A Web 3.0-driven Collaborative Multicriteria Spatial Decision Support System

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.

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.

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.

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.

Intelligent argumentation based collaborative decision making system assists stakeholders in a decision making group to assess various alternatives under different criterion based on the argumentation. A performance score of each alternative under every criterion in Multi-Criteria Decision Making (MCDM) is represented in a decision matrix and it denotes satisfaction of the criteria by that alternative. The process of determining the performance scores of alternatives in a decision matrix for criterion could be controversial sometimes because of the subjective nature of criterion. We developed a framework for acquiring performance scores in a decision matrix for multi-criteria decision making using an intelligent argumentation and collaborative decision support system we developed in the past [1]. To validate the framework empirically, we have conducted a study in a group of stakeholders by providing them an access to use the intelligent argumentation based collaborative decision making tool over the Web. The objectives of the study are: 1) to validate the intelligent argumentation system for deriving performance scores in multi-criteria decision making, and 2) to validate the overall effectiveness of the intelligent argumentation system in capturing rationale of stakeholders. The results of the empirical study are analyzed in depth and they show that the system is effective in terms of collaborative decision support and rationale capturing. In this paper, we present how the study was carried out and its empirical results.

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

Part 1 Theoretical and conceptual issues: spatial multicriteria decision analysis developing web-based GIS/MCE - improving access to data and spatial decision support tools spatial dimensions in multicriteria analysis. Part 2 Locational decision-making and analysis: equitable approaches to location problems application of a multiple criteria trade-off approach to spatial decision-making GDSS in highway planning - their use and adoption urban land management in transition economies - a decision support prototype for Shanghai the strategic identification of suitable land for low income housing - a case study from South Africa South Africa patterns of inequality and reform relating to socio-economic status and access to services. Part 3 Systemic decision-making and analysis: apres moi le deluge - multicriteria problems in multiregional water resources decision-making distributed knowledge, distributed processing, distributed users - emerging methodologies and case based reasoning for decision-making using traditional environmental knowledge and GIS geographic tools for decision-making in watershed management GIS-approach for land suitability assessment in developing countries - the case study of forest development project in Mexico exploring the potential of multi-criteria spatial decision support systems - a system for sustainable land-use planning and design.

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.

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

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

To achieve sustainable development in the road sector, the use of Electric Vehicles (EVs) appears as a positive response to transport emissions. Among the available technologies, dynamic charging seems to overcome the main weakness points of EVs, even if it requires that traditional roads (t-roads) be equipped with a system providing electricity for EVs. Thus, so-called electrified roads (e-roads) must be implemented into the urban road networks. Since it is not possible to electrify all roads simultaneously, and also to consider the demand needs of citizens, a selection criterion is essential. This research describes and develops a simple, self-explanatory, repeatable, and adaptable selection criterion aimed at helping city managers in prioritizing the roads of an urban network to be upgraded from t-road to e-road status. This method belongs to the so-called Multicriteria Spatial Decision Support Systems (MC-SDSS)—processes useful for solving spatial problems through the integration of multicriteria analysis (Fuzzy Analytic Hierarchy Process, F-AHP) with a geo-referenced data management and analysis tool (GIS). The developed algorithm is based on several criteria related to the infrastructure/transport, social and environmental areas. The result of the implemented method is a Feasibility Index (FI), able to prioritize the roads most eligible to be upgraded as e-roads, as also verified by its application on the urban area of Milan (Italy).

Spatial data mining is the discovery of interesting hidden patterns and characteristics that may be implicitly in spatial databases. This paper aims to produce a descriptive model for examining the suitability in settlements by applying various machine learning techniques to figure out the knowledge discovery in spatial databases (KDSD). The study illustrates the unique hallmark that characterizes the spatial data mining by conducting the data mining algorithms. Moreover, the study presents the importance of spatial data mining and discussed multiple data sets preprocessing, classification functions, clustering and outlier detection in directions supervised learning for extracting classification rules and assessing the local amenity based on rules reliability. The classification accuracy among the three methods of the classifier algorithms (Decision Tree, Rule-Based, and Bayesian) is also compared, thereby determining the most suitable classifier by experiments performance evaluation of the training and test set.

When decentralized strategies are considered for managing wastewater, natural systems for wastewater treatment (NSWT) (constructed wetlands) seem to be strongly preferred in comparison with the conventional activated sludge wastewater treatment systems. However, because of their high land requirements, land-use suitability analysis should be conducted to specify adequate areas for their accommodation. Multicriteria spatial decision support systems have emerged as the technology that takes into account both decision criteria and constraints in complex land-use planning problems. This study presents a multiattribute decision analysis approach called the spatial fuzzy Analytic Hierarchy Process (SFAHP) to support raster-based land-use suitability studies for the implementation of NSWT. The SFAHP enables decision makers to circumvent vagueness when they perform evaluations using linguistic variables. Its application in a region of Northeastern Greece using a two-level criteria hierarchical model demonstrates ...

A GIS-based multicriteria spatial decision support system for planning urban infrastructures

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.