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A bivariate multifractal analysis approach to understanding socio-spatial segregation dynamics.

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Joint multifractal analysis using wavelet leaders offers a robust framework for urban data, revealing complex socio-spatial segregation patterns across scales. This method extends traditional segregation indices and identifies intermittent segregation zones.

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Area of Science:

  • Urban studies
  • Spatial statistics
  • Complex systems analysis

Background:

  • Multifractal analysis is established for urban data but joint analysis of multiple spatial signals is underexplored.
  • Understanding multiscale relationships, like socio-spatial segregation, is crucial for urban dynamics.
  • Traditional methods face limitations with spatial data complexity and scale variations.

Purpose of the Study:

  • To introduce and validate a joint multifractal analysis approach for urban spatial signals.
  • To extend classical segregation indices using a bivariate multifractal framework.
  • To analyze socio-spatial segregation and identify intermittent segregation patterns.

Main Methods:

  • Wavelet leaders multifractal analysis of irregular point processes.
  • Estimation of self-similarity and intermittency exponents.
  • Calculation of self-similar and multifractal cross-correlation.
  • Combination of multifractal and geographic analysis methods.

Main Results:

  • Local bivariate multifractal analysis correlates with classical two-group segregation indices.
  • The proposed framework is less susceptible to the modifiable areal unit problem and normalization issues.
  • The analysis reveals more pronounced evolution of segregation across spatial scales.
  • Identification of 'perturbed' areas exhibiting intermittent segregation.

Conclusions:

  • Joint multifractal analysis provides a powerful, scale-aware tool for urban socio-spatial segregation studies.
  • This approach overcomes limitations of traditional methods, offering deeper insights into urban complexity.
  • The framework effectively identifies and characterizes intermittent segregation, enhancing urban analysis capabilities.