Rank aggregation methods dealing with incomplete information applied to Smart Cities

Abstract

Abstract-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.

Smart City Ranking System: A Supporting Tool to Manage Migration Trends for Australian Cities

“…As a result, this limits the number of cities and indicators used for the ranking system. A usual challenge that may occur is only partial ranking can be found [77]. As a strategy to overcome the lack and unreliability of data, it is recommended that a large scale of data should be collected and integrated [51].…”

Smart City-Ranking of Major Australian Cities to Achieve a Smarter Future

“…The selection of appropriate components to evaluate the smartness of a city remain a point of discussion in the absense of a concrete definition of Smart City concept. However, studies [25,63,[68][69][70][71][72] shows the technology, culture, governance, sustainability, citizen-centred, economy, environment, liveability, transportation, communication, energy, water, culture, and education are main areas of attentions for a Smart City. Allam and Dhunny [68] recommend the assessment of smartness based on key dimensions of culture, metabolism, and governance.…”

“…The number of indicators for a factor and factors for a component is selected based on its relevancy as well as the availability of data. To assure a comprehensive ranking and avoiding a partial ranking [71,80], 90 indicators were selected to represent 26 factors and six components (Refer Figure 2 and detailed indicators list is attached as Appendix A). The figure also shows a color-coding system, wherein each component is associated with a specific color (orange for Economy, red for Governance, green for Environment, and so on).…”

Métodos y algoritmos en problemas de decisión de grupo a partir de relaciones de preferencia fuzzy

“…Proceedings 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (Dopazo and Martínez-Céspedes, 2015). El citado congreso es categoría A del "CORE Computer Science Conference Rankings".…”

“…Una situación común en problemas del mundo real es que los rankings proporcionados por los expertos pueden no contener información sobre todas la alternativas a considerar. En términos generales, si R es un ranking o lista ordenada con respecto al conjunto X, dependiendo de la información que contiene R se pueden presentar varias situaciones (Dopazo and Martínez-Céspedes, 2015;Farah and Vanderpooten, 2007):…”