Exploring Switzerland's Rural-Urban Continuum Through Unsupervised Learning

In recent decades, urban expansion across Europe has accelerated, driving the rapid growth of rural-urban interfaces. The increasingly complex and dynamic nature of territorial transitions calls for the timely development of classification systems designed to systematically organize areas along a spectrum, from distinctly urban to distinctly rural. Current classifications often rely on predefined criteria, such as population size and density, which may not fully capture the nuanced and evolving nature of transitions driven by the complex interplay of socioeconomic processes, demographic shifts, environmental factors, and dynamic geographic forces.

This research addresses existing gaps by employing modern data-driven approaches, including machine learning and clustering techniques, to develop adaptive typologies that integrate diverse demographic, socioeconomic, and environmental variables. Using Switzerland as a case study, the proposed methodology offers a dynamic and scalable framework for territorial classification, supporting the effective management of territorial transitions and landscape conservation in Alpine regions. The analysis leverages a multidimensional dataset derived from the 2020 official census, incorporating 18 variables that encompass demographic profiles, socio-economic, and the physical space characteristics.

We used Self-Organizing Map (SOM) combined with hierarchical clustering. SOM, a type of competitive learning neural network, reduces the complexity of high-dimensional data by mapping it onto a two-dimensional grid of neurons. Visual outputs, such as heatmaps, enhance the interpretation of trends and patterns, providing a clearer understanding of variables distributions and interrelationships. Afterward, the SOM output grid of neurons was aggregated into six distinct clusters, which were mapped onto the geographical space. This produced a visual representation of the spatial organization of territorial typologies along the rural-urban continuum in Switzerland at a detailed municipal level.

The data-driven clustering approach developed in this study proved effective in capturing the complex and diverse nature of Swiss territorial typologies. The key findings reveal a landscape marked by a complex rural-urban interface, extensive intermediate zones, and significant spatial fragmentation. These final six territorial typologies could be characterized as follows: urban centres, representing the main hubs at the highest level of the Swiss urban hierarchy; suburban areas, located near and well-connected to urban centres; two peri-urban areas, distinguished into aging-rural areas and rural-urban edge; rural-forest areas, situated at medium to high elevations, featuring a forested landscape and rural settings; unproductive areas, encompassing high-altitude regions and including critical Alpine glaciers.