Decision Analysis Research Articles

A novel ANN-CA and MCDA integrated framework for predicting urban expansion and its implications on future flood risk, Accra Metropolis

Urban development in African countries significantly impacts environmental sustainability and city resilience, particularly in flood risk management. The Accra Metropolis, in particular, faces increasingly prevalent annual floods that disproportionately affect the urban poor living in informal settlements, resulting in significant loss of life and disruption of livelihoods. This study developed a GIS-based flood risk mapping framework that integrates artificial neural network (ANN) and cellular automata (CA) modelling with multi-criteria decision analysis. This hybrid approach was employed to predict flood scenarios for the Metropolis in 2032 and 2042. The framework incorporated six hydro-geomorphological indicators influencing extreme events: LULC, slope, elevation, drainage density, soil type, and proximity to rivers. The study analysed LULC projections, revealing a trend of substantial urban expansion with an estimated 10.9% increase in built-up areas within the next 20 years. This growth is expected to significantly heighten future flood risk in both the central upstream and downstream portions of the Metropolis, particularly in low-lying informal settlements close to the Odaw River and Korle Lagoon. Model performance was validated by ROC curve analysis, with AUC values of 0.927 and 0.922 for 2032 and 2042, respectively, which resonates with the records of previous flood distribution studies of the area. This study highlights the crucial need for sustainable urban development, improved infrastructure, and proactive flood mitigation measures in Accra to assist urban planners, policymakers, and stakeholders in managing flood risks effectively.

Learning mobility in European higher education: How has the Union’s flagship initiative progressed?

AbstractIn 2010, the European Commission set out the development of an economy based on knowledge and innovation as one of the priorities of its Europe 2020 strategy for smart, sustainable, and inclusive growth. This culminated in the ‘Youth on the Move’ flagship initiative, aimed at enhancing the performance and international attractiveness of Europe’s higher education institutions and raising the Union’s overall education and training levels. Therefore, it is relevant to assess the performance of the ‘Youth on the Move’ initiative via the creation of composite indicators (CIs) and, ultimately, monitor the progress made by European countries in creating a positive environment supporting learner mobility. For this reason, we make use of the CI-building ‘Benefit-of-the-Doubt’ approach, in its robust and conditional setting to account for outliers and the human development of those nations, to exploit the European Commission’s Mobility Scoreboard framework between 2015/2016 and 2022/2023. Furthermore, we incorporate the value judgements of experts in the sector to construct utility scales and compute weight restrictions through multi-criteria decision analysis. This enables the conversion of ordinal scales into interval ones based on knowledgeable information about reality in higher education. In the end, the results point to a slight performance improvement, but highlight the need to improve the ‘Recognition of learning outcomes’, ‘Foreign language preparation’, and ‘Information and guidance’.

Using exploratory modeling to challenge narratives of risk governance in Mexico City.

Achieving more sustainable adaptation to social-environmental change demands the transformation of the narratives that provide the rationale for risk governance. These narratives often reflect long-standing beliefs about social and political relationships, ascribe actions and responsibilities, and specify solutions to risk. When such solutions are implemented through material investments in landscapes, these narratives become embedded in physical infrastructure with long legacies. Dominant narratives can mask a range of divergent problem framings. By masking alternatives, narratives can contribute to the persistence of unsustainable governance trajectories. Decision-support tools have begun to represent narratives as drivers of system dynamics; making narratives visible can reveal opportunities for more sustainable governance. We present the results of the project "The Dynamics of Multi-Scalar Adaptation in the Megalopolis", a dynamic, exploratory model of socio-hydrological risks in Mexico City that was designed to both endogenize and simultaneously challenge the dominant narratives that characterize water-risk governance in the city. Qualitative data characterize dominant narratives at city and borough scales. An agent-based model, informed by multicriteria decision analysis and coupled with hydrological, urbanization, and climatic model inputs, permitted the development of exploratory governance scenarios designed to challenge dominant narratives. Scenarios revealed how dominant narratives may contribute to the persistence of vulnerability "hotspots" in the city, despite stated goals of equity and vulnerability alleviation. Participatory workshops with representatives of the city government illustrate how making such narratives visible through exploratory modeling can lead to a questioning of prior assumptions and causal relations, recognition of a need for intersectoral collaboration, and insights into potential management strategies.

