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A new method for multispace analysis of multidimensional social exclusion.

Matheus Pereira Libório1, Hamidreza Rabiei-Dastjerdi2,3, Sandro Laudares1

  • 1Pontifical Catholic University of Minas Gerais, Belo Horizonte, 30535-012 Brazil.

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This study introduces Robust Multispace PCA, a new method for analyzing social phenomena. It reduces data loss and improves multi-geographic comparisons for social exclusion indicators.

Keywords:
Composite indicatorsMultidimensional analysisPrincipal component analysisSocial exclusionSpatial analysis

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Area of Science:

  • Social Sciences
  • Geographic Information Science
  • Statistical Analysis

Background:

  • Social phenomena are complex and geographically dependent.
  • Composite indicators, often using Principal Component Analysis (PCA), represent these phenomena but suffer from data sensitivity and limitations in multi-space-time comparisons.
  • Existing methods like PCA can lead to informational loss and hinder comparative analyses across different geographic scales.

Purpose of the Study:

  • To introduce Robust Multispace PCA, a novel method designed to overcome the limitations of traditional PCA for analyzing multidimensional social phenomena.
  • To enhance the accuracy and informativeness of composite indicators for social exclusion.
  • To facilitate more robust comparisons of social phenomena across multiple geographic spaces and time points.

Main Methods:

  • Weighted sub-indicator importance based on conceptual relevance.
  • Non-compensatory aggregation to ensure weight significance.
  • Dimension aggregation to balance weight structures.
  • A novel outlier-eliminating scale transformation function for multispatial comparison.

Main Results:

  • The Robust Multispace-PCA method significantly reduces informational loss by 1.52 times compared to traditional methods.
  • Demonstrated improved accuracy in composite indicators for social exclusion in urban areas across eight cities.
  • The new scale transformation effectively handles outliers, enabling reliable multispatial comparisons.

Conclusions:

  • Robust Multispace-PCA offers a more informative and accurate representation of multidimensional social phenomena.
  • The method's ease of use makes it suitable for researchers and policymakers.
  • It supports the development of targeted policies across multiple geographic scales, enhancing evidence-based decision-making.