  The paper introduces an approach to identify a set of spatially constrained
homogeneous areas maximally homogeneous in terms of epidemic trends. The
proposed hierarchical algorithm is based on the Dynamic TimeWarping distances
between epidemic time trends where units are constrained by a spatial proximity
graph. The paper includes two different applications of this approach to Italy,
based on different data (number of positive test and number of differential
deaths, with respect to the previous years) and on different observational
units (provinces and Labour Market Areas). Both applications, above all the one
related to Labour Market Areas, show the existence of well-defined areas, where
the dynamics of growth of the infection have been strongly differentiated. The
adoption of the same lock-down policy throughout the entire national territory
has been therefore sub-optimal, showing once again the urgent need for local
data-driven policies.