A navigational risk evaluation of ferry transport: Continuous risk management matrix based on fuzzy Best-Worst Method.

Ferry transport has witnessed numerous fatal accidents due to unsafe navigation; thus, it is of paramount importance to mitigate risks and enhance safety measures in ferry navigation. This paper aims to evaluate the navigational risk of ferry transport by a continuous risk management matrix (CRMM) based on the fuzzy Best-Worst Method (BMW). Its originalities include developing CRMM to figure out the risk level of risk factors (RFs) for ferry transport and adopting fuzzy BWM to estimate the probability and severity weights vector of RFs. Empirical results show that twenty RFs for ferry navigation are divided into four zones corresponding to their risk values, including extreme-risk, high-risk, medium-risk, and low-risk areas. Particularly, results identify three extreme-risk RFs: inadequate evacuation and emergency response features, marine traffic congestion, and insufficient training on navigational regulations. The proposed research model can provide a methodological reference to the pertinent studies regarding risk management and multiple-criteria decision analysis (MCDA).

Selection of data analytic techniques by using fuzzy AHP TOPSIS from a healthcare perspective

The healthcare industry has been put to test the need to manage enormous amounts of data provided by various sources, which are renowned for providing enormous quantities of heterogeneous information. The data are collected and analyzed with different Data Analytic (DA) and machine learning algorithm approaches. Researchers, scientists, and industrialists must manage or select the best approach associated with DA in healthcare. This scientific study is based on decision analysis between the DA factors and alternatives. The information affects the whole system in a rational manner. This information is very important in healthcare sector for appropriate prediction and analysis. The evaluation discusses its benefits and presents an analytic framework. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is used to address the weight of the factors. The Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) address the rank of the data analytic alternatives used in healthcare sector. The models used in the article briefly discuss the challenges of DA and approaches to address those challenges. The assorted factors of DA are capture, cleaning, storage, security, stewardship, reporting, visualization, updating, sharing, and querying. The DA alternatives include descriptive, diagnostic, predictive, prescriptive, discovery, regression, cohort and inferential analyses. The most influential factors of the DA and the most suitable approach for the DA are evaluated. The ‘cleaning’ factor has the highest weight, and ‘updating’ is achieved at least by the Fuzzy-AHP approach. The regression approach of data analysis had the highest rank, and the diagnostic analysis had the lowest rank. Decision analyses are necessary for data scientists and medical providers to predict diseases appropriately in the healthcare domain. This analysis also revealed the cost benefits to hospitals.

Network Analysis of Two-Stage Customer Decisions with Preference-Guided Market Segmentation

Abstract Network-based analyses have shown their effectiveness in understanding customer preferences through interactions and relationships between customers and products, particularly for tailored product design. There is limited research on applying this analysis to diverse customers with varied preferences. This paper introduces a market-segmented network modeling approach, guided by customer preference, to explore heterogeneity in customers' two-stage decision-making process: consideration-then-choice. In heterogeneous markets, like household appliances, even customers with similar characteristics or purchasing similar products can exhibit different decision-making processes. Therefore, In this method, we segment customers into groups based on preferences rather than just characteristics, allowing for more accurate choice modeling. Using Joint Correspondence Analysis, we identify associations between customer attributes and preferred products, characterizing market segments through clustering. We then build individual bipartite customer-product networks and apply the Exponential Random Graph Model to compare the product features influencing customer considerations and choices in various market segments. Using a US household vacuum cleaner survey, our method detected different customer preferences for the same product attribute at different decision-making stages. The market-segmentation model outperforms the non-segmented benchmark in prediction, highlighting its accuracy in predicting varied customer behaviors. This study underscores the vital role of preference-guided segmentation in product design, illustrating how a deeper understanding of customer preferences at different decision stages can inform and refine design strategies, ensuring products are more closely aligned with the dynamic needs and tastes of diverse markets.

