Typology of Alpine region using spatial-pattern indicators

The European Alps and their surroundings is a heterogeneous region, where different spatial conditions require appropriate research approaches as well as political and planning strategies. Researchers and decision makers are dependent principally on information relating to environmental and spatial characteristics in their area of interest. Up until now and following the call of Agenda 21, a significant amount of information has already been compiled in a variety of sustainability-indicator systems that also contain information on spatial conditions. The aim of the presented study was to develop a regional typology of the European Alps and their surroundings, on the basis of spatial-pattern indicators. irst step, a set of 25 spatial-pattern indicators on topography, landscape composition, landscape pattern, and road accessibility were calculated for the 17,504 municipalities in the Alpine-Space cooperation area. The indicator results were subjected to a Principal Components Analysis (PCA) using Varimax rotation with Kaiser normalization. The PCA resulted in five components that explain 71.0% of the total variance. A PCA validation using a sub-sampling approach revealed that the PCA was valid. The PCA results were subsequently employed in a hierarchical clustering-approach using the Ward algorithm with squared Euclidean distance. The number of clusters was chosen by means of the dendrogram, according to the elbow criteria, and by reasons of interpretability. erarchical clustering resulted in 6 clusters. Cluster 1 represents “Non-mountainous cultural landscapes”, cluster 2 “Poorly structured agricultural landscapes”, cluster 3 “Agricultural landscapes, interspersed with highly structured semi-natural and natural areas”, cluster 4 “Remote, highly structured cultural landscapes with a high level of insolation”, cluster 5 “Mountainous, forested areas”, and cluster 6 “Mountainous, semi-natural and natural open areas”. gh the presented typology and its underlying analyses have some limitations, they can be applied for various purposes. The spatial-pattern indicators provide individual information for more than 17,000 municipalities in the Alpine Space. Supra-regional relationships of spatial-pattern types are offered by the five extracted components and the six clusters. The results can support researchers and stakeholders from the local to international level.