Suchergebnisse

It is essential to understand territorial disparities across different domains and geographical scales, as in-depth comprehension of spatial inequalities, and the identification of marginalised places, is critical to producing policies that can actively contrast the current situation and improve the quality of life of citizens resident in these areas. This paper introduces a multi-perspective and multi-scalar analysis exploring a communal object of research, the 'lonely' places. This analysis goes beyond the dichotomy of rural-urban, involving multiple domains (agriculture, demography, migration, physical and digital infrastructure) and geographical scales, and presenting results at regional, municipal and grid levels. In this paper, two specific case studies are presented: the uneven spatial access to the high-speed broadband network and the spatial distribution of urban amenities in EU cities concerning the access to service at the local and neighbourhood level. The data sets employed in this work include traditional sources and new sources of data, including spatiotemporal records collected through apps. The spatial analysis is performed at different geographical scales and employs a wide range of analysis techniques, including Machine Learning. Results reveal the patterns of territorial disparities across Europe and identify disconnected and marginalised places with different domains. The localisation of spatial disparities of opportunities, as obtained with this analysis, is of high importance to understand the phenomena related to spatial inequality, as it provides geographical information for urban and territorial policy at high granularity to target the areas affected and the causes of disconnection.

The scarcity or lack of access to essential services at the local and neighbourhood levels in cities can result in significant spatial inequalities, as some areas and their residents can deal with disadvantages and a lower quality of daily life. In particular, the spatial distribution and the variety of amenities at the local scale represent an important feature of the liveliness of places. The local availability and access to essential services are particularly relevant for some demographic groups experiencing limited mobility or mobility poverty, such as older adults living in cities, and spatial disparities have been further exacerbated by the COVID-19 pandemic, which highlighted severe difficulties in accessing essential services. This work explores the issue focussing on the following question: who can access what depending on where they live in cities? Using Machine Learning and Spatial Autocorrelation applied to different data sources for spatial information on the location of urban amenities and Internet access, this work aims to identify the most underserved places in terms of the variety of available amenities and access to quality broadband in three European capital cities. A comparison to urban areas where high percentages of older adults reside makes it possible to identify where residents can locally access several essential services (green spaces, health care, and local shopping) and where this need cannot be satisfied because of a lack in the amenity variety available at walking distance to their home. The combination of underserved areas with a high concentration of senior residents identifies left-behind areas in these cities, where interventions on inequalities are most needed. Results can inform policies aiming at favouring fair access to services at the local scale, possibly including slow and active mobility modes, and in general to develop comprehensive and sustainable planning strategies for cities, leaving no place and no person behind.