PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analyzing a sustainability indicator by means of qualitative kernels
Cecilio Angulo, Luis Gonzalez, Francisco Ruiz, Andreu Catala, Francisco Velasco and Nuria Agell
In: Advanced methods for decision making and risk management in sustainability science (2006) Nova Science Publishers , New York, USA . ISBN 1-60021-427-4


At the beginning of this new century, a growing concern for Ecology has led to the vital need for sustainability in society, the main reason being to achieve a high quality of life. This explains why, in the meeting of European municipalities in Hanover in February 2000, where the sustainability measurement indicators were agreed, the satisfaction of citizens with their local community was chosen as the first indicator. The social indicator movement began in the 1950s with objective health, education level, per capita income and life expectancy indicators, etc., with the idea that an improvement in the objective circumstances would affect some aspect of the quality of life. However, the uncertain explanatory power of objective conditions led, in the following decade, to a second line of research based on the use of more direct subjective indicators, more closely linked to the individual whose quality of life is being assessed. Instead of inferring wellbeing from the characteristics of the group, the individual is questioned regarding their level of wellbeing. Throughout the year 2002, the research group GREC, mainly formed by researchers from the UPC and ESADE-URL, carried out a study, commissioned by the Town Council of Vilanova i la Geltru, a town with over 55.000 inhabitants, on the subjective indicator ‘citizen satisfaction’. The work was commissioned for two reasons. Firstly, the independence of the public institution, allowing the results obtained from reference questionnaires to be believed and, secondly, thanks to the experience of the group in processing qualitative information. An initial descriptive study, successfully defended and presented to the Town Council, has permitted a considerable volume of data to be collected. This information is basically qualitative, but with a high informative content on the perception that the citizens have of Vilanova. With this basis, the GREC group and a working party from the Universidad de Sevilla, members of ARCA ( ), raised the need to analyze this highly uncertain and subjective information, using new artificial intelligence techniques based on collective and interval reasoning. This document presents two totally new inference techniques for the field of interval and qualitative analysis, based on the kernel methods and statistical learning methodology, which have been shown to be very effective as machine learning. Moreover, on using them it is possible to work on any original space, and it is not necessary to work with numerical values (bioinformatics, string-type kernels). This study analyzes two approaches oriented at sustainability data: kernels on a discrete structure (the orders of magnitude) and interval kernels. Their good characteristics are studied with several examples, and an initial application to the sustainability data of the Town Council of Vilanova is performed. Finally, the feasibility is analyzed of a soft-computing tool which allows the degree of citizen satisfaction to be qualitatively assessed.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2224
Deposited By:Cecilio Angulo
Deposited On:01 October 2006