What Constitutes High Risk for Venous Thromboembolism? Comparing Approaches to Determining an Appropriate Threshold.

Guidelines recommend pharmacological venous thromboembolism (VTE) prophylaxis only for high-risk patients, but the probability of VTE considered "high-risk" is not specified. Our objective was to define an appropriate probability threshold (or range) for VTE risk stratification and corresponding prophylaxis in medical inpatients. Patients were adults admitted to any of 10 Cleveland Clinic Health System hospitals between December 2020 and August 2021 (N = 41,036). Hospital medicine physicians and internal medicine residents from included hospitals were surveyed between June and November 2023 (N = 214). We compared five approaches to determining a threshold: decision analysis, maximizing the sensitivity and specificity of a logistic regression model, deriving a probability from a point-based model, surveying physicians' understanding of VTE risk, and deriving a probability from physician behavior. For each approach, we determined the probability threshold above which a patient would be considered high-risk for VTE. We applied each threshold to the Cleveland Clinic VTE risk assessment model (CCM) and calculated the percentage of the 41,036 patients in our cohort who would be considered eligible for prophylaxis due to their high-risk status. We compared these hypothetical prophylaxis rates with physicians' observed prophylaxis rates. The different approaches yielded thresholds ranging from 0.3% to 5.4%, corresponding inversely with hypothetical prophylaxis rates of 0.2% to 75%. Multiple thresholds clustered between 0.52% to 0.55%, suggesting an average hypothetical prophylaxis rate of approximately 30%, whereas physicians' observed prophylaxis rates ranged from 48% to 76%. Multiple approaches to determining a probability threshold for VTE prophylaxis converged to suggest an optimal threshold of approximately 0.5%. Other approaches yielded extreme thresholds that are unrealistic for clinical practice. Physicians prescribed prophylaxis much more frequently than the suggested rate of 30%, indicating opportunity to reduce unnecessary prophylaxis. To aid in these efforts, guidelines should explicitly quantify high-risk.

SITW Method: A New Approach to Re-identifying Multi-criteria Weights in Complex Decision Analysis

Multi-Criteria Decision Analysis (MCDA) addresses complex decision-making problems across various fields such as logistics, management, medicine, and sustainability. MCDA tools provide a structured approach to evaluating decisions with multiple conflicting criteria, assisting decision-makers in navigating intricate scenarios. Engaging experts is crucial for identifying multi-criteria models due to the diverse aspects of decision-making problems. Techniques such as pairwise comparisons and criterion weight assignment are commonly used to incorporate expert knowledge into decision models. Criterion weight assignment allows experts to indicate the importance of each criterion; however, issues can arise if model parameters are lost or experts become unavailable. To mitigate these issues, techniques like entropy or standard deviation can determine weights without direct expert input. In this context, the Stochastic Identification of Weights (SITW) method utilizes existing assessment samples to re-identify models and obtain weights that replicate the rankings of a reference model. This study compares information-based methods (Entropy, STD) with the SITW method in re-identifying the TRI medical function as a benchmark. The effectiveness of these methods is evaluated using Spearman's weighted correlation coefficient across various scenarios and alternative numbers. Results indicate that the SITW method provides more significant results than other methods in identifying multi-criteria weights by leveraging previously evaluated alternatives. Future research could explore broader approaches and uncertainty scenarios to ensure comprehensive decision support in complex contexts.

