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The methodology used to develop RIfAO and the resulting recommendation for weighting scenario choice and index revision (w.r.t. = with respect to).
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Composite Indicators (CIs, a.k.a. indices) are increasingly used as they can simplify interpretation of results by condensing the information of a plurality of underlying indicators in a single measure. This paper demonstrates that the strength of the correlations between the indicators is directly linked with their capacity to transfer information...
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... methodology used to develop RIfAO, conduct the statistical analysis with the tools from Section 2, and elaborate the resulting recommendations for weighting scenario choice and index revision is shown in Fig. 3. Step 1 refers to the normalization of the dataset with the min-max normalization. In step 2, the correlations are analysed by means of Pearson correlation coefficient ρ to study the interrelations between the indicators. The normalized indicators are then aggregated with the additive weighted sum in step 3. Step 4 studies the ...
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... 1, 2, …, n. This procedure results in a linear transformation of the data, ranging from 0 (min) to 1 (max), and is performed on all indicators to render them comparable. Table 1 gives an overview of each of the 12 indicators that are included in the RIfAO framework, and Fig. 4 shows the Pearson correlation coefficients (ρ) between them (step 2 in Fig. 3). For conciseness, the indicators are labelled according to their ID number (e.g., IND 1), as defined in Table 1, in all graphs and ...
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... equal weights are assigned to each indicator, with the modelling assumption that the trade-offs between each one included in the conceptual framework should be equal. This section explores information transfer in RIfAO at equal weights and it is performed in two steps. First, the RIfAO indicators are aggregated with equal weights (step 3 in Fig. 3) and an ex-post assessment of information transfer is performed by estimating the correlation ratios, via regression analysis, between the indicators and the index (step 4 in Fig. 3). The resulting regression fits are shown in Fig. 5, where both a linear (R 2 i ) and nonlinear (S i ) regression model are fitted to the data. Second, the ...
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... explores information transfer in RIfAO at equal weights and it is performed in two steps. First, the RIfAO indicators are aggregated with equal weights (step 3 in Fig. 3) and an ex-post assessment of information transfer is performed by estimating the correlation ratios, via regression analysis, between the indicators and the index (step 4 in Fig. 3). The resulting regression fits are shown in Fig. 5, where both a linear (R 2 i ) and nonlinear (S i ) regression model are fitted to the data. Second, the resulting correlation ratios (S i ) are then normalized and assessed in comparison to the vector of equal weights. This comparison is shown in Table ...
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... analytical analysis presented in Section 3 was adapted to RIfAO to study the effect of each indicator on the average correlations of the index (step 4 in Fig. 3). The results are presented in Fig. 6, showing how the average R 2 i , S i and Pearson correlation (ρ) perform when indicators are added incrementally one-by-one to develop RIfAO. The measures show a common trend. Nonetheless, it can be seen how notable divergence emerges between S i and Pearson correlation (ρ) when IND 6 and IND 7 are ...
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... of weighting that a decision-maker might be interested in case he/she wants to achieve a balanced information transfer or a maximized one, while the framework of indicators has to remain the same. They are contextualized as two different scenarios, Scenario A and Scenario B, with different conditions that a DM might require to be met (step 5 in Fig. ...
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... information transfer from the indicators to the CI, but with a low total information transfer. When the Maximize opt. weighting scheme is deployed, these indicators receive low or zero weights. This results in a high total information transfer, but with a large discrepancy between the individual indicators. A third scenario (Scenario C, step 5 in Fig. 3) has thus been developed, where the ...
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... In many cases, global composite metrics are often deployed to compare regions or countries based on Environmental, Social, and Governance (ESG) outlooks (Global, 2020). Few papers have emerged recently focusing on building composite resilience indicators for engineering systems such as energy systems (Lindén et al., 2021), wastewater management systems (Sun et al., 2020), and transportation infrastructure (Vajjarapu and Verma, 2021). ...
