ABSTRACT
The problem of ranking cities according to their smart city nature can be classified as a multi-criteria decision-making problem, due to the multidimensional character of the smart city concept. It involves aggregating data from several ranked lists according to multiple criteria in order to produce a synthetic ranking which compares city performance. There is a growing interest in city rankings since they are recognized as instruments for assessing attractiveness of urban regions, policy evaluation, benchmarking, management decision-making, etc. We propose a two-stage approach to address the group-ranking problem in the smart city context. Methods are based on deriving priority vectors of cities from outranking matrices that collect relevant information from input data. Furthermore, fuzzy preference relations properties and a procedure similar to Google’s PageRank algorithm are considered. The application of the proposed methods is illustrated using the data provided by the IESE Cities In Motion Index 2016 (CIMI 2016) report. Our approach provides a theoretical framework for studying the problem, efficient computational methods to solve the problem, and some performance measures. It relaxes the completeness requirements and overcomes some shortcomings of current group-ranking methods.