Іnternational and foreign practice of compensation for war crimes

The scientific article is dedicated to the study of international and foreign practices of compensation for war crimes. The article analyzes legal mechanisms and tools used for compensating victims of war crimes in various countries of Europe and the world. The main focus is on the practice of international courts, such as the International Criminal Court (ICC), as well as national judicial institutions that handle war crime cases. Specific cases and decisions that have influenced the development of law enforcement practices in this area are examined. One of the key aspects of the work is the analysis of international treaties and conventions that regulate compensation issues, including the Convention on the Non-Applicability of Statutory Limitations to War Crimes and Crimes Against Humanity, as well as the Rome Statute of the ICC. Various compensation models are considered, including monetary compensation, rehabilitation, and other forms of reparation. The article also explores the role of non-governmental organizations and international funds in the process of compensating war crime victims. The effectiveness of different approaches and mechanisms used to ensure justice and restore the rights of victims is analyzed. The work pays attention to the problems and challenges faced by law enforcement agencies when handling war crime cases, as well as the prospects for improving the international legal mechanism for protecting the rights of victims. The research is based on the analysis of specific cases, court decisions, and legal acts, allowing conclusions to be drawn about the current state and possible ways of developing the compensation system for war crimes. Thus, the article contributes to the scientific discussion on improving the mechanisms of legal protection for victims of war crimes and ensuring justice in international law.

389. WILL SYNCHRONOUS ESOPHAGEAL AND LUNG RESECTION INCREASE THE INCIDENCE OF ANASTOMOTIC LEAKS? A MULTICENTER RETROSPECTIVE STUDY

Abstract Background Reports on combined resection for synchronous lung lesions and esophageal cancer (CRLE) cases are rare and mostly individual cases. Furthermore, the feasibility of CRLE has always been a controversial topic. In the current study, we retrospectively analyzed the feasibility of CRLE and established an individualized prediction model for esophageal anastomotic leaks after CRLE by performing a multicenter retrospective study. Methods Patients who underwent esophagectomy between January 2009 and June 2021 were extracted from a 4-centre retrospectively maintained database, and those with CRLE at the same setting were matched in a 1:2 propensity score matched (PSM) ratio to esophagectomy alone (EA) patients. A nomogram was then established based on the variables involved in multivariate logistic regression analysis. Internal validation of the nomogram was conducted utilizing Bootstrap resampling. Decision and clinical impact curve analysis were computed to assess the practical clinical utility of the nomogram. A prognosis analysis for CRLE and EA patients by Kaplan-Meier curves was conducted. Results Of the 7152 esophagectomies, 216 cases of CRLE were eligible, and 1:2 ratio PSM EA patients were matched. The incidence of anastomotic leaks following CRLE increased significantly (P=0.035). The results of the multivariate analysis indicated the leaks varied according to the type of lung resection (anatomic > wedge resection, P=0.016) and site of resected lobe (upper > middle/low lobe; P=0.027), and a nomogram was established to predict the occurrence of leaks accurately (AUC=0.786). Although no statistically significant difference in overall survival (OS) was observed in the CRLE group (P=0.070), a trend toward lower survival rates was noted. Further analysis revealed that combined upper lobe anatomic resection was significantly associated with reduced OS (P=0.027). Conclusion Our study confirms that CRLE is feasible but comes with a significantly increased risk of anastomotic leaks and a concerning trend of reduced survival, particularly when upper lobe anatomic resections are performed. These findings highlight the need for careful patient selection and surgical planning when considering CRLE.

Spatial multi-criteria approaches for estimating geogenic radon hazard index

The geogenic radon hazard index (GRHI) map plays a crucial role in evaluating radon exposure risks. The construction of this map requires a comprehensive analysis of radon levels in soil gas and some critical factors, such as uranium content in bedrock, soil permeability, and geological inhomogeneities. In this context, the spatial multi-criteria decision analysis is proposed to integrate the GRHI-based criteria for identifying the high-potential radon areas. In particular, the multivariate integration involves the fuzzy gamma operator and a hybrid multi-criteria decision-making technique, namely AHP-TOPSIS, which represents a novel approach in GRHI mapping. Thus, a comparison is provided through the definition of the GRHI map of an unexplored study area, that is the Apulia region, located in Southern Italy. In order to evaluate the output maps, high radon potential areas are identified based on some available indoor radon measurement data. The success-rate curve, as a valid evaluation metric, is employed for the performance assessment and comparison of these two methods. The results demonstrate that although both generated GRHI maps are closely correlated with high-potential radon zones in Apulia, the hybrid AHP-TOPSIS method is preferable in identifying areas with elevated radon potential.

