Cityseer-api is a Python package consisting of computational tools for fine-grained street-network and land-use analysis, helpful in assessing the morphological precursors to vibrant neighbourhoods. It is underpinned by network-based methods developed specifically for urban analysis at the pedestrian scale. cityseer-api computes a variety of node and segment-based network centrality methods, land-use accessibility and mixed-use measures, and statistical aggregations. Accessibilities and aggregations are computed dynamically over the street-network while taking walking distance thresholds and the direction of approach into account, and can optionally incorporate spatial impedances and network decomposition to increase spatial precision. The use of Python facilitates compatibility with popular computational tools for network manipulation (NetworkX), geospatial topology (shapely), geospatial data state management (GeoPandas), and the NumPy stack of scientific packages. The provision of robust network cleaning tools aids the use of OpenStreetMap data for network analysis. Underlying loop-intensive algorithms are implemented in Numba JIT compiled code so that the methods scale efficiently to larger cities and regions. Online documentation is available from cityseer.benchmarkurbanism.com, and the Github repository is available at github.com/benchmark-urbanism/cityseer. Example notebooks are available at cityseer.benchmarkurbanism.com/examples/

The increasing complexity of urban environments has exposed the limitations of prescriptive approaches in urban design and planning, highlighting the need for more adaptive, data-informed, and methodologically rigorous processes. Evidence-Based Design and Planning (EBDP) offers a promising response by embedding evidence as a continuous and iterative element throughout design and decision-making. Yet, its adoption in practice remains uneven, constrained by project limitations, data availability, and the challenge of operationalising analytical workflows. This paper addresses these challenges by proposing a transferable framework for EBDP, developed through the review of six realised projects, ranging from public space enhancements to metropolitan masterplans and policy studies, undertaken in both professional practice and academic research. Examined alongside existing theoretical models, these cases revealed recurring patterns that informed the framework. The resulting model consists of four interlinked phases: clarification and evidence-based project definition; integration of an evidence base through analysis and modelling; generation of options synthesising diverse evidence; and evaluations to guide adaptation and decision-making. Rather than a linear or prescriptive sequence of stages, the framework uses iteration and flexible feedback processes anchored by a unifying Hybrid Spatial Model to synthesise evidence, support the generation of design options, and underpin engagement and feedback processes considering project objectives. This paper offers a systematic yet flexible framework for EBDP that can be adapted across scales, project types, and contexts.

This study evaluates the extent to which fully automated street network modelling can reproduce the analytical outcomes of a hybrid (automated–manual) approach for centrality and accessibility analysis across four morphologically distinct cities: Nicosia, London, Gothenburg, and Madrid. Results show that the geometry-preserving segmentation logic of the automated workflow systematically increases network granularity relative to the continuity-merging rules applied in the hybrid model, producing higher node densities and shorter street segments, particularly in cities with irregular, historically layered street patterns. Despite these geometric differences, angular integration exhibits strong to near-perfect rank correlation between hybrid and automated models across all cases, with agreement increasing at larger spatial radii. This indicates that global configurational structure is robust in automated simplification. Angular betweenness displays lower, though still moderate to high, correspondence, especially at smaller radii, reflecting its sensitivity to over-segmentation and local geometric variation. Spatial autocorrelation analysis reveals that ranking differences are strongly clustered in footpaths in open green spaces and detailed paths in residential areas. However, the differences are more locally confined and diminish at larger radii. Accessibility results further demonstrate scale-dependent divergence: short-range attraction reach and attraction distance estimates align closely between models, whereas discrepancies increase at larger thresholds due to the cumulative effects of finer-grained connectivity and additional pedestrian paths in automated networks. Overall, the findings indicate that automated street network models reliably capture large-scale integration and accessibility patterns across diverse urban contexts, while local, flow-sensitive measures remain more affected by network granularity and path representation. These results support the use of automated workflows for scalable and comparative urban analysis, with targeted manual refinement reserved for contexts requiring high local precision.

The National Buildings Database (NBD) has been delivered to the UK Department for Energy Security and Net Zero (DESNZ). This covers the complete population of 2.2 million non-domestic premises and 30 million dwellings in Great Britain (but not Northern Ireland). Each is modelled in detail, in three dimensions, using the 3DStock method. Actual annual meter data are attached. This paper outlines some of the myths about the building stock—its composition, representation, age, total floor area, activity classification and energy use—that the authors have encountered in their 20 years of working on stock models, consumption of energy and strategies for decarbonisation. The NBD can dispel these myths and the confusion they cause if they are unknowingly perpetuated. Nine myths are described and disproved based on arguments and data from the NBD. Light is thrown on the true nature of the British building stock, in particular non-domestic and mixed-use buildings. New perspectives and methods of analysis are possible with a database that covers all buildings. POLICY RELEVANCE The NBD has been designed to support better evidenced development and targeting of policies and regulation on buildings. It allows for specific segments and subsets of the stock to be analysed. The rich detail combining geometry, building attributes, and activity allows for policy options to be developed and assessed based on the actual premises and their features. The spatial nature of the data allows for localised energy planning and decarbonisation action. Some of the ‘myths’, if acted on, could lead to wasteful or misdirected policies. Examples are given, including one that directly illustrates a case of the NBD being used to correct previous policy assumptions. The NBD makes it possible to avoid these dangers. Data from the NBD have been made publicly available, so that local authorities, community groups, and consulting and industrial firms can have a firmer statistical basis on which to make policies and plans.