This research provides an overview of the challenges in analyzing multidimensional social exclusion data using multiple indicators. It highlights the importance of composite indi- cators in simplifying the understanding of complex realities. Grounded in this literature, the research proposes a new approach to address the issues related to the multispatial and multitemporal analysis of composite indicators, contributing to the existing body of knowledge in this area. To illustrate its potential, social exclusion measures proposed by the Center for Studies and Mapping of Social Exclusion for Public Policies are used for two southern Brazil municipalities. This framework considers demographic, economic, educational, and household dimensions and fourteen variables. The proposed approach offers two significant contributions: firstly, it prevents outliers from heavily influencing the normalized sub-indicators and composite indicators during the scale transformation process. Secondly, it provides a solution compatible with the three-dimensional nature of the problem, thereby enhancing the multitemporal analysis of composite indicators.