Flood risk decomposed: optimized machine learning hazard mapping and multi-criteria vulnerability analysis in the city of Zaio, Morocco.

Urban flood risk mapping has become crucial for effective mitigation and urban planning. This study assesses and maps flood risk in the city of Zaio, Morocco, using machine learning and Multi-Criteria Decision Analysis (MCDA) techniques to overcome data scarcity challenges. We employed the Random Forest (RF) model with nine flood conditioning factors for flood hazard and the Analytical Hierarchy Process (AHP) for vulnerability assessment. To enhance RF model performance, we compared three hyperparameter tuning techniques: Bayesian Optimization (RF-BO), Genetic Algorithm (RF-GA), and Grid Search (RF-GS). RF-BO demonstrated superior accuracy in flood hazard modelling. Flood vulnerability was assessed using AHP, incorporating social and physical factors. The final flood risk map was produced by combining the RF-BO hazard model with the AHP vulnerability assessment. Results indicate that flood hazard in Zaio is significantly affected by factors such as topography and distance to rivers. Moreover, areas with high population density closer to rivers, especially in the south-western residential area, were found to be more exposed to flood risk. The findings highlight the utility of ML models, MCDA, and hyperparameter optimization in urban flood risk mapping, enabling the identification of high-risk urban areas that should be prioritized for flood protection efforts. This approach proves especially valuable in ungauged regions with limited hydrological data.

Into the Jungle of Biological Agents of Foodborne Diseases: Time to Put Some Order for the French Risk Manager.

Consumers can be exposed to many foodborne biological hazards that cause diseases with varying outcomes and incidence and, therefore, represent different levels of public health burden. To help the French risk managers to rank these hazards and to prioritize food safety actions, we have developed a three-step approach. The first step was to develop a list of foodborne hazards of health concern in mainland France. From an initial list of 335 human pathogenic biological agents, the final list of "retained hazards" consists of 24 hazards, including 12 bacteria (including bacterial toxins and metabolites), 3 viruses and 9 parasites. The second step was to collect data to estimate the disease burden (incidence, Disability Adjusted Life Years) associated with these hazards through food during two time periods: 2008-2013 and 2014-2019. The ranks of the different hazards changed slightly according to the considered period. The third step was the ranking of hazards according to a multicriteria decision support model using the ELECTRE III method. Three ranking criteria were used, where two reflect the severity of the effects (Years of life lost and Years lost due to disability) and one reflects the likelihood (incidence) of the disease. The multicriteria decision analysis approach takes into account the preferences of the risk managers through different sets of weights and the uncertainties associated with the data. The method and the data collected allowed to estimate the health burden of foodborne biological hazards in mainland France and to define a prioritization list for the health authorities.

Multi-criteria Decision Making Methods and sustainable Appliations in the Digital Age

Abstract: Designing a sustainable electrification system for remote places in developing countries is very complex while considering various factors such as technical, economic, environmental and social (TEES). Development of an efficient, rational, coherent and a translucent framework is required to address issues related to the requirement of energy needs based on the end user. The main goal of this research is to design a methodological framework based on decision analysis and optimized model with locally available energy sources. The new optimal renewable energy options are required to be used to minimize the fossil fuel dependency of electrical generation. Initially, suitable energy alternatives are selected by considering different criterion with multiple scenarios for decision analysis. The further optimal combination of energy sources evaluated for different possibilities. The new methodology of hybrid renewable optimization is applied to a rural place, Himachal Pradesh to illustrate its effectiveness based on metrological data

Architecture Design Space Generation via Decision Pattern-Guided Department of Defense Architecture Framework Modeling

