Methods and algorithms for constructing consensus rankings in the smart cities context

City-rankings have become a central instrument for assessing the attractiveness of urban regions over the last 20 years. Demographic, environmental, economic, political and socio-cultural factors are encouraging the urban world to design and implement Smart Cities. The Smart City measures are achieved through carefully chosen indicators and allow cities to reorganize themselves successfully, via an understanding of its strengths and weaknesses. Regarding the concept about what a Smart City is, methods and algorithms are needed in order to generate a Smart City Consensus Ranking that summarizes data information according different criteria. The rank aggregation problem consists on combine several ranked lists of the considered alternatives in a robust way to produce a consensus ranking of the n alternatives listed from the most important alternative to the least important one. Modern social choice theory and multi-criteria decision-making (MCDM) have motivated considerable recent work in the computational rank aggregation arena. In this work we present some weighting and aggregation methods from the fields of Social Choice theory, MCDM and Graph Theory. These methods provide a decision-theoretic motivation for constructing a consensus ranking. Our main objective in this article is to contribute to the improvement of the overall quality of composite indicators by looking at one of their technical weaknesses, that is, the aggregation model used for their construction in the smart city context