Spatial (GIS-based) decision support system for the Westernport region

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

Based on the deeply analysis of the current status and the existing problems of GIS technology applications in Epidemiology, this paper has proposed the method and process for establishing the spatial decision support systems of AIDS epidemic prevention by integrating the COM GIS, Spatial Database, GPS, Remote Sensing, and Communication technologies, as well as ASP and ActiveX software development technologies. One of the most important issues for constructing the spatial decision support systems of AIDS epidemic prevention is how to integrate the AIDS spreading models with GIS. The capabilities of GIS applications in the AIDS epidemic prevention have been described here in this paper firstly. Then some mature epidemic spreading models have also been discussed for extracting the computation parameters. Furthermore, a technical schema has been proposed for integrating the AIDS spreading models with GIS and relevant geospatial technologies, in which the GIS and model running platforms share a common spatial database and the computing results can be spatially visualized on Desktop or Web GIS clients. Finally, a complete solution for establishing the decision support systems of AIDS epidemic prevention has been offered in this paper based on the model integrating methods and ESRI COM GIS software packages. The general decision support systems are composed of data acquisition sub-systems, network communication sub-systems, model integrating sub-systems, AIDS epidemic information spatial database sub-systems, AIDS epidemic information querying and statistical analysis sub-systems, AIDS epidemic dynamic surveillance sub-systems, AIDS epidemic information spatial analysis and decision support sub-systems, as well as AIDS epidemic information publishing sub-systems based on Web GIS.

Spatial Decision Support Systems (SDSS), which support spatial analysis and decision making, are currently receiving much attention. Research on SDSS originated from two distinct sources, namely, the GIS community and the DSS community. The synergy between these two research groups has lead to the adoption of state of the art technical solutions and the development of sophisticated SDSS that satisfy the needs of geographers and top-level decision makers. Recently, the Web has added a new dimension to SDSS and Web-based SDSS (WebSDSS) are being developed in a number of application domains. This article provides an overview of the emergence of SDSS, its architecture and applications, and discusses some of the enabling technologies and research challenges for future SDSS development and deployment.

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.

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

PurposeThe purpose of this paper is to develop a spatial intelligent decision support system (SIDSS) for increasing productivity in the rubber agroindustry by green productivity (GP) approach. The SIDSS was used to measure the productivity of rubber plantation and rubber agroindustry by GP approach, and select the best strategies for increasing the productivity of rubber agroindustry.Design/methodology/approachThis system was developed by combining spatial analysis, GP, and fuzzy analytic network process (ANP) with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry. Rubber plantation productivity measurement model was used to find the productivity level of rubber plantation with fuzzy logic, and also to provide information and decision alternatives to all stakeholders regarding spatial condition of rubber agroindustry, production process flow, and analysis of the seven green wastes at each production process flow using the geographic information system. GP measurement model was used to determine the productivity performance of the rubber agroindustry with the green productivity index (GPI). The best strategy for increasing the productivity was determined with fuzzy ANP.FindingsRubber plantation measurement model showed that the average of plantation productivity was 6.25 kg/ha/day. GP measurement model showed that the GPI value of ribbed smoked sheet (RSS) was 0.730, whereas of crumb rubber (CR) was 0.126. The best strategy for increasing the productivity of rubber agroindustry was raw material characteristics control. Based on the best strategy, the GPI value of RSS was 1.340, whereas of CR was 0.228.Research limitations/implicationsThis decision support system is still limited as it is based on static data; it needs further development so that it can be more dynamically based on developments in the rubber agroindustry related levels of productivity and environmental impact. In addition, details regarding the decision to increase the productivity of the rubber section by benchmarking efforts should be studied further, both among plantation as well as among countries such as Thailand so that the productivity of rubber plantation and agroindustry can be integrated.Practical implicationsThis research can help the planters to select superior clones for rubber trees, to improve the technique of tapping latex, and to use a better coagulant. The good quality and quantity of raw material is a key factor in increasing the productivity of rubber agroindustry; if the quality of latex is good then the resulting product will also have a good quality and production cost can be reduced. In addition, the application of GP through the calculation of GPI value using improvement scenarios can be used as a reference and comparison for evaluating the performance of rubber agroindustry to reduce the waste generated by the activities of rubber processing plant.Social implicationsReduction of waste generated by production activities can improve the quality of life of the workforce and the environment. The calculation of GPI value can also be used as a basis to use raw materials, water, and electricity more efficiently.Originality/valueThis system was developed by combining spatial analysis, GP, and fuzzy ANP with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry.

To begin with, we discuss the combination between Grid and DSS; then construct a spatial river basin decision support system base on information Grid; and then study it in detail; finally focus our attention on decision-making models.

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

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.

Coconut agroindustry downstream has received its importance regarding the impact on the sustainability of coconut commodities. However, developing the downstream side of coconut remains a great challenge due to its complexity comprised of actors involved with their interests, the value of decision-making variables that are difficult to predict, and the presence of incomplete information in determining the right and fast decisions. To fill the gap, we need a spatial intelligent decision support system to accelerate the decision-making process. The method used in this research was the system development life cycle with the waterfall approach. This research identified system mechanisms, system analysis, system design, system configuration, and system development. The results of this study are the basis for developing system applications.

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

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

Bringing ecosystem services indicators into spatial planning practice: Lessons from collaborative development of a web-based visualization platform

Financial Inclusion is, in many ways, a spatial planning issue: Where do financial institutions provide services, how far do customers travel to access mobile money, which services are available where and how is agent cash-flow handled? Utilising geodata can contribute significantly to measuring financial access and thus assist in improving Financial Inclusion by expanding the reach of services and locating areas of economic exclusion. This study presents a new Spatial Decision Support System, with a frontend embedded directly within a spreadsheet interface, that enables measuring and planning financial access through geospatial analysis and Earth Observation derived products. The purpose is to complement existing Financial Inclusion measures, which rely significantly on large-scale representative household surveys to quantify financial access and opportunity to proxy quality of inclusion. The Decision Support System relies on Earth Observation and Public Participatory GIS, which enables a decoupling from the census cycle and global reach. Our findings indicate that a geospatial approach to measuring and making decisions regarding the location of financial access points can positively affect both tracking and delivering Financial Inclusion and reducing the urban–rural service cliff-edge. Our proposed geospatial methodology is useful for decision-makers in two ways: a) It allows the measurement of the large-scale geospatial reach of financial services – useful for decision-makers, planners, politicians, national statistical offices, and NGOs in charge of tracking progress towards the Sustainable Development Goals. b) It helps with planning and optimising services for local financial entities such as mobile money agents, brick and mortar bank branches, and less formal saving mechanisms such as saving clubs. The Spatial Decision Support System is currently used by several Financial Service Providers in Ghana and undergoing implementation for one in North-western Tanzania.

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.