Time-Space Analysis of Multidimensional Phenomena: A Composite Indicator of Social Exclusion Through k-Means

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.

Automated summary

Composite Indicators are one-dimensional measurements that simplify the interpretation of multidimensional phenomena and aid in the formulation of public policies. The literature on composite indicators is diverse and abundant, aimed at reducing uncertainties in the process of normalization, weighting, and aggregation. While there is already a large body of literature to guide researchers in constructing reliable composite indicators, there are still open questions regarding representing multidimensional phenomena in time-space analysis.