RESUMO Para compreender como a Covid-19 se distribui pelo espaço brasileiro, os geógrafos utilizam-se do raciocínio geográfico apoiados em técnicas de mapeamento e representações espaciais. Nesse sentido, propomos aqui uma série de procedimentos para compreender a doença no espaço, primeiramente com a exploração e descrição dos dados, a análise espacial e a síntese por meio da modelização gráfica, partindo em seguida para a comunicação cartográfica. Como efeito, esperamos que esse caminho teórico e metodológico possa balizar a criação de uma imagem de mundo capaz de responder a demandas mais urgentes postas pela pandemia, ao mesmo tempo criar reflexões sobre como a produção do espaço atual cria vulnerabilidades nesta globalização perversa.

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Composite indicators are almost always determined by methods that aggregate a reasonable number of manifest variables that can be weighted—or not—as new synthesis variables. A problem arises when these aggregations and weightings do not capture the possible effects that the various underlying dimensions of the phenomenon have on each other, and consequently distort the assessment of intra-urban inequality. In this paper, we explore the direct and indirect effects that the different underlying dimensions of intra-urban inequality have on indicators that represent this phenomenon. Structural equation modeling was used to build a composite indicator that captures the direct and indirect effects of the underlying dimensions of intra-urban inequality. From this modeling that combines confirmatory factor analysis with a system of simultaneous equations, the intra-urban inequality of the urban conurbation of Maringá–Sarandi–Paiçandu, Brazil was measured. The model comprises first- and second-order structures. The first-order structure is composed of non-observed variables that represent three underlying dimensions of intra-urban inequality. The second-order structure is the intra-urban inequality composite indicator that synthesizes the non-observed variables of the first-order structure. The model aims at demonstrating how to perform a theorized measurement of urban inequality so that it makes it possible to identify which dimensions most influence the others, as well as which dimensions are more relevant to this purpose.

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. KeywordsComposite indicators • Multidimensional phenomena • Cluster analysis • Principal component analysis • Time-in-space analysis • Urban geography