The present study illustrates a multidimensional analysis of an indicator of urban land use efficiency (per-capita built-up area, LUE) in mainland Attica, a Mediterranean urban region, along different expansion waves (1960–2010): compaction and densification in the 1960s, dispersed growth along the coasts and on Athens’ fringe in the 1970s, fringe consolidation in the 1980s, moderate re-polarization and discontinuous expansion in the 1990s and sprawl in remote areas in the 2000s. The non-linear trend in LUE (a continuous increase up to the 1980s and a moderate decrease in 1990 and 2000 preceding the rise observed over the last decade) reflects Athens’ expansion waves. A total of 23 indicators were collected by decade for each municipality of the study area with the aim of identifying the drivers of land use efficiency. In 1960, municipalities with low efficiency in the use of land were concentrated on both coastal areas and Athens’ fringe, while in 2010, the lowest efficiency rate was observed in the most remote, rural areas. Typical urban functions (e.g., mixed land uses, multiple-use buildings, vertical profile) are the variables most associated with high efficiency in the use of land. Policies for sustainable land management should consider local and regional factors shaping land use efficiency promoting self-contained expansion and more tightly protecting rural and remote land from dispersed urbanization. LUE is a promising indicator reflecting the increased complexity of growth patterns and may anticipate future urban trends.
Metropolitan growth in Europe has resulted in drastic changes of urban forms, socio-spatial structures and land-use patterns due to sequential processes of urbanization, suburbanization and re-urbanization. To assess latent shifts from mono-centric models towards more disarticulated and decentralized settlement configurations, the present study evaluates spatio-temporal patterns of growth between the 1920s and the 2010s in three Mediterranean cities with different structure and functions (Barcelona: compact and moderately polycentric; Rome: dispersed, medium-density; Athens: mono-centric, hyper-compact). To identify and characterize long-term urban transformations, an original approach was illustrated in this study, based on a multivariate analysis of 13 indicators resulting from descriptive statistics and linear regression modeling the relationship between population density and distance from inner cities. The empirical results of this study indicate that Barcelona, Rome and Athens have experienced different urbanization cycles, characterized by a (more or less) concentrated distribution of population along urban gradients. Despite similarities in demographic dynamics and planning practices, these processes have determined (i) a mostly centralized growth in Barcelona, (ii) a relatively dispersed and discontinuous spatial structure in Rome, and (iii) a steep decline of population density with the distance from downtown Athens. Compact urban expansion, population decline and urban de-concentration were finally assessed using the analytical approach proposed in this study.
Salvati L. and Carlucci M. Patterns of sprawl: the socioeconomic and territorial profile of dispersed urban areas in Italy, Regional Studies. The debate on the causes and consequences of urban sprawl has not led to a widely accepted interpretative framework so far. However, to formulate effective sustainable development policies, a comprehensive analysis of sprawl is becoming more and more urgent in Europe. Through an exploratory data analysis of the spatial distribution of 132 indicators (regarded as socioeconomic and environmental factors of urban sprawl) in 8100 Italian municipalities affected by different levels of settlement dispersion, this study discusses place-specific factors that depend on the socioeconomic context and lead to diverging models of sprawl throughout the country. The illustrated methodology produces an informative base possibly supporting urban containment and sustainable development policies in ‘sprawling’ regions.