Urban Ageing in Europe—Spatiotemporal Analysis of Determinants

Abstract

<jats:p>The aim of this study was to identify determinants of the population ageing process in 270 European cities. We analyzed the proportion of older people: men and women separately (aged 65 or above) in city populations in the years 1990–2018. To understand territorially-varied relationships and to increase the explained variability of phenomena, an explanatory spatial data analysis (ESDA) and geographically weighted regression (GWR) were applied. We used ArcGIS and GeoDa software in this study. In our research, we also took into account the spatial interactions as well as the structure of cities by size and level of economic development. Results of the analysis helped to explain why some urban areas are ageing faster than others. An initial data analysis indicated that the proportion of the elderly in the population was spatially diversified and dependent on gender, as well as the size and economic development of a unit. In general, elderly individuals were more willing to live in larger and highly developed cities; however, women tended to live in large areas and men in medium-sized to large urban areas. Then, we conducted the urban ageing modelling for men and women separately. The application of GWR models enabled not only the specification of the city population ageing determinants, but also the analysis of the variability in the strength and direction of dependencies occurring between the examined variables in individual cities. Significant differences were noted in the analysis results for specific cities, which were often grouped due to similar parameter values, forming clusters that divided Europe into the eastern and western parts. Moreover, substantial differences in results were obtained for women and men.</jats:p>

Regionally Divergent Patterns in Factors Affecting Municipal Waste Production: The Polish Perspective

“…The localized estimations of the final model (5) were visualized on the maps, as they play a key role in interpreting the obtained results and understanding the studied relationships with a spatial perspective [70].…”

How Is Urban Greenness Spatially Associated with Dockless Bike Sharing Usage on Weekdays, Weekends, and Holidays?

“…Secondly, spatial models have never been applied in studies of association between urban greenness and dockless bike sharing usage, hence resulting in the lack of the exploration of the spatially varying impacts of urban greenness on bike sharing usage [4,5]. The main purpose of the research is to explore and compare the spatial associations between eye-level and overhead level greenness, and the usage of dockless bike sharing on weekdays, weekend, and holidays in a center area of Shenzhen, China [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57].…”

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“…where i represents the Thiessen-polygon buffer, y i represents the dependent variable (dockless bike sharing usage), (u i , v i ) represents the spatial location of i, 0 represents the intercept, k represents the coefficients, x ik represents the independent variables, and i represents the error term [44][45][46][47]. The GWR model estimator can be expressed as follows:…”

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