Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. 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.
Results show that a local bivariate multifractal analysis can not only be related to classical two-group segregation indices but also extends them to provide a robust analytical framework that 1 is less susceptible to the modifiable areal unit problem and normalization methods and that 2 can reveal more pronounced evolution across spatial scales.
In its broadest sense, residential segregation refers to the degree to which various population groups inhabit or encounter distinct social surroundings 1. Because it is one of the most fundamental processes in human geography, consistent and correct measurement of segregation is critical 2 ; most experts agree that spatial segregation is a complex attribute of an urban system, difficult to capture with a single index 3. There are, therefore, numerous classical and well-established measures; for a comprehensive overview, see, e.
