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Street classification is fundamental to transportation planning and design. Urban transportation planning is mostly based on function-based classification schemes (FCS), which classifies streets according to their respective requirements in the pre-defined hierarchy of the urban street network (USN). This study proposes a computational approach for a network-based categorization of street segments (NSC). The main objectives are, first, to identify and describe NSC categories, second, to examine the spatial distribution of street segments from FCS and NSC within a city, and third, to compare FCS and NSC to identify similarities and differences between the two. Centrality measures derived from network science are computed for each street segment and then clustered based on their topological importance. The adaption of clustering, which is a numerical categorization technique, potentially facilitates the integration with other analytical processes in planning and design. The quantitative description of street characteristics obtained by this method is suitable for development of new knowledge-based planning approaches. When extensive data or knowledge of the real performance of streets are not available or costly, this method provides an objective categorization from those data sets that are readily available. The method can also assign the segments that are categorized as “unclassified” in FCS to the categories in the NSC scheme. Since centrality metrics are associated with the functioning of USNs, the comparison between FCS and NSC not only contributes to the understanding and description of the fine variations in topological properties of the segments within each FCS class but also supports the identification of the mismatched segments, where reassessment and adjustment is required, for example, in terms of planning and design.

Rapid transitions induced by migration flows and socio-economic developments brought about massive changes in urbanization processes and resulted in increasingly uncertain futures. The implications and complexities of the ensuing urbanization patterns are difficult to predict and project into the future. While most studies are focused on large cities and major urban centers, urbanization processes in small and medium-sized cities have garnered little scholarly and political attention. To understand future urbanization patterns, we used the TOPOI method, a novel approach for classifying territorial settlements, and spatial autoregressive models to examine contrasting futures of population growth and shrinkage in one small and one medium-sized city in Lower Saxony, Germany. Results revealed that despite planning frameworks, high population density and functional mix, respectively, were insufficient mechanisms to reduce land take. Contrary to current assumptions on the functional mix of small and medium-sized towns, our findings showed that more than half of the settlements across the study area accommodated three or more functions. Since the share of residential buildings and functional mix strongly influenced land take, further research is needed to understand their implications on sustainable urban planning. Shrinking towns in Lower Saxony continue to present multidimensional challenges and emphasize the need for transforming local planning cultures and institutional frameworks to sustainably manage and repurpose these potentially vacant areas.

The accessibility of suburban and rural areas presents existential challenges for resident communities. Municipality inhabitants with little access to public transport and structurally weak regions often work in distant urban agglomerations, with long commutes. Widespread monofunctional ‘dormitory’ villages and suburban areas with low building density and access to public transport, fuelling automobility dependence, are still implemented as planning concepts in Lower Saxony. The objectives of this study are to analyse the quality of public transport accessibility along the urban-rural gradient in relation to settlement types in two regions in Lower Saxony; to identify the interrelations between good accessibility by public transport and population density; to identify opportunities and challenges of urban-rural public transport accessibility. The application of the settlement classification system TOPOI – developed to analyse and define settlement patterns in urban-rural gradients – enabled us to determine the quality of public transport. Furthermore, we newly developed the ‘Public Transport Access Score’ based on parameters such as available public transport modalities, their catchment areas, operating frequencies, and connectivity and applied it to demonstrate the level of accessibility for different settlement types of similar characteristics (TOPOI). Hence, we localised how sustainable accessibility of public transport can benefit the development of the urban–rural network.