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

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.