Production models’ flexibilization in capitalist economies continues to transform industrial activities’ spatial organization in a regional continuum. Placed in planned complexes located on cities’ fringes, firms often stand inaccessible from regional circulation routes, which hinder activities’ long-term economic sustainability. Further changes are impending, as forthcoming Smart Manufacturing logistics require efficient linkages between local and regional transportation models. Such issues compel urban planners, economists and policymakers to re-evaluate industrial territories’ imprint on metropolitan dynamics and enact proper strategies towards the industry. In this paper, the role of road-circulation network centralities on industrial complexes’ placement in a regional continuum is analysed, refining the existent methods to assess industry spatial configuration and agglomeration logics. Empirical cases comprise five Brazilian industrial complexes in Porto Alegre’s Metropolitan Region. Hypothesis is that road-circulation network centralities’ hierarchies (closeness and betweenness) have positive correlations to industrial placement patterns at regional and inner-complex scale, informing regional contiguity dynamics amid discontinuous industrial spaces.

The need to enrich the semantic completeness of OpenStreetMap (OSM) data is crucial for its effective use in geographic information systems and urban studies. Addressing this challenge, our research introduces a novel hierarchical feature augmentation approach to developing machine learning classifiers by the features retrieved from various levels of road network connectivity. This method systematically augments the feature space by incorporating measure values of connected road features, thereby integrating extensive contextual information from the network hierarchy. In our evaluation, conducted across diverse urban landscapes in six cities in Italy and Türkiye, we tested two geometry-, six centrality-, and eight semantic-based features to predict road functional classes stored as a highway = * key in OSM. The findings indicate a marginal impact of geometric features and city identifiers on classification performance. Utilizing centrality attributes alongside semantic features in a direct, non-hierarchical manner results in an F1 score of 80%. However, integrating these features in our network-based hierarchical feature augmentation process remarkably increases the F1 score up to 85%. The success of our approach underlines the importance of network-based feature engineering in capturing the complex dependencies of geographic data, considering a more accurate and contextually aware OSM classification framework.

In recent years, there has been a growing awareness that not all decommissioned and obsolete real-estate assets can be recovered and reused. After the paradigm of urban growth, and following the paradigm of regeneration, a new paradigm seems to be looming on the horizon: the paradigm of shrinkage. Due to this change in perspective, discussions on the potential of demolition policies as an alternative to regeneration and reuse are gaining support in the debate about urban growth. In the United States, there are two on-going programs using demolition as their main tool for urban planning: the blight elimination programs and the flood buyout programs. The former foresees the demolition of abandoned and decayed real-estate assets, while the latter envisions the demolition and relocation of buildings within areas under flooding risks. Given their successful employment in the U.S., this paper evaluates the applicability of these programs to an Italian case, which is characterized by a different building heritage and different territorial conditions. Simulations of the programs’ application are made using two case studies: Lecce nei Marsi (Abruzzo) and Moncalieri (Piemonte). The results demonstrate the substantial feasibility of the blight elimination programs’ usage in Italy, while the flood buyout programs instead demonstrates major obstacles that may hinder its successful application.