确定欧洲城市生活质量差异的指标

Wolfgang Breuer, Dominique Brueser
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引用次数: 4

摘要

通过涵盖城市生活若干主题的指标对城市进行比较,对于诸如城市发展资金分配等政策决定至关重要。简单地将大量指标加到一个单一指数中,由于不透明和非常有限的解释可能性,引起了合理的批评。然而,同样的理由也可以用来反对使用大量的分类指标进行城市比较。本文通过确定少数相关指标来确定生活质量差异,有助于引导一条中间路线。这一分析的基础是欧洲统计局数据库提供的城市审计关键指标集,它由46个指标组成,涵盖城市生活的不同方面。主成分分析揭示了少数指标,这些指标对所分析的十个国家和五个时间框架中每个国家所选城市之间的总体差异有很大影响。本研究将主成分分析的一般应用扩展到区域聚类中,结合244个部分分析来确定城市差异的决定指标。结果表明,可以确定一小部分指标,这些指标通常是最相关的决定因素之一。这些选定的指标分布在代表环境、人力、制造业和社会城市资本以及人口方面的最初组。它们涵盖了当前关于城市环境、基础设施和移民困难的政治辩论,安全,特别是人口密集地区因匿名和贫困而造成的安全损害,以及导致大城市空间短缺和小城市遗弃的人口变化。将这种方法应用于更广泛的数据集似乎很有希望,因为它可能会产生重要的见解,从而影响城市发展及其资金分配过程的政策措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determining Indicators of Quality of Life Differences in European Cities
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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