The importance of architecture design keeps increasing as the complexity of systems and system-of-systems (SoSs) continues rising. While the architecture frameworks such as the Department of Defense Architecture Framework (DoDAF) are commonly used to guide architecture design, many perspectives are still hindering their effective use. Instead of generating a set of architecture description models probably only for satisfying the milestone review, the architecture frameworks should be used to organize the vague or incomplete information, identify and formulate the decision-making problem, and guide the architecture decision-making. Unfortunately, the decision points are hidden in the architecture models and the ambiguity often leads to a confusion of whether the architecture models are built incorrectly due to the lack of modeling experience or the lack of adequate decision analysis. Therefore, this paper identifies the key decision points and decision types during the architecture model development based on the DoDAF. Plus, this paper proposes a set of decision patterns and a guide to their use to provide qualitative decision analysis for developing architecture models and generating alternatives. An illustrative example to anti-submarine SoSs demonstrates the process of applying the decision patterns to the DoDAF model’s development and the generated architecture alternatives.

ADMINISTRATIVE AND LEGAL GUIDELINES FOR STANDARDIZATION OF FINANCIAL REPORTING OF ENTERPRISES AS SOCIO-ECONOMIC SYSTEMS

This article aims to present an approach for determining the most effective administrative and legal guidelines for the standardization of financial reporting in modern open socio-economic systems. It emphasizes the current state of financial reporting of these open socio-economic systems at the time of analysis and the application of the proposed approach. As the most common form of open socio-economic systems today, enterprises are taken as the basis. The method of multi-criteria decision analysis (MCDA), which encompasses various approaches to decision-making based on multiple criteria, including hierarchical analysis, was applied. In addition to its comprehensiveness, the method of pairwise comparison, which involves the use of two elements for comparison, was also applied. Experts were involved in the assessment and determination of the administrative and legal guidelines. As a result, an approach to forming key administrative and legal guidelines for the standardization of financial reporting was developed, which, unlike other similar approaches, aims to take into account external environmental factors and the changes they bring in order to try to adjust financial reporting standardization policies in complex development conditions. The practical value is revealed in the possibility of application by state bodies and structures involved in the development and establishment of financial reporting standards in Ukraine.

Re-imagining the Factors Influencing the Choice of Project Management Tools and Techniques in Tanzanian Infrastructure Projects: Elicitation of Practitioners’ Perspectives

Construction project Management tools and Techniques (CPMTT) are vital for the success of construction projects. Nonetheless, there is a paucity of studies that examined the factors influencing the choice of CPMTT in developing countries, particularly, Tanzania. The objective of the study was to analyze the factors influencing the choice of CPMTT in Tanzanian public infrastructure projects, with the aim to fill the knowledge gap. The study was descriptive in nature and employed a mixed method approach. Using purposive and snowball sampling techniques, data was collected from 60 out of 70 Project management practitioners including Consultants and project Coordinators within Dar-es-salaam Metropolitan Development Projects (DMDP) and similar infrastructure projects. Fifty valid responses were received equivalent to 83% response rate and interviews were done. Quantitative data from respondents was analyzed using IBM SPSS v25. The directed content analysis method was employed for qualitative data from 10 Resident Engineers to supplement the quantitative results. Using factor analysis, four groups of organization support, ease of access and usefulness of tools, fulfilment of project objectives, and project environment and idea generation were determined to influence the choice of CPMTT. Project managers frequently choose tools that are easily accessible without considering the organization's project objectives or resource commitment. The findings could be used as a guide for project practitioners to critically assess the areas of weakness and select CPMTT that can help address those weaknesses and hence improve project delivery in Tanzania, an unexplored context. Project managers are called to re-imagine project needs based on priority and select the CPMTT that satisfies the needs based on experience, skills and project objectives as crucial factors in the selection of CPMTT rather than using their discretion. This would improve the delivery of infrastructure projects and, hence, performance outcomes in the construction sector in Tanzania and other emerging countries. This study was limited to DMDP public infrastructure projects in Tanzania. Keywords: Construction Project Management Tools and Techniques (CPMTT), Decision analysis, mixed-methods, Tanzania

