic scope sets them apart from locational decision problems.

This book discusses the use of geographic information systems (GIS) for spatial decision support systems (SDSS). It argues that the increased availability in spatial business data has created new opportunities for the use of GIS in creating decision tools for use in a variety of decisions that involve spatial dimensions. This chapter identifies visualization and analytical capabilities of GIS that make such systems uniquely appropriate as decision aids, and presents a conceptual model for measuring the efficacy of GIS-based SDSS. The discussions on the applications of SDSS and future enhancements using intelligent agents are intended to inform practitioners and researchers of the opportunities for the enhancement and use of such systems.

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference.