Estimating quality of life dimensions from urban spatial pattern metrics. (January 2021)

• Record Type: Journal Article
• Authors: Sapena, Marta; Wurm, Michael; Taubenböck, Hannes; Tuia, Devis; Ruiz, Luis A.
• Is Part Of: Computers, environment and urban systems. Volume 85(2021)
• Publication Date: 2021-01
• Subjects: Spatial metrics -- Socio-economic variables -- Local climate zones -- Quality of life -- Remote sensing; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101549
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: The spatial structure of urban areas plays a major role in the daily life of dwellers. The current policy framework to ensure the quality of life of inhabitants leaving no one behind, leads decision-makers to seek better-informed choices for the sustainable planning of urban areas. Thus, a better understanding between the spatial structure of cities and their socio-economic level is of crucial relevance. Accordingly, the purpose of this paper is to quantify this two-way relationship. Therefore, we measured spatial patterns of 31 cities in North Rhine-Westphalia, Germany. We rely on spatial pattern metrics derived from a Local Climate Zone classification obtained by fusing remote sensing and open GIS data with a machine learning approach. Based upon the data, we quantified the relationship between spatial pattern metrics and socio-economic variables related to 'education', 'health', 'living conditions', 'labor', and 'transport' by means of multiple linear regression models, explaining the variability of the socio-economic variables from 43% up to 82%. Additionally, we grouped cities according to their level of 'quality of life' using the socio-economic variables, and found that the spatial pattern of low-dense built-up types was different among socio-economic groups. The proposed methodology described in this paper is transferable to other datasets, levels, and regions. This is of great potential, due to the growing availability of open statistical and satellite data and derived products. Moreover, we discuss the limitations and needed considerations when conducting such studies. Highlights: We apply local climate zones representing cities by their density, building types, heights, greenness and land cover. Socio-economic indicators can be modelled by means of urban spatial pattern metrics extracted from local climate zone maps. Grouping cities into quality of life levels show similarities in the spatial pattern of low-dense built areas. The proposed method helps to obtain empirical evidences that may contribute to urban planning.