A Decision Framework for Identifying Methods to Construct Stable Composite Indicators That Capture the Concept of Multidimensional Social Phenomena: The Case of Social Exclusion

Libório, Matheus Pereira; Diniz, Alexandre Magno Alves; Rabiei-Dastjerdi, Hamidreza; Martinuci, Oséias da Silva; Martins, Carlos Augusto Paiva da Silva; Ekel, Petr

doi:10.3390/su15076171

Cited by 5 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Min-max normalization addresses outliers by compressing data within a specific range. It also standardizes feature scales for comparability [65][66][67]. Stepwise regression was utilized to select superior predictors from a larger set of potential predictors to avoid overfitting the data and prevent misleading variable importance regression [68][69][70][71][72].…”

Section: Methodsmentioning

confidence: 99%

Investigating the Spatiotemporal Relationship between the Built Environment and COVID-19 Transmission

Huang,

Shi,

Zordan

et al. 2023

IJGI

View full text Add to dashboard Cite

Earlier studies have examined various factors that may contribute to the contagion rate of COVID-19, such as urban climatic and socioeconomic characteristics. However, there is a lack of studies at the township level detailing the spatiotemporal settings of built environment attributes, especially in the context of lockdown as a response to the global Omicron outbreak. In this study, we extended the existing literature by relating the initial-stage Omicron pandemic conditions with more comprehensive measures of the built environment, including density, diversity, design, distance to transit, and destination accessibility. The variations from the confirmed clusters of COVID-19 and asymptomatic infected cases before, during, and after the lockdown throughout the Omicron outbreak were identified geographically using GIS methods in 218 township-level divisions across Shanghai during the lockdown period. We also compared the regression results of the ordinary least-squares regression, geographically weighted regression, and geographically and temporally weighted regression. Our results show that (1) among all the built environment variables, metro line length, walking accessibility, hotel and inn density, and population exhibited positive significance in influencing pandemic prevalence; (2) spatial and temporal variations were evident in the association between accessibility, mobility, density-related built environment variables, and COVID-19 transmission across three phases: pre-lockdown, during lockdown, and post-lockdown. This study highlights the importance of targeted public health interventions in densely populated areas with high demand for public transit. It emphasizes the significance of transportation network layout and walking accessibility in controlling the spread of infectious diseases in specific urban contexts. By considering these factors, policymakers and stakeholders can foster urban resilience and effectively mitigate the impact of outbreaks, aligning with the objectives of the 2030 UN Sustainable Development Goals.

show abstract

Section: Methodsmentioning

confidence: 99%

Investigating the Spatiotemporal Relationship between the Built Environment and COVID-19 Transmission

Huang,

Shi,

Zordan

et al. 2023

IJGI

View full text Add to dashboard Cite

show abstract

“…Among these characteristics, the multidimensional nature of social phenomena creates several operational challenges for their representation (Libório et al, 2023 ). These particular challenges have been widely explored in the literature on composite indicators through studies such as that by Nardo et al ( 2005 ), Munda ( 2012 ), Becker et al ( 2017 ), and Kuc-Czarnecka et al ( 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A new method for multispace analysis of multidimensional social exclusion

et al. 2023

Self Cite

View full text Add to dashboard Cite

Social phenomena are multidimensional and dependent on geographic space. Numerous methods are capable of representing multidimensional social phenomena through a composite indicator. Among these methods, principal component analysis (PCA) is the most used when considering the geographical perspective. However, the composite indicators built by the method are sensitive to outliers and dependent on the input data, implying informational loss and specific eigenvectors that make multi-space–time comparisons impossible. This research proposes a new method to overcome these problems: the Robust Multispace PCA. The method incorporates the following innovations. The sub-indicators are weighted according to their conceptual importance in the multidimensional phenomenon. The non-compensatory aggregation of these sub-indicators guarantees the function of the weights as of relative importance. Aggregating indicators in dimensions balances the weight structure of dimensions in the composite indicator. A new scale transformation function that eliminates outliers and allows multispatial comparison reduces by 1.52 times the informational loss of the composite indicator of social exclusion in eight cities' urban areas. The Robust Multispace-PCA has a high potential for appropriation by researchers and policymakers, as it is easy to follow, offers more informative and accurate representations of multidimensional social phenomena, and favors the development of policies at multiple geographic scales.

show abstract

Beyond the richter scale: a fuzzy inference system approach for measuring objective earthquake risk

Mohammadi,

Balouei,

Amini

et al. 2024

Nat Hazards

View full text Add to dashboard Cite

Earthquakes pose significant natural hazards and impact populations worldwide. Iran is among the most susceptible countries to seismic activity, making comprehensive earthquake risk assessment crucial. This study employs geospatial methods, including integrating satellite, ground-based, and auxiliary data to model earthquake risk across this country. A Fuzzy Inference System (FIS) is used to generate earthquake hazard probability and vulnerability layers, considering factors such as slope, elevation, fault density, building density, proximity to main roads, proximity to buildings, population density, and earthquake epicenter, magnitude, proximity to the epicenter, depth density, peak ground acceleration (PGA). The results highlight high-risk areas in the Alborz and Zagros Mountain ranges and coastal regions. Moreover, the findings indicate that 39.7% (approximately 31.7 million people) of Iran’s population resides in high-risk zones, with 9.6% (approximately 7.7 million) located in coastal areas vulnerable to earthquakes. These findings offer valuable insights for crisis management and urban planning initiatives.

show abstract

A Decision Framework for Identifying Methods to Construct Stable Composite Indicators That Capture the Concept of Multidimensional Social Phenomena: The Case of Social Exclusion

Cited by 5 publications

References 37 publications

Investigating the Spatiotemporal Relationship between the Built Environment and COVID-19 Transmission

Investigating the Spatiotemporal Relationship between the Built Environment and COVID-19 Transmission

A new method for multispace analysis of multidimensional social exclusion

Beyond the richter scale: a fuzzy inference system approach for measuring objective earthquake risk

Contact Info

Product

Resources

About