Web-based Spatial Decision Support Systems (WebSDSS): Evolution, Architecture, Examples and Challenges

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

Web-based spatial decision support system for optimum route to forest fires: A case of Viphya plantations

SmartScape™: A web-based decision support system for assessing the tradeoffs among multiple ecosystem services under crop-change scenarios

In this research, a web-based spatial decision support system (SDSS) was developed to demonstrate how the Internet can be used to deliver low-cost, user-friendly and interactive land use decision support to a wide audience. Although the resulting Cape Land Use Expert System (CLUES) was specifically developed to perform land suitability analysis in the Western Cape, the technology can also be applied to other regions and modified for other applications. CLUES consists of five components: a land unit database (LUD), knowledge base, inference engine, web map service (WMS) and graphical user interface (GUI). The LUD consists of polygons (land units) and attributes (land properties), while the knowledge base stores each user's land use requirement rules. These rules are used by the inference engine to rate the suitability of each land unit in the LUD. The result is then mapped by the WMS and presented to the users as suitability maps. Users can direct the entire analysis through a user-friendly GUI. The development and demonstration of CLUES exposed several advantages and limitations of current web technology and has demonstrated that the Internet offers great opportunities for the deployment of land use suitability information to a wide audience.

Geospatial web services within a scientific workflow: Predicting marine mammal habitats in a dynamic environment

Decision making for crop production planning is essentially driven by location-based or more precisely by space-oriented information. Therefore, farmers and regional experts in the field mostly rely on new spatial-data-oriented decision making tools. IVIP is a prototype for a Web-based Spatial Decision Support System (WSDSS) demonstrating the benefits of location-based decision making using digitalized geographic information about ground allocation and soil quality. We present how the library of potential models for the IVIP WSDSS has been realized by extending the Scientific Workflow Management System Kepler that assists the collaboration of agricultural experts and computer scientists during model development. We first describe the requirements of our WSDSS, and then give a short introduction to the Kepler platform and explain in detail which extensions have been realized: cascading client-server architecture, spatial operations support, and WSDL interface. Finally, we illustrate how the biomass yield model has been modeled in our system.

This paper presents the conceptual design of a spatial decision support system (SDSS) proposed for Victorias Westernport region that aims for the sustainable and integrated (whole-of-catchment) management of regional natural resources. It is a solution integrating a range of approaches including, GIS technology, a scenario management tool, state-of-art terrestrial and marine models, environmental management strategy evaluation and multi-criteria techniques.Traditionally, GIS are key to (spatial) data management, but lack problem domain modelling capability. This means additional processing or analytical capabilities are needed to extend functionality for decision making. The Westernport SDSS builds upon a GIS but draws on models and data processing systems and interacts with other parts of an overall information system to support decision-making. This system utilises a number of models that are interlinked through a cascade of their results. Put simply, one set of model results input into the next in a modelling chain. The system will derive a set of socio-economic-environmental measures (performance indicators), such as land use, nutrient and sediment concentration in water (water quality measures), and other relevant indicators for coastal and bay ecosystems. Users will then be able to systematically compare alternative natural resource management plans and strategies in light of multiple and possibly conflicting criteria. By integrating relevant models within a structured framework, the system will promote transparency of policy development and natural resources management.

Due to their extension and complexity, problems concerned with geographic space are usually classified as ill or unstructured. For these reasons decision-making processes (DMP) that conclude with the selection of optimal or satisfactory solutions require effective and efficient means of support. Spatial Decision Support Systems (SDSS) are computer systems developed to support DMP in which the problems have geographic dimensions and whose structure is complex or impossible to delineate. These systems are functionally composed of data and scientific models managed with the aim of providing maximised support to DMP. The component represented by spatial data is one of the main obstacles that have to be overcome for SDSS to give effective support. Geographic Information Systems (GIS) have been a paradigm in SDSS development strategies, fundamentally due to their capacity to collect, store, and handle spatial data. The scientific modelling component, represented by mathematical models of natural physical processes, usually is implemented in SDSS through specific software subsystems. Especially in the last seven years there has been great scientific interest in SDSS accompanied by a proliferation of adjacent technologies. Some authors have attempted to classify SDSS without, however, reaching a generally accepted proposal. This lack of clarity has made detailed analysis of existing systems and development of new projects more difficult. The aims of this research were: a) identify and analyse the main variables that determine current application software development methodologies in the field of SDSS; b) provide a taxonomy of these methodologies, with the objectives of being generic, general, and practical; c) identify and analyse the strategy that presents the greatest flexibility to develop effective SDSS. The research was based on a bibliographic survey and is part of the doctoral thesis of first author. The paper presents and criticises relevant issues about different development strategies and the respective systems produced. Three main variables were identified and guided the development of the taxonomy of the strategies. The paper proposes five taxonomic classes of coupling GIS technology and scientific modelling subsystems. These classes are defined and the paper argues that they are sufficient to categorise the main current methodologies as well as suggest places to expect new technologies. Each class is also exemplified with a number of SDSS applied to the watershed DMP domain. Within these classes, the research identified and analysed the one most likely to show the greatest flexibility to develop effective SDSS

