Population Trends and Urbanization: Simulating Density Effects Using a Local Regression Approach

Abstract

Density-dependent population growth regulates long-term urban expansion and shapes distinctive socioeconomic trends. Despite a marked heterogeneity in the spatial distribution of the resident population, Mediterranean European countries are considered more homogeneous than countries in other European regions as far as settlement structure and processes of metropolitan growth are concerned. However, rising socioeconomic inequalities among Southern European regions reflect latent demographic and territorial transformations that require further investigation. An integrated assessment of the spatio-temporal distribution of resident populations in more than 1,000 municipalities (1961–2011) was carried out in this study to characterize density-dependent processes of metropolitan growth in Greece. Using geographically weighted regressions, the results of our study identified distinctive local relationships between population density and growth rates over time. Our results demonstrate that demographic growth rates were non-linearly correlated with other variables, such as population density, with positive and negative impacts during the first (1961–1971) and the last (2001–2011) observation decade, respectively. These findings outline a progressive shift over time from density-dependent processes of population growth, reflecting a rapid development of large metropolitan regions (Athens, Thessaloniki) in the 1960s, to density-dependent processes more evident in medium-sized cities and accessible rural regions in the 2000s. Density-independent processes of population growth have been detected in the intermediate study period (1971–2001). This work finally discusses how a long-term analysis of demographic growth, testing for density-dependent mechanisms, may clarify the intrinsic role of population concentration and dispersion in different phases of the metropolitan cycle in Mediterranean Europe.

Changes in the spatial distribution of population in urban agglomerations: A spatial-temporal perspective

“…Polinesi, who used Greek cities as an example to establish a partial regression model and found that population growth is internally affected by cities with different levels of development 19 . The difference from previous research is that we deduce the change law of spatial population distribution in the urban agglomeration, thus, we can argue the determining factors of this law.…”

“…At the rst stage, the scale of the urban population increased signi cantly. At the second stage, the urban population tends to saturate and grow slowly 19 . Taking Athens of Greece as an example, from 1950 to 2000, it had been a linear growth, and the growth rate was relatively high, from 2001 to 2020, the development of the permanent population tends to be slow 19 .…”

Identifying Spatial Driving Factors of Energy and Water Consumption in the Context of Urban Transformation

“…The cluster characteristics are emphasized with the use of Moran's I and LISA to prescreen the spatial autocorrelation effects [32,36,39,43,45,47,48]. LISA is modified from Moran's I to indicate the cluster for each location [49], and was included to provide the local spatial autocorrelation.…”

“…Figures of a consolidated city-county (the unified administrative body of Taichung city is used as the case study) were extracted to explore dramatic changes in socio-economic variables. To diminish the bias of long-term changes and variance in non-linear regression analysis, local indicators of spatial association (LISA) was applied to identify local clusters and to screen a better bandwidth, which is needed in GWR analysis [32,38,44,45]. Multicollinearity was removed with OLS.…”

Modeling Urban Growth and Socio-Spatial Dynamics of Hangzhou, China: 1964–2010

“…In recent years, the research on urban population density and urban growth in European and American countries continues to make progress, accompanied by the improvement of methods. For example, Polinesi et al try to reflect the relationship between population trends and urbanization in Greece by simulating density effects using a local regression approach [14]. Mariani et al illustrates a simplified procedure identifying population sub-centers over 50 years in three Southern European cities (Barcelona, Rome, Athens) by identifying metropolitan subcenters from diachronic density-distance curves [15].…”