The quality of life in Czech rural and urban spaces

Quality of life (QoL) is a multifaceted concept encompassing subjective well-being and objective environmental conditions. Its assessment is challenging due to its multidimensionality and the varying perspectives of different disciplines. This study focuses on the objective dimension of QoL, understanding it as an evaluation of how "good" life is, as defined by Murgaš and Klobučník (2016). The objective QoL is measurable through a decomposition into component domains represented by aggregated datasets or indices. A key premise is that QoL varies spatially, influenced by geographic factors like demography, health, safety, and environment. The concept of "livability" is often intertwined, representing a place's suitability for living. This study contrasts QoL in urban and rural spaces, recognizing the complexities of their definition and the blurred transitions caused by suburbanization. The study utilizes a fuzzy approach to quantify the degree of municipal membership in rural or urban spaces, moving beyond a simple dichotomy. Previous research on QoL's spatial differentiation often uses national statistics, regression models, or a combination of spatial and non-spatial analyses, predominantly focusing on the subjective dimension. This study addresses the need for more research on the objective dimension of QoL in urban and rural areas simultaneously, utilizing a municipal-level analysis.

Existing research on spatial differentiation in quality of life often employs data from national statistical offices or government agencies. Analytical approaches range from regression models (linear, probit, logistic) to combinations of spatial and non-spatial analyses. The majority of studies focus on the subjective dimension, finding higher QoL in rural areas. Objective QoL assessments yield mixed results, depending on the country and chosen indicators. Studies like Bertolini and Pagliacci (2017), and Campanera and Higgins (2011) indicated higher objective QoL in rural spaces, while Ma et al. (2020) found comparable levels across urban and rural spaces. Defining urban and rural spaces commonly involves population or population density criteria, with some studies using respondent self-classification or municipal statutes. Different approaches to defining urban and rural spaces include Eurostat methodology, multivariate approaches with principal component analysis (PCA), and fuzzy logic approaches that capture the urban-rural continuum. Ma et al. (2020) offer a unique perspective, treating urban and rural spaces as distinct systems with specific QoL features, differing from traditional global-level comparisons.

This study uses an objective QoL index adapted from Murgaš and Klobučník (2016), incorporating 10 indicators reflecting aspects like life expectancy, mortality, education, employment, and environmental pollution. Data limitations resulted in some indicators using data from different time periods (5-year averages for most, with exceptions for education, emissions, and blood donations). The study uses a fuzzy approach (Pászto et al., 2015, 2016) to quantify the degree of municipal membership in urban and rural spaces, creating a scale from 0 (purely rural) to 1 (purely urban). Spatial analysis included assessing spatial autocorrelation (global Moran's I and local Moran's I or LISA) to identify clusters of similar QoL values and membership degrees. Geographically weighted correlation (GWC) was employed to explore the non-stationary relationships between QoL and urban space membership, using Spearman's correlation with a tricube kernel and various bandwidths (k-nearest neighbors). Finally, a typology based on bivariate mapping classified municipalities into groups according to QoL levels (low, medium, high) and urban/rural membership categories (rather rural, intermediate, rather urban). Three categories were determined for each variable using the natural breaks method. Non-parametric methods were used due to non-normal data distributions.

Globally, no significant relationship existed between overall QoL and urban/rural membership. However, positive spatial autocorrelation was found for the QoL index, indicating clustering of similar values. LISA analysis, using a bandwidth of 100 neighbors for regional detail, revealed distinct clusters of low and high QoL areas. Low QoL was concentrated in northwest and northeast regions, while high QoL prevailed in central and southeast areas, with outliers at boundaries. For urban-rural membership, spatial autocorrelation was lower. Geographically weighted correlation, again with a 100-neighbor bandwidth, showed a positive relationship between QoL and urban space membership in most locations, particularly around major cities. Exceptions existed in economically deprived northeast and northwest regions showing a negative correlation. The highest correlation coefficients were found in the suburbs, not the city centers themselves. A typology classified municipalities based on QoL level and urban/rural membership. Results showed high QoL prevalent in urban areas and their hinterlands in several regions, while low QoL concentrated in northwest and northeast regions. Analysis of the typology considering population and area indicated that the largest portion of the population lives in areas of high QoL and high urban membership. However, intermediate spaces demonstrated the greatest potential for high QoL, although not dominating in population terms. The medium quality of life level prevailed in all three membership categories.

The findings support the hypothesis that objective QoL is spatially differentiated and related to urban space membership. The positive geographically weighted correlation confirms a tendency for higher QoL in more urban areas, although exceptions exist in economically and socially disadvantaged regions. The significance of the results lies in demonstrating the importance of considering spatial context in QoL analysis, and in highlighting the potential of intermediate spaces for high QoL. The use of a fuzzy approach to define urban-rural membership allows for a more nuanced understanding of the continuum, avoiding the limitations of a simple dichotomy. The study's findings contribute to the understanding of regional disparities and inform policy-making focused on enhancing QoL across urban and rural areas. Further research could explore the interaction between objective and subjective QoL, examine the impact of specific policies on QoL in different space types, and refine the QoL index with more detailed and consistent data.

This study presents a novel methodology for assessing the relationship between objective QoL and urban-rural space membership using a fuzzy approach. It demonstrates a positive spatial correlation between QoL and urbanity, with exceptions in economically disadvantaged regions. It highlights the potential of intermediate spaces for high QoL. This methodology is replicable in other contexts, offering a valuable tool for regional development and planning. Future research should focus on integrating subjective QoL measures, longitudinal analysis, and exploring the impact of specific policy interventions.

Data limitations affected the index's construction, with some indicators using data from varying time periods. The use of an objective QoL index might not fully capture the subjective experiences and perceptions of residents. The spatial analysis methods are sensitive to parameter choices, although the chosen parameters were shown to produce robust results.