Building composite indicators for the territorial quality of life assessment in European regions: combining data reduction and alternative weighting techniques

Ustaoglu, Eda; Lopez, Gloria Ortega; Gutierrez-Alcoba, Alejandro

doi:10.1007/s10668-023-04116-w

Cited by 1 publication

(4 citation statements)

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“…A great deal of composite indicators uses equal weighting, which assigns the same weight to each component. In other instances, such as factor analysis, principal component analysis, the entropy method, or data envelopment analysis (DEA), the weights are derived directly from the data [21,34]. Other methods use the AHP (Analytical Hierarchy Process) [74,75], the best worst method [76], SMART (Simple Multi-Attribute Rating Technique) [77], and the delphi method [78] to estimate the weights external to the data.…”

Section: Weighting the Indicatorsmentioning

confidence: 99%

“…The second is the effectiveness of data-based methods at coarser scales as the expert-based methods reflect the local conditions and these are more powerful at local scales. A further reason for selecting the entropy method is that according to the findings of Ustaoglu et al [21], it is the least sensitive method to a change in weights, and it can be considered as a robust and flexible method in the construction of composite indicators.…”

Section: Weighting the Indicatorsmentioning

confidence: 99%

“…This stems from the existence of various methods, definitions, and variables used for explaining the existence of spatial factors which have an influence on QoL [16][17][18]. A further reason is that the analysis has been conducted for different case study areas characterised by different socio-economic and spatial development patterns, and it is questionable whether a spatial pattern of an urban system in one urban area has a similar effect in determining QoL in a different urban area [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…For the weighting of sub-indicators, entropy weight method was applied. Similar approaches were undertaken for the development of indicators including by Arifwidodo [38], Zoeteman et al [37], Xiao et al [39], Bovkır et al [34], and Ustaoglu et al [21]. Residential expansion and densification indicators were constructed based on residential land use, economic output, and population data (for the review of urban expansion and densification indicators, refer to Ma and Xu [40], Xu and Min [41], and Chen et al [42]).…”

Section: Introductionmentioning

confidence: 99%

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The Inter-Relationships of Territorial Quality of Life with Residential Expansion and Densification: A Case Study of Regions in EU Member Countries

Ustaoglu,

Williams

2024

Urban Science

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High-density urban development is promoted by both global and local policies in response to socio-economic and environmental challenges since it increases mobility of different land uses, decreases the need for traveling, encourages the use of more energy-efficient buildings and modes of transportation, and permits the sharing of scarce urban amenities. It is therefore argued that increased density and mixed-use development are expected to deliver positive outcomes in terms of contributing to three pillars (social, economic, and environmental domains) of sustainability in the subject themes. Territorial quality of life (TQL)—initially proposed by the ESPON Programme—is a composite indicator of the socio-economic and environmental well-being and life satisfaction of individuals living in an area. Understanding the role of urban density in TQL can provide an important input for urban planning debates addressing whether compact development can be promoted by referring to potential efficiencies in high-density, mixed land use and sustainable transport provisions. Alternatively, low-density suburban development is preferable due to its benefits of high per capita land use consumption (larger houses) for individual households given lower land prices. There is little empirical evidence on how TQL is shaped by high-density versus low-density urban forms. This paper investigates this topic through providing an approach to spatially map and examine the relationship between TQL, residential expansion, and densification processes in the so-called NUTS2 (nomenclature of terrestrial units for statistics) regions of European Union (EU) member countries. The relative importance of each TQL indicator was determined through the entropy weight method, where these indicators were aggregated through using the subject weights to obtain the overall TQL indicator. The spatial dynamics of TQL were examined and its relationship with residential expansion and densification processes was analysed to uncover whether the former or the latter process is positively associated with the TQL indicator within our study area. From our regression models, the residential expansion index is negatively related to the TQL indicator, implying that high levels of residential expansion can result in a reduction in overall quality of life in the regions if they are not supported by associated infrastructure and facility investments.

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