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

Although the study of multifractal properties is now an established approach for the statistical analysis of urban data, the joint multifractal analysis of several spatial signals remains largely unexplored. The latter is crucial for understanding complex multiscale relationships in cities, such as socio-spatial segregation processes, where the evolution of behavior across geographical scales traditionally plays a central role. In this context, the proposed approach, which uses wavelet leaders for multifractal analysis of irregular point processes, estimates self-similarity and intermittency exponents as well as self-similar and multifractal cross-correlation by combining classical multifractal and geographic analysis methods.