Adequacy and Consistency of an Intraurban Inequality Indicator Constructed through Principal Component Analysis

Libório, Matheus Pereira; Martinuci, Oséias da Silva; Machado, Alexei Manso Corrêa; Hadad, Renato Moreira; Bernardes, Patrícia; Camacho, Vitor Augusto Luizari

doi:10.1080/00330124.2021.1871766

Cited by 19 publications

(19 citation statements)

References 34 publications

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“…where GI k is the Global Index of the cluster k; Rank is the position of x ik in the ranking of indicators i ; x ik is the value of the indicator i for the cluster k ; and n is the number of indicators. Based on the Global Index GI k it is possible to externally validate the cluster's characterization from its correlation with a multidimensional phenomenon's reference indicator (Dialga & Giang 2017;Libório et al, 2021).…”

Section: Cluster Characterization and Analysis In Time-spacementioning

confidence: 99%

“…Fig. 2 Scheme for the time-space analysis of multidimensional phenomena 2021); the subjective weighting of indicators when applying the analytic hierarchy process (Libório et al, 2021); different weightings for each geographical area when applying benefit-of-doubt approach (Giambona & Vassallo, 2014); and definitions subjective values of cut-off values (Van Regenmortel et al, 2018). Although the latter is the method most used in the literature (Kneale, 2012), the approach of Alkire and Foster (2011) limits the classification to two levels and is originally designed to define levels of deprivation and poverty.…”

Section: What Is Social Exclusion?mentioning

confidence: 99%

“…Some cut-offs of monetary indicators may not make sense when currency or minimum wage change over time (Thomas & Jesse, 2012). Besides, not all-important indicators for representing the multidimensional phenomenon are relative in nature, as the average income for social exclusion (Libório et al, 2021), the concentration of substances in water for risk of environmental contamination (Chu et al, 2018), or gross domestic product for sustainable development (Davidescu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

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Composite Indicators are one-dimensional measurements that simplify the interpretation of multidimensional phenomena that facilitate public policies' elaboration. The literature on composite indicators is abundant, diversified, and inserted in practically all knowledge areas. Part of this literature aims to reduce uncertainties that propagate through the structure of the composite indicator during the process of normalization, weighting, and aggregation of indicators. Even if no composite indicator is exempt from criticism, the current literature is already sufficiently large and deep to guide researchers in constructing reliable composite indicators. However, most related works are concerned with representing multidimensional phenomena in time or space. Although some studies are interested in representing multidimensional phenomena that co-occur in time-space, the portion of the literature that addresses composite indicators is still not comprehensive, therefore leaving several open questions: What are the additional challenges in representing multidimensional phenomena in time-space? What methods can be used? Which method is most appropriate for this type of representation? What are the shortcomings of this method? How to reduce these shortcomings? This research aims at answering these questions in order to advance the time-space analysis of multidimensional phenomena. As a general contribution, the work presents a scheme of procedures that reduce subjectivities and uncertainties in the representations of multidimensional phenomena in time-space. As a specific contribution, it provides accurate and reliable information on the trajectory of social exclusion in the analyzed region. KeywordsComposite indicators • Multidimensional phenomena • Cluster analysis • Principal component analysis • Time-in-space analysis • Urban geography

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Section: Cluster Characterization and Analysis In Time-spacementioning

confidence: 99%

Section: What Is Social Exclusion?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

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“…The process of constructing composite indicators applying GRG and PCA involves the normalization of scale, weighting, and aggregation of sub-indicators. The composite indicator constructed by k-means follows another procedure where geographical units are grouped into categories considering the Euclidean distance of the n-sub-indicators (Libório et al, 2021 ; Libório et al, 2021 ).…”

Section: Operational Frameworkmentioning

confidence: 99%

“…In stage one, the most correlated sub-indicators with the most significant weights in the CI-Exposure-PCA were selected. The exclusion of poorly correlated sub-indicators is a procedure used to improve the quality of clusters (Libório et al, 2021 ; Libório et al, 2021 ). In stage two, the Average silhouette, Elbow, and Gap Statistic methods were employed to define the number of clusters (Rousseeuw, 1987 ).…”

Section: Operational Frameworkmentioning

confidence: 99%

Factors that most expose countries to COVID-19: a composite indicators-based approach

et al. 2021

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Studies carried out in different countries correlate social, economic, environmental, and health factors with the number of cases and deaths from COVID-19. However, such studies do not reveal which factors make one country more exposed to COVID-19 than other. Based on the composite indicators approach, this research identifies the factors that most impact the number of cases and deaths of COVID-19 worldwide and measures countries’ exposure to COVID-19. Three composite indicators of exposure to COVID-19 were constructed through Principal Component Analysis, Simple Additive Weighting, and k-means clustering. The number of cases and deaths from COVID-19 is strongly correlated ( R > 0.60) with composite indicator scores and moderately concordant ( K > 0.4) with country clusters. Factors directly or indirectly associated with the age of the population are the ones that most expose countries to COVID-19. The population of countries most exposed to COVID-19 is 12 years older on average. The proportion of the elderly population in these countries is at least twice that of countries less exposed to COVID-19. Factors that can increase the population’s life expectancy, such as Gross Domestic Product per capita and the Human Development Index, are four times and 1.3 times higher in more exposed countries to COVID-19. Providing better living conditions increases both the population’s life expectancy and the country’s exposure to COVID-19.

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Measuring inequality through a non-compensatory approach

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Adequacy and Consistency of an Intraurban Inequality Indicator Constructed through Principal Component Analysis

Cited by 19 publications

References 34 publications

Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

Time–Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

Factors that most expose countries to COVID-19: a composite indicators-based approach

Measuring inequality through a non-compensatory approach

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