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

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

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.

Solutions to spatial problems often impact a variety of stakeholders. Geographic information systems (GIS) and spatial decision support systems (SDSS) provide useful tools for the analysis of spatial problems and can be used to investigate the impact of alternative solutions on individual stakeholders. These technologies are, however, often expensive and difficult to implement. As such, some suggest that GIS and SDSS provide an unfair advantage to those with the economic means needed to implement the technology.

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

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.

\n Um dos problemas que afetam as cidades brasileiras é a falta de uma metodologia adequada para a implantação de equipamentos coletivos públicos (escolas, postos de saúde, entre outros), e da utilização de modo racional desses equipamentos. Esse foi o ponto de partida para esse trabalho, cujo objetivo principal foi desenvolver uma ferramenta de análise espacial para auxiliar o poder público no planejamento e na gestão dos serviços públicos de educação e saúde, no que concerne basicamente à melhor localização das unidades básicas de atendimento e à melhor distribuição dos usuários por essas unidades, buscando reduzir os custos de transporte. Após uma avaliação da atual política de distribuição e utilização dos equipamentos coletivos de educação e saúde no Brasil e no mundo, foi realizada uma análise dos conceitos relativos às ferramentas de apoio a problemas de decisão de caráter espacial, particularmente os Sistemas de Apoio à Decisão Espacial (SADE). De posse dessa base conceitual, buscou-se formular as bases de um sistema (ou uma metodologia) que apoiasse a implantação de novos equipamentos coletivos e uma utilização eficiente dos equipamentos já existentes, tendo como parâmetro principal de comparação o custo de deslocamento dos usuários. Esse sistema deveria ser adequado à realidade das cidades médias brasileiras, de maneira geral hoje inseridas num cenário de falta de planejamento e de ausência de bases de dados estruturadas (e atualizadas). Ainda, como proposta adicional, procurou-se incorporar como ferramentas de apoio ao sistema algumas técnicas emergentes que, embora relativamente pouco utilizadas no planejamento urbano até o presente momento, apresentam grande potencial para tal. São elas: os Autômatos Celulares (ou CA, Cellular Automata) e as Redes Neurais Artificiais (RNA). Os fundamentos do sistema concebido foram transpostos para uma aplicação prática desenvolvida em um Sistema de Informações Geográficas (SIG) através de um estudo de caso conduzido na cidade de São Carlos (SP), cujos resultados demonstraram que quando se pensa em otimizar os custos de deslocamento, a principal ação a ser empreendida é a redistribuição da demanda às unidades de oferta, antes de se pensar na abertura de novas unidades. Mais importante do que os resultados numéricos obtidos, foi a confirmação de que é possível se utilizar as diversas ferramentas de planejamento e gestão de modo integrado. A partir dessa metodologia, um investimento em desenvolvimento de software pode levar à construção de um efetivo Sistema de Apoio à Decisão Espacial. De maneira mais geral, pode-se afirmar que a obtenção de dados é o grande obstáculo para pesquisas dessa natureza. A montagem de uma base de dados sólida e confiável é, sem dúvida, o ponto crucial para a execução de projetos potencialmente bem sucedidos\n

This chapter provides an overview of SDSS (Spatial Decision Support System) by differentiating them from geographical information systems (GIS) and examining the types of problem to which they can be applied, the decision-making processes they support, typical system designs, and a framework for their implementation and evolution during the decision research process. It begins with an example of a DSS that illustrates the nature of the problem which SDSS must address. The characteristics of geoprocessing systems in general are then reviewed. Following this, a framework and an architecture for SDSS are presented. Finally, some brief conclusions are offered. -from Author

A time-based model and GIS framework for assessing hazardous materials transportation risk in urban areas

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.

Advancement of Geographic Information System (GIS) technologies and Artificial Intelligence (AI) supports development of Decision Support System (DSS) and Spatial Decision Support System (SDSS) to solve complex real life problems. Its areas of implementation encompasses diverse fields from defense to civil departments, from meteorology to disaster management, from traffic analysis to network planning etc. Centre for Development of Telematics (CDOT) has developed a software system for analyzing and identifying optimized GIS routes for laying optical fiber who's planning has been done by Bharat Broadband Network Limited (BBNL). BBNL has done planning for connecting Customer Premise Equipment (CPE) to the Central Office (CO). These routes have been planned by doing manual foot survey. This software system analyses whether the planned GIS routes are optimal or not and suggests optimized fiber routes for planning. In this paper we have presented a solution to the problem that we faced during above mentioned project execution. We have suggested an algorithmic solution based on C4.5 Decision Tree Machine Learning Algorithm. This software system first learns based on user input and then creates rules. Using these rules software system provides fiber planning of optical network over GIS. This algorithm, using machine learning, finds best possible route from a set of routes calculated using different GIS data sources.