The impact of climate change and the dynamic nature of environmental conditions underscore the critical need to enhance resilience of systems and process safety considerations. The efficacy of such efforts primarily depends on how resilience is measured. Among the myriad efforts to quantify resilience, composite indicators have emerged as promising tools. However, these indicators typically employ statistical methods to derive weights for aggregation and rely on statistical homogeneity among indicators which can limit their scope and fidelity. In this study, we propose an alternative novel resilience index derived from a system’s structure and the essential conditions for safe operation during and after disruptions. The proposed measure reflects the systems’ ability to resist and respond to failures by addressing possibilities of impact propagation to other infrastructure systems. Moreover, it eliminates the need for weights and allows for compensability among its leading indicators. Using a case study based on the on-site wastewater treatment and disposal systems (OSTDS) in South Florida that faces increasing risks due to rising sea levels, we investigate the validity of the proposed index and perform a comparative analysis with statistically-driven measures. Furthermore, we demonstrate the adaptation of the proposed index for decision making within a generalized optimization framework.
... Composite indexes cluster several indicators under a condensed category which provides a synthetic measure of a complex multidimensional and meaningful phenomena in urban resilience (Asadzadeh et al., 2015). The essence of constructing a composite indicator is to condense and summarize the information contained in numerous indicators under a single indicator to accurately reflect the underlying concept (Lind en et al., 2021). The argument against composite indices includes their lack of capturing the interconnectedness of the indicators (Ampratwum et al., 2023). ...
... Tate (2012) affirms that composite indicators are overly generalized which causes some significant hazard mitigation measures to be overlooked. Indicators are interwoven and interdependent (Allen et al., 2017) where the information on an indicator is influenced by either another indictor or multiple indicators (Lind en et al., 2021). Each indicator carries a certain level of information about its functioning and behaviour (Lind en et al., 2021). ...
... Indicators are interwoven and interdependent (Allen et al., 2017) where the information on an indicator is influenced by either another indictor or multiple indicators (Lind en et al., 2021). Each indicator carries a certain level of information about its functioning and behaviour (Lind en et al., 2021). Composite indices can be constructed by using additive scales, structural equation models (Bollen, 1989) and 2-stage latent variable models (Alinovi et al., 2009). ...
Purpose - Urban communities can be faced with many destructive events that can disrupt the daily functioning of activities and livelihood of people living in the communities. In this regard, during the last couple of years, many governments have put a lot of efforts in building resilient urban communities. Essentially, a resilient urban community has the capacity to anticipate future disasters, prepare for and recover timely from adverse effects of disasters and unexpected circumstances. Considering this, it is therefore important for the need to continuously review the existing urban community resilience indicators, in order to identify emerging ones to enable comprehensive evaluation of urban communities in the future against unexpected events . This study therefore aims to conduct a systematic review to develop and critically analyse the emerging and leading urban community resilience indicators.
Methodology/approach – Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRSIMA) protocol, 53 journal articles were selected using Scopus. The selected papers were subjected to thorough content analysis.
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Findings – From the review, 45 urban community resilience indicators were identified. These indicators were grouped into eight broad categories namely, Socio-demographic, Economic, Institutional Resilience, Infrastructure and Housing Resilience, Collaboration, Community Capital, Risk Data Accumulation, and Geographical and Spatial characteristics of community. Further, the results indicated that, the U.S had the highest number of publications, followed by Australia, China, New Zealand, and Taiwan. In fact, very few studies emanated from developing economies.
Originality – The outputs of this study will inform policymakers, practitioners, and researchers on the new and emerging indicators that should be considered when evaluating the resilience level of urban communities. The findings will also serve as a theoretical foundation for further detailed empirical investigation.
... As was pointed out by [22] the purpose of constructing synthetic measure, among other things, "to condense and summarise the information contained in a number of underlying indicators, in a way that accurately reflects the underlying concept". Thus finally, the Spearman coefficient for comparison criteria with respect to information transferred for the IFSM is proposed. ...
