Rank aggregation methods dealing with incomplete information applied to Smart Cities

City-rankings have become a central instrument for assessing the attractiveness of urban regions over the last years. Demographic, environmental, economic, political and socio-cultural factors are forcing the urban world to design and implement Smart Cities. A set of multidimensional components underlies the fuzzy smart city concept. As a result cities are evaluated and ranked with regard to different characteristics, and smart city measures are achieved through chosen indicators. Therefore, the problem of combining multiple rankings to form an aggregate ranking, which compares city performance, is recognized as a useful tool in this context. Moreover, a usual situation is when incomplete information arises and only partial rankings may be supplied. This paper addresses the general problem of rank aggregation dealing with incomplete information based on rank aggregation methods and multicriteria decision making theory. It consists on constructing a consensus ranking from partial rankings of a set of objects provided according different criteria. Our techniques rely on outranking matrices as a way of collecting relevance information from input data, theory of fuzzy preference relations and the PageRank algorithm.