Composite Indicators are one-dimensional measurements that simplify the interpretation of multidimensional phenomena that facilitate public policies' elaboration. The literature on composite indicators is abundant, diversified, and inserted in practically all knowledge areas. Part of this literature aims to reduce uncertainties that propagate through the structure of the composite indicator during the process of normalization, weighting, and aggregation of indicators. Even if no composite indicator is exempt from criticism, the current literature is already sufficiently large and deep to guide researchers in constructing reliable composite indicators. However, most related works are concerned with representing multidimensional phenomena in time or space. Although some studies are interested in representing multidimensional phenomena that co-occur in time–space, the portion of the literature that addresses composite indicators is still not comprehensive, therefore leaving several open questions: What are the additional challenges in representing multidimensional phenomena in time–space? What methods can be used? Which method is most appropriate for this type of representation? What are the shortcomings of this method? How to reduce these shortcomings? This research aims at answering these questions in order to advance the time–space analysis of multidimensional phenomena. As a general contribution, the work presents a scheme of procedures that reduce subjectivities and uncertainties in the representations of multidimensional phenomena in time–space. As a specific contribution, it provides accurate and reliable information on the trajectory of social exclusion in the analyzed region.

综合指标是一维测量，可简化多维现象的解释，促进公共政策的制定。关于复合指标的文献丰富多样，几乎涵盖了所有知识领域。该文献的一部分旨在减少在指标标准化、加权和聚合过程中通过复合指标结构传播的不确定性。即使没有一个综合指标可以免于批评，目前的文献已经足够大和深入，可以指导研究人员构建可靠的综合指标。然而，大多数相关作品都关注在时间或空间中表现多维现象。尽管一些研究对表示时空共存的多维现象感兴趣，但涉及复合指标的部分文献仍然不全面，因此留下了几个悬而未决的问题：在时间上表示多维现象还有哪些额外挑战？空间？可以使用哪些方法？哪种方法最适合这种类型的表示？这种方法有什么缺点？如何减少这些缺点？本研究旨在回答这些问题，以推进多维现象的时空分析。作为一项总体贡献，这项工作提出了一种程序方案，可以减少时空多维现象表示中的主观性和不确定性。作为具体贡献，