Planning of shale gas infrastructure and drilling sites for hydraulic fracturing has important spatial implications. The evaluation of conflicting and competing objectives requires an explicit consideration of multiple criteria as they have important environmental and economic implications. This study presents a web-based multicriteria spatial decision support system (SDSS) prototype with a flexible and user-friendly interface that could provide educational or decision-making capabilities with respect to hydraulic fracturing site selection in eastern Ohio. One of the main features of this SDSS is to emphasize potential trade-offs between important factors of environmental and economic ramifications from hydraulic fracturing activities using a weighted linear combination (WLC) method. In the prototype, the GIS-enabled analytical components allow spontaneous visualization of available alternatives on maps which provide value-added features for decision support processes and derivation of final decision maps. The SDSS prototype also facilitates nonexpert participation capabilities using a mapping module, decision-making tool, group decision module, and social media sharing tools. The logical flow of successively presented forms and standardized criteria maps is used to generate visualization of trade-off scenarios and alternative solutions tailored to individual user's preferences that are graphed for subsequent decision-making.

Enhanced validity and reliability of spatial decision support systems (SDSS) for sustainable transportation decision-making

BackgroundDuring a mass casualty incident, evacuation of patients to the appropriate health care facility is critical to survival. Despite this, no existing system provides the evidence required to make informed evacuation decisions from the scene of the incident. To mitigate this absence and enable more informed decision making, a web based spatial decision support system (SDSS) was developed. This system supports decision making by providing data regarding hospital proximity, capacity, and treatment specializations to decision makers at the scene of the incident.MethodsThis web-based SDSS utilizes pre-calculated driving times to estimate the actual driving time to each hospital within the inclusive trauma system of the large metropolitan region within which it is situated. In calculating and displaying its results, the model incorporates both road network and hospital data (e.g. capacity, treatment specialties, etc.), and produces results in a matter of seconds, as is required in a MCI situation. In addition, its application interface allows the user to map the incident location and assists in the execution of triage decisions.ResultsUpon running the model, driving time from the MCI location to the surrounding hospitals is quickly displayed alongside information regarding hospital capacity and capability, thereby assisting the user in the decision-making process.ConclusionsThe use of SDSS in the prioritization of MCI evacuation decision making is potentially valuable in cases of mass casualty. The key to this model is the utilization of pre-calculated driving times from each hospital in the region to each point on the road network. The incorporation of real-time traffic and hospital capacity data would further improve this model.

A spatial decision support system for retail plan generation and impact assessment

Current spatial decision support systems (SDSS) in manufacturing environments, which are mostly CAD-based, have many drawbacks and thus may make less effective and efficient spatial decisions. GIS-based SDSS can overcome those drawbacks and support decision-making in a much effective manner. Unfortunately, there is little research on GIS-based SDSS in a manufacturing environment. This thesis study focuses on a systematic approach to the development of GIS-based SDSS in a manufacturing environment. By using Trane China as an example, the prototype of Trane China SDSS (TCSDSS) is developed by adopting the unified software development process. The TCSDSS prototype contains eight object modules, which incorporate ArcGIS embedded models, ArcGIS feature data, and MS Access attribute data, to support decision-making in Trane China. The benefits from the use of the TCSDSS prototype that have been found include its spatial analysis, network analysis, and 3D analysis functions besides the improvement on communication and efficiency. It is also found that the newly developed GIS software packages such as ArcGIS 8.3, with the enhanced drawing functions, can provide enough adequate precision level required by facility layout.

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

Smart Water is an essential component of the smart environment since life, sustenance, growth and death of the environment depends on water. The concept of smart water is derived from the concept of smart cities having one to one relationship in their building blocks. Smart Water Management is a very high responsive, intelligent digital system operated by IOTs and ICTs, clouds, related computer-based models along with humans to identify water-related issues and even automatically using artificial intelligence solving it in real time without human interventions. The chapter presents an attempt to develop a Smart Water Management for Kozhikode Metropolitan area. Water in the study area is studied in conjunction with the spatial distribution of community in a watershed. The issues arising out of the present and potential usage pattern for households for community wellbeing and economic development is the basis of water resources management in Kozhikode Metropolitan Area (KMA). KMA enjoys substantial precipitation a year and is bound by two major rivers on both sides and are lined by many streams within its jurisdiction. However, KMA faces many water-related problems. A SWOT analysis was performed to identify and consolidate the capabilities of various watersheds and its communities in the study area. The issues faced by various communities were grouped into few categories and solutions were proposed for each community. Integrated Smart Water Management is proposed as a solution to the problems faced by the communities. It is footed on the principles of Water democracy, which is implemented through a system of ICT, IoT and decision support systems. The Integrated Smart Water Management System enables the community to be aware of the issues well in advance and find and implement solutions proactively. A spatial decision support system (SDSS) is proposed to help the community to take decisions related to water management. The SDSS takes many of the decisions as per the set procedure and alerts the community only cases where a systematic solution is not available. The integrated water management system implements the decisions taken by the SDSS, through its automatic sensors and actuators managing the water resources.