... The criteria should capture the most important properties of the analyzed phenomena, represent them accurately and provide a large amount of information. There should be a positive correlation between the criteria and the synthetic measure, and also each criterion should contribute to the decision-maker(s)' views on its importance regarding the concept [22]. Now the measure of the information transferred from each criterion to the IFSM is defined. ...
The paper addresses the problem of complex socioeconomic phenomena assessment using questionnaire surveys. The data are represented on an ordinal scale; the object assessments may contain positive, negative, no answers, a "difficult to say" or "no opinion" answers. The general framework for Intuitionistic Fuzzy Synthetic Measure (IFSM) based on distances to the pattern object (ideal solution) is used to analyze the survey data. First, Euclidean and Hamming distances are applied in the procedure. Second, two pattern object constructions are proposed in the procedure: one based on maximum values from the survey data, and the second on maximum intuitionistic values. Third, the method for criteria comparison with the Intuitionistic Fuzzy Synthetic Measure is presented. Finally, a case study solving the problem of rank-ordering of the cities in terms of satisfaction from local public administration obtained using different variants of the proposed method is discussed. Additionally, the comparative analysis results using the Intuitionistic Fuzzy Synthetic Measure and the Intuitionistic Fuzzy TOPSIS (IFT) framework are presented.
Sustainable development is one of the most urgent challenges facing humanity. Its basic principle is to improve people’s well-being and maintain it over time. In 2015, the United Nations approved 17 sustainable development goals (SDGs) to ensure a better and sustainable future for all, balancing economic, social and environmental development. SDGs create an ‘indivisible whole’; thus, examining their interactions is crucial. Our goals were twofold: (i) to assess the implementation degree of SDGs in EU countries and (ii) to examine the interactions between goals. The potential to achieve a given SDG is approximated by a composite indicator, calculated based on an innovative method of implementing tools derived from sensitivity analysis (SA). Respecting the degree of variability of individual variables and their correlation, we set the weights to equalise their importance. Moreover, the application of SA allows us to remove strongly correlated variables that are not transferring supplementary information. We use countries’ linear ordering and Spearman’s rank correlation coefficient to assess interactions between SDGs.
Our research shows that Scandinavian countries predominately occupy the leading positions, respectively, eight times on the podium for Sweden and four by Denmark (of which three as a leader). The Netherlands also stands out, occupying the superior position in the performance of SDGs 2, 3, 5, 8, 9, 12 and 17. The top in achieving one of the most priority area – no poverty (SDG1) belongs to Czechia. Our study confirmed the results obtained by many researchers regarding the more potent synergy between SDGs compared to the trade-offs. The only trade-offs observed in our study relate to SDG15, which negatively correlated with SDG3 and 17. Furthermore, SDG7 was the only one not associated with others.
The weighting of sub-indicators is widely debated in the composite indicator literature. However, these weighting schemes’ effects on the composite indicator’s spatial dependence property are still little known. This research reveals a direct relationship between the weighting scheme of sub-indicators and the spatial autocorrelation of the composite indicator. The Global Moran’s Index (I) of composite indicators built using Data-driven (Moran’s I = 0.636) and Hybrid (Moran’s I = 0.597) weighting schemes is, on average, eleven percent higher than in the Equal-weights (Moran’s I = 0.549) and Expert opinion (Moran’s I = 0.560) weighting schemes. The average score of the composite indicator is higher when they are built by weighting schemes that better describe the spatial dependence. The spatial dependence of sub-indicators and composite indicators are not related. All fifteen sub-indicators show lower spatial autocorrelation than the composite indicators built by Expert opinion, Hybrid, and Data-driven weighting schemes. The spatial weighting matrix influences the spatial autocorrelation but does not change the robustness and quality parameters of the composite indicator. The research develops a Data-driven weighting scheme that allows individually or simultaneously considering the opinion of experts and parameters of quality and robustness of the composite indicator. It also offers the means to reduce judgment errors and evaluation biases in Expert opinion sub-indicator weighting schemes.
An overview of the "COINr" R package, which is for building and analysing composite indicators.
