Spatial Multicriteria Decision Making and Analysis

ABSTRACTWith the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses.

One of the major accompaniments of the globalization is the rapid growing of urban areas. At the end of the 1970th only 38% of world lived in cities, this number increased to more than 50% by 2008. In 2030 two third of all people worldwide are expected to live in cities, many of them in megacities. Urban sprawl is a major environmental concern affecting cities and urban. Urban sprawl depends on the socio-economic situation in the cities. Thus, reducing migration, sustainable handling of the limited resources and smart growth are acknowledged as key tasks for urban planning. Coping with these tasks requires precise and adaptive planning instruments. The presented study is part of the research project GAUS (Gaining Additional Urban Space) aiming at inventorying the open space available in urban environments and, moreover, providing flexible multi-criteria spatial decision support system for its development. The method is based on VHR (Very high resolution) optical satellite data (QuickBird (QB) and IKONOS (IK)) which is applied on three study areas: Berlin, Istanbul, and Ruhr Area. Object-based image analysis is applied to map land cover and land use and derive metrics describing urban form and inner-urban structure on multiple scales. The workflow has been standardized and leads to comparable results across different test sites and datasets. In intersection with available GIS (Geographical Information System) and local ancillary data, the outputs of image analysis serve as input for a multi-criteria spatial decision support system. Flexible multi-criteria spatial decision support (MC-SDSS) tool has been created by using MATLAB (Matrix Laboratory) software and its tools (Mapping toolbox etc.). Users can change their weights and parameters with this tool for their different study areas. Urban planners can use final suitability maps of this tool. Thus complex decisions are supported by numerical calculation and spatial visualization in order to come to objective solutions. This work contribute to close the gap between remote sensing methods and applied urban planning. © 2011 IEEE.

BackgroundIn the energy transition framework towards decarbonization, green hydrogen, obtained through water electrolysis powered by renewable energy, is gaining importance. In order to pave the way to its production and trade, it is required to assess its main advantages and challenges, which are not only energy-related but involve also techno-economic, social, environmental, and geopolitical aspects. In line with this, the current article aims to provide a Multi-criteria Spatial Decision Support System to investigate the suitability of North African countries with respect to the production of solar-based hydrogen and its potential trade, and to apply it to Tunisia, as one of the most promising countries for becoming a competitive hydrogen exporting leader. Combining the Analytic Hierarchy Process and the Geographical Information System, this study focuses on evaluating the land suitability for solar hydrogen production at a country level, serving as the foundation for a methodology applicable across the entire North African region. After defining ten different criteria, these are spatially analysed and then prioritized according to different experts’ preferences, so that a final suitability map is obtained. The added value of the study is the inclusion of social and geopolitical criteria in this kind of assessments, often focused on techno-economic parameters alone.ResultsThe suitability map allows to classify the majority of the Tunisian areas as moderately or highly suitable, even if the most favourable areas in terms of availability of resources are often negatively influenced by the geopolitical or economic assessment. The sensitivity analysis has also proved the high suitability of Tunisia, with no areas assessed as very low suitable even if the different criteria are extremized.ConclusionsAmong the several influencing factors addressing the suitability for green hydrogen uptake, this article makes it possible to explore the social and geopolitical externalities, as well as the environmental and techno-economic dimensions. Even if stakeholders’ preferences affect the final results, the sensitivity analysis makes it possible to test their robustness. Supporting the adoption of new clean technologies towards the carbon-neutrality target, the methodological framework could be applied for other countries and also tailored on other specific technological pathways.

Challenges in the wind turbines location process in Central Europe – The use of spatial decision support systems

Multicriteria Spatial Decision Support System (MC-SDSS) is one of the most common tools for solving the spatial decision problems. Using the MC-SDSS in the Web 2.0 environment enhances the collaborative decision making by providing a flexible problem-solving framework, where the relevant GIS-based multicriteria decision analysis (MCDA) tools and information are provided for active participation/collaboration. However, the Web 2.0-based collaborative GIS-MCDA knowledge including the user generated content (preferences/opinions) and the GIS-MCDA elements are not machine-understandable. Thus, such knowledge cannot be interpreted, processed, searched, shared, and reused by the Web-based software agents and applications. Additionally, the GIS-MCDA models are often constructed based on the knowledge and understanding that arises out of the subjective viewpoints and they lack a shared and common reference for collaboration, thus making it hard for participants to collaboratively solve the decision problem. To address these problems, this paper proposes the Web 3.0-based collaborative MC-SDSS, which integrates the Web 2.0 (a Web of people) community methods and the Semantic Web (a Web of meaning) technologies. The collaborative/participatory GIS-MCDA knowledge is semantically modeled in an ontological framework as a backbone of the Semantic Web. The ontology is needed to express the collaborative GIS-MCDA knowledge in a shared, unified, structured, meaningful, and machine-interpretable form, ready for software agents and people to interpret, process, communicate, share, and reuse it. The paper demonstrates an implementation of the proposed Web 3.0-based MC-SDSS for solving the parking site selection problem in Tehran, Iran.

Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northeastern Greece

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.

A fuzzy multi-criteria spatial decision support system for solar farm location planning

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of the geographical information systems (GIS) and multiple criteria decision analysis (MCDA) methods. GIS-based MCDA allows the incorporation of conflicting objectives and decision maker (DM) preferences into spatial decision models. During recent decades, a variety of research articles have been published regarding the implementation of methods and/or tools in a variety of real-world case studies. The article discusses, in detail, the criteria and methods that are implemented in GIS-based landfill siting suitability analysis and especially the exclusionary and non-exclusionary criteria that can be considered when selecting sites for municipal solid waste (MSW) landfills. This paper reviews 36 seminal articles in which the evaluation of candidate landfill sites is conducted using MCDA methods. After a brief description of the main components of a MC-SDSS and the applied decision rules, the review focuses on the criteria incorporated into the decision models. The review provides a comprehensive guide to the landfill siting analysis criteria, providing details regarding the utilization methods, their decision or exclusionary nature and their monotonicity.

Dealing with spatial decision problems means very often to face a GIS and a multi-criteria decision analysis based problem at the same time. GIS and Multi-criteria Decision Analysis are two well defined research areas, but both can benefit one from each other. In the last twenty years several researches have paid attention to MCDA-GIS integration and to the development of Spatial Decision Support Systems (SDSS), but few papers presented examples of perfect integration. Also in case of perfect integration, they often integrate within GIS only one multicriteria model. Our application is a better contribution in this way. We developed four GRASS modules that permit to implement four different MCDA methods: the Regime, the FUZZY, the ELECTRE and the Dominance-based Rough Set Approach. One of the main advantages of our application over the previous ones is the possibility of a future development in an open-source context. The paper presents an application of the Regime, Electre and Fuzzy modules.

Forest management planning can be challenging when allocating multiple ecosystem services (ESs) to management units (MUs), given the potentially conflicting management priorities of actors. We developed a methodology to spatially allocate ESs to MUs, according to the objectives of four interest groups—civil society, forest owners, market agents, and public administration. We applied a Group Multicriteria Spatial Decision Support System approach, combining (a) Multicriteria Decision Analysis to weight the decision models; (b) a focus group and a multicriteria Pareto frontier method to negotiate a consensual solution for seven ESs; and (c) the Ecosystem Management Decision Support (EMDS) system to prioritize the allocation of ESs to MUs. We report findings from an application to a joint collaborative management area (ZIF of Vale do Sousa) in northwestern Portugal. The forest owners selected wood production as the first ES allocation priority, with lower priorities for other ESs. In opposition, the civil society assigned the highest allocation priorities to biodiversity, cork, and carbon stock, with the lowest priority being assigned to wood production. The civil society had the highest mean rank of allocation priority scores. We found significant differences in priority scores between the civil society and the other three groups, highlighting the civil society and market agents as the most discordant groups. We spatially evaluated potential for conflicts among group ESs allocation priorities. The findings suggest that this approach can be helpful to decision makers, increasing the effectiveness of forest management plan implementation.

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.

Spatial conservation prioritization does not necessarily lead to effective conservation plans, and good plans do not necessarily lead to action. These “science-action” gaps are pernicious and need to be narrowed, especially if the international goal of conserving 30% of the planet by 2030 is to be realized. We present the Earthwise Framework, a flexible and customizable spatial decision support system (SDSS) architecture and social process to address the challenges of these science-action gaps. Utilizing case study experience from regions within California, South Africa, and British Columbia, we outline the framework and provide the Little Karoo, South Africa SDSS data, code and results to illustrate five design strategies of the framework. The first is to employ an “open science” strategy for collaborative conservation planning and action. Another is that marginal value functions allow for the continuous accounting of element (e.g., habitat) representation in prioritization algorithms, allowing for an SDSS that is more automated and saves valuable time for stakeholders and scientists. Thirdly, we program connectivity modeling integrated within the SDSS, with an algorithm that not only automatically calculates all the least cost corridors of a region, but prioritizes among them and removes the ones that do not make ecological sense. Fourth, we highlight innovations in multi-criteria decision analysis that allow for both cost-efficient plan development, like representative solution sets, but also land-use planning requirements, like site specific valuation, in what appears to be a more transparent, understandable, and usable manner than traditional approaches. Finally, strategic attention to communicating uncertainty is also advocated. The Earthwise Framework is an open science endeavor that can be implemented via a variety of software tools and languages, has several frontiers for further research and development, and shows promise in finding a better way to meet the needs of both humans and biodiversity.

A proposed Multi Criteria Analysis decision support tool for international environmental policy issues: a pilot application to emissions control in the international aviation sector

The paper presents a multicriteria analysis decision support system called MultiChoice, designed to support decision makers in solving different multicriteria analysis problems. Various well-known methods and software systems are discussed. The basic features of the solving modules, the interface modules and the system modules are described.