Public deliberation on health gain measures

Abstract Researchers and decision-makers use health gain measures to assess the value of health interventions. However, our current understanding of how these measures are understandable and accessible to the community is limited. This study examined a diverse group of stakeholders’ attitudes and preferences for 9 commonly used health gain measures. We recruited 20 stakeholders, including patients, caregivers, pharmacists, allied health professionals, and citizens. We conducted 2 in-person deliberative meetings in which participants learned, discussed, deliberated on, and ranked 9 health gain measures. The final ranking conducted after unified deliberation showed the quality-adjusted life year (QALY) as the top-ranked measure, followed by the clinical benefit rating method used by the U.S. Preventive Services Task Force, and multicriteria decision analysis (MCDA). We identified 3 themes during deliberations: the importance of using patient values in population-based health gain measures, examining complementary measures together, and choosing measures that are intuitive and easy to understand. Future policymaking should consider incorporating the QALY, clinical benefit rating, and MCDA into prioritization decisions.

Review on Potential Urban Development Site Selection Using Geospatial-Based Multi-Criteria Decision Analysis (MCDA) Techniques in the Context of Ethiopia

This review paper provides a comprehensive assessment of the opportunities and challenges in leveraging geospatial-based technologies, particularly Multi-Criteria Decision Analysis (MCDA) techniques, to enhance the effectiveness of urban development site selection in Ethiopia. The overarching findings highlight several critical focus areas for improvement. Firstly, the review emphasizes the need to strengthen the availability, quality, and accessibility of geospatial data through the development of robust data infrastructures and innovative data collection methods. Secondly, it recommends adopting a more inclusive and collaborative approach to stakeholder engagement, coupled with targeted capacity-building initiatives to enhance the technical skills and knowledge of urban planners, GIS experts, and local stakeholders. Thirdly, the paper suggests incorporating advanced spatial modeling and simulation techniques to capture the dynamic nature of urban growth patterns, as well as integrating climate change impact assessments and disaster risk analyses into the decision-making framework. Fourthly, it emphasizes the importance of strengthening institutional and organizational frameworks to promote cross-sectoral collaboration, data sharing, and integrated decision-making processes. Finally, the review underscores the significance of designing user-friendly and customizable Spatial Decision Support Systems (SDSS) that cater to the specific needs and constraints of the Ethiopian urban development context. The review concludes that the use of geospatial-based MCDA approaches holds immense potential in addressing Ethiopia's complex urban development challenges, as it enables a comprehensive evaluation of suitability factors and the exploration of alternative urban development strategies.

Ecosystem service values support conservation and sustainable land development: Perspectives from four University of California campuses

Urban landscapes homogenize our world at global scales, contributing to “extinction of experience”, a progressive decline in human interactions with native greenspace that can disconnect people from the services it provides. College age adults report feeling disconnected from nature more than other demographics, making universities a logical place to explore interventions intended to restore a connection with nature. This study surveyed 1088 students and staff across four university campus communities in Southern California, USA and used multicriteria decision analysis to explore their landscape preferences and the implications of those preferences for combatting extinction of experience. Our results suggest that perspectives of, and preferences for, different greenspace forms vary significantly (i.e., they are not perceived as substitutable). Support for native ecosystems, particularly coastal sage scrub (top ranked landscape) was generally high, suggesting that disaffection with wild nature is not particularly widespread. Programs for replacing turf grass lawns (lowest ranked landscape) with native plants were also well supported, but support for stormwater bioswales was more moderate (and variable). This may reflect their relative newness, both on university campuses and in urban spaces more generally. Not all members of campus communities preferred the same landscapes; preferences differed with degree of pro-environmentalism and university status (undergraduate student, graduate student, staff). Even so, all respondents exhibited landscape preferences consistent with at least one approach for combatting extinction of experience, suggesting that ecologists, engineers and urban planners have a viable set of generalizable tools for reconnecting people with nature.