Determining indicators of quality of life differences in European cities
||Wolfgang Breuer and Dominique Schaeling
||Determining indicators of quality of life differences in European cities
||19th Annual European Real Estate Society Conference in Edinburgh, Scotland
||The comparison of cities by indicators covering several topics of urban life is crucial for policy decisions such as funding allocation for urban development. Simply adding up a high number of indicators to one single index evokes reasonable criticism due to opacity and very limited interpretation possibilities. Nevertheless, the same arguments can be made against using large sets of disaggregated indicators for city comparison. This paper helps to steer a middle course by identifying of a small number of relevant indicators to determine quality of life differences. The basis of this analysis is the Urban Audit Key Indicator Set which is provided by the Eurostat database and consists of 46 indicators covering different aspects of urban life. Principal Component Analysis reveals a small number of indicators which have a high impact on the overall differences between the selected cities of each of the ten countries and five time frames that were analysed. This study extends the general application of Principal Component Analysis for regional clustering by the combination of 244 partial analyses to identify determining indicators of urban differences. The results show that a small set of indicators, which are often among the most relevant determinants, can be identified. Those selected indicators are spread over the initial groups representing environmental, human, manufactured and social urban capital as well as demographic aspects. They cover current political debates on environmental, infrastructural and migration difficulties in cities, safety and especially security impairment due to anonymity and poverty in densely populated areas as well as population changes leading to space shortage in larger cities but also abandonment in small cities. Applying this method to wider data sets seems promising as it might lead to important insights which could impact policy measures on urban development and its funding allocation processes.
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||Parallel Session I9
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