Abstract
The evaluation of the urban quality of life has been an important aspect of the research concerning the contemporary city and an increasingly support to urban planning and management. As part of a project to monitor the quality of life in the city of Porto, a survey of the resident population was conducted in order to study the citizens' perceptions of their local quality of life and its evolution in recent years. The opinions of individuals on their level of satisfaction with various fields of the urban quality of life are systematised, as well as their integrated assessment. This analysis is complemented by a multivariate analysis that allows the grouping of the interviewees in large homogenous groups and their social and economic characterisation. Based on the results achieved, we try to highlight the usefulness of the qualitative analysis of the quality of life to support the definition of urban policies. © 2007 Springer Science + Business Media BV/The International Society for Quality-of-Life Studies (ISQOLS).

Abstract
In previous work (Brito and De Carvalho (1999)) we have considered the presence of dependence rules between variables in the framework of a symbolic clustering method. In another paper Brito (1998) has addressed the problem of clustering probabilistic data. The aim of this paper is to bring together the two issues, that is, to take into account dependence rules on probabilistic data. This is accomplished by introducing new generality measures with an appropriate generalization operator. This approach allows for the extension of a symbolic clustering. method to constrained probabilistic data.

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In this paper we introduce and develop the concept of interval-weighted networks (IWN), a novel approach in Social Network Analysis, where the edge weights are represented by closed intervals composed with precise information, comprehending intrinsic variability. We extend IWN for both Newman's modularity and modularity gain and the Louvain algorithm, considering a tabular representation of networks by contingency tables. We apply our methodology to two real-world IWN. The first is a commuter network in mainland Portugal, between the twenty three NUTS 3 Regions (IWCN). The second focuses on annual merchandise trade between 28 European countries, from 2003 to 2015 (IWTN). The optimal partition of geographic locations (regions or countries) is developed and compared using two new different approaches, designated as Classic Louvain and Hybrid Louvain , which allow taking into account the variability observed in the original network, thereby minimizing the loss of information present in the raw data. Our findings suggest the division of the twenty three Portuguese regions in three main communities for the IWCN and between two to three country communities for the IWTN. However, we find different geographical partitions according to the community detection methodology used. This analysis can be useful in many real-world applications, since it takes into account that the weights may vary within the ranges, rather than being constant.

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