Geographic information systems (GISs) as a technology have been studied and reported extensively and, not unexpectedly, in the field of geography. The various ways of capturing spatial data, arranging attribute data into appropriate database structures, and making the resulting large data sets efficient to store and query have been extensively researched and reported (Densham, 1991). However, the geographic research community has only recently noted the need to study how GISs are used as decision tools, especially with regard to how such decision making might be related to a decision maker’s cognitive style (Mennecke, Crossland, et al., 2000). As an example, the University Consortium for Geographic Information Science called for research examining how geographic knowledge is acquired through different media and by users with different levels of experience and training (University Consortium for Geographic Information Science, 1996). Researchers in the fields of decision sciences and information systems have more recently begun to make contributions in the area of decision making with GISs. When a GIS is employed as a decision support system, in these studies the resultant system is often referred to as a spatial decision support system, or SDSS (see Crossland, 1992; Crossland, Perkins, et al., 1995; Mennecke et al., 2000). A geographic information system in its simplest form is a marriage of accurately scaled digital maps with a database. The digital maps comprise spatially referenced details such as natural elements (lakes, rivers, topographic elevation contours, etc.), manmade objects (buildings, roads, pipelines, etc.), and political boundaries (city limits, state and county lines, international boundaries, etc.). These natural elements are typically referenced, with varying degrees of precision, to latitude/longitude coordinates on the earth’s surface. It must be noted here that the degree of precision and, more importantly, differences in degrees of precision for the various elements are the subjects of much research and user consternation in applications of GISs to solving problems. The database, in turn, catalogs information about the various spatial elements (e.g., the names of rivers, names of buildings, building owner, operator of a pipeline, etc.). These descriptive entries in the database are often referred to as attributes of the various spatial elements. A GIS may be paired with the global positioning system (GPS), from which real-time, satellite-derived location information may be derived, as provided by an appropriate GPS receiver.

Abstract. Cities that have been continuously inhabited and embody the spatial traces of historical continuity are defined as “multi-layered”. The remnants of historical periods and cultures, which constitute the stratified layers of the city and the interconnections among them, contribute to the spatial complexity and identity of multi-layered cities. However, when these remnants are not perceivable or effectively integrated into the contemporary city, the conservation and long-term sustainability of multi-layeredness become increasingly challenging. This necessitates the documentation of historical layers and the synthesis of fragmentary information from diverse sources into a coherent and systematic framework. Thus, the aim is to make the components of different historical periods in various parts of the city known, and to understand and evaluate their relationship with the contemporary city both vertically and horizontally. Ankara, inhabited since prehistoric times and characterized by being a multi-layered city, was chosen as the study area. To produce comprehensive and usable information, Geographic Information Systems (GIS) were utilized. GIS facilitates the processing of complex and voluminous data from diverse disciplinary sources. "MULAAN▪GIS [MUlti-LAyered ANkara GIS]" was produced by processing historical period components into the database, together with the attributes of the identity areas representing their period. This approach unveiled historical continuities and discontinuities, the physical, functional, visual, and intellectual integration levels, along with the challenges faced by citizens. The historical spatial dataset and integration degrees created in the GIS have the potential to serve as a spatial decision support system on heritage protection, thereby providing an input for spatial plans.

ABSTRACT: Watershed management decision making is a complex process. Cooperation and communication among federal, state, and local stakeholders is required while balancing biophysical and socioeconomic concerns. The public is taking part in environmental decisions, and the need for technology transfer from public agencies to stakeholders is increasing. Information technology has had a profound influence on watershed management over the past decade. Advances in data acquisition through remote sensing, data utilization through geographic information systems (GIS), and data sharing through the Internet have provided watershed managers access to more information for management decisions. In the future, applications incorporating hydrologic simulation models, GIS, and decision support systems will be deployed through the Internet. In addition to challenges in making complex modeling technology available to diverse audiences, new information technology issues, such as interoperability, Internet access, and security, are introduced when GIS, simulation models, and decision support systems are integrated in an Internet environment. This paper presents a review of current use of information technology in watershed management decision making and a discussion of issues created when developing Internet based, integrated watershed management decision support systems. A prototype spatial decision support system (SDSS) for rangeland watershed management was developed using web services, which are components that communicate using text based messages, thus eliminating proprietary protocols. This new framework provides an extensible, accessible, and interoperable approach for SDSS.

Due to constant decrease in farmlands, it is important to identify the best lands for sustainable agriculture (productive and profitable agriculture that protects the environment and that is socially equitable). This requirement has resulted in the development of land suitability maps for agriculture by combining a range of factors. Spatial analysis approaches, based on the concepts of the weighted sum, combined with Geographical Information Systems (GIS) offer the opportunity to efficiently produce these land suitability maps. The functions of the weighted sum make it possible to assign numerical weights, to distinguish between positive and negative criteria and to rank alternatives. A spatial decision support system has been developed for establishing the land suitability map for agriculture. It incorporates a version of the weighted sum method SAW (Simple Additive Weighting), applicable to the vector data model, in ArcGIS within the GIS program package environment. This approach has been tested on the area of Mleta (Algeria) to assess the land suitability for durum wheat agriculture. The parameters and the classification system used in this work are inspired from the FAO. The coherence of results confirms the system effectiveness.

This paper describes the possibility of the Geographic Information Systems (GIS) as a means to support decision making in solving spatial problems. Spatial problems accompany every human activity, of which agriculture is no exception. The solutions to these problems requires the application of available knowledge in the relevant decision-making processes. GISs integrate hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. Coupled with GISs, geography helps to better understand and apply geographic knowledge to a host of global problems (unemployment, environmental pollution, the loss of arable land, epidemics etc.). The result may be a geographical approach represents a new way of thinking and solutions to existing spatial problems. This approach allows to apply existing knowledge to model and analyze these problems and thus help to solve them.