Harnessing Spatial Heterogeneity in Composite Indicators through the Ordered Geographically Weighted Averaging (<scp>OGWA</scp>) Operator

Fusco, Elisa; Libório, Matheus Pereira; Rabiei‐Dastjerdi, Hamidreza; Vidoli, Francesco; Brunsdon, Chris; Ekel, Petr Iakovlevitch

doi:10.1111/gean.12384

Cited by 7 publications

(2 citation statements)

References 74 publications

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“…The appropriation of the system by researchers in these areas favors the understanding and elaboration of public policies that allow for reductions in social vulnerabilities [84], the equitable distribution of public infrastructure in cities [85], improvements in public healthcare services [86], making better decisions in custody hearings [87], promoting the digital maturation of companies, achieving opportunities and avoiding market threats [88], and improving accessibility to healthcare facilities [89], among others.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

Ferreira,

Ribeiro,

Schuffner

et al. 2024

Axioms

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This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts’ knowledge are considered. Human preferences may be represented within the fuzzy preference relations and by applying diverse other preference formats. Considering this, so-called transformation functions reduce any preference format to fuzzy preference relations. This paper’s results can be applied independently or as part of a general approach to solving a wide class of problems with fuzzy coefficients, as well as within the framework of a general scheme of multi-criteria decision-making under conditions of uncertainty. The considered techniques for fuzzy preference modeling are directed at assessing, comparing, choosing, prioritizing, and/or ordering alternatives. These techniques have served to develop a computing system for multi-attribute decision-making. It has been implemented in the C# programming language, utilizing the “.NET” framework. The computing system allows one to represent decision-makers’ preferences in one of five preference formats. These formats and quantitative estimates are reduced to nonreciprocal fuzzy preference relations, providing homogeneous preference information for decision procedures. This paper’s results have a general character and were applied to analyze power engineering problems.

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Section: Discussionmentioning

confidence: 99%

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

Ferreira,

Ribeiro,

Schuffner

et al. 2024

Axioms

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“…The range of applications of composite indicators is broad, offering solutions to multidimensional nature problems in diverse knowledge fields. These include sustainability [4], community resilience [5], environment [6], healthcare [7], quality of work [8], tourism [9], transport [10], education [11], spatial inequality [12], pandemic impact assessment [13], and vulnerability to food consumption [14], among many others [15,16].…”

Section: Introductionmentioning

confidence: 99%

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

Libório,

Karagiannis,

Diniz

et al. 2024

Entropy

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This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts’ uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon’s theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power.

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Towards designing a comprehensive composite index for social vulnerability to natural hazards in the big data era: potential challenges and partial solutions

Rabiei-Dastjerdi,

Brereton,

O’Neill

2024

Nat Hazards

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This study provides a practical definition and framework to measure social vulnerability to natural hazards, addressing gaps in the literature after three decades of Susan Cutter's Place-Based Model. The current social vulnerability index, designed based on available data such as census data, is limited in capturing all aspects of social vulnerability and spatial inequalities. This research explored and proposed a new theoretical perspective and methodological framework for designing a comprehensive index for social vulnerability to natural disasters using emerging big data, which is practical and feasible and can be applied to social vulnerability studies in general and social vulnerability to natural hazards in particular. This research first defines constructs of social vulnerability to natural hazards, including (1) socioeconomic status or conditions, (2) physical infrastructure or accessibility to facilities and services, (3) ecological-environmental conditions, (4) access to security facilities and crime rate, (5) technological inequalities, (6) health conditions of the citizens, and (7) susceptibility. Then, it proposes potential solutions for developing a comprehensive composite index under ideal conditions without (big) data limitations, using the US and the UK as case examples. These solutions are not just theoretical but also practical and feasible, instilling confidence in their implementation. This research offers valuable insights for researchers and policymakers in diverse sectors, supporting the design of effective disaster risk reduction strategies and intervention programs.

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Harnessing Spatial Heterogeneity in Composite Indicators through the Ordered Geographically Weighted Averaging (OGWA) Operator

Cited by 7 publications

References 74 publications

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications

The Use of Information Entropy and Expert Opinion in Maximizing the Discriminating Power of Composite Indicators

Towards designing a comprehensive composite index for social vulnerability to natural hazards in the big data era: potential challenges and partial solutions

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