Federico Mara

The X-minute city has gained prominence as a planning paradigm for promoting sustainable local living, yet its operationalisation remains methodologically heterogeneous. This paper conducts a systematic review (Scopus, last search 4 March 2026) of 45 quantitative, proximity-based studies to examine how modelling decisions shape X-minute city assessments. Using reflexive thematic analysis, this review shows that proximity “as-modelled” is not an inherent property of urban space but a construct produced through a sequence of interdependent decisions concerning: analysis scope; functional inclusion; analysis approach; spatial representation; modelling variables; and assessment logic. These decision cascades, often implicit or inconsistently reported, generate substantial variation in results and limit the comparability and transferability of existing X-minute city analyses. The paper identifies connections between decision domains, examines how upstream assumptions influence downstream analytical possibilities, and highlights dominant modelling pathways and methodological divergences. Beyond proposing future directions (e.g., use of multi-threshold scenarios, equity-sensitive parameters, hybrid data strategies, and uncertainty/sensitivity reporting), the study provides a grounded baseline framework and guidance for documenting modelling processes. This research ultimately supports more transparent, reproducible, and context-sensitive proximity assessments, thereby contributing both conceptually and practically to the robustness and policy relevance of X-minute city studies.

Urban models support sustainable, resilient, and equitable planning, but their validity hinges on underlying spatial data. This study examines the epistemological and technical consequences of relying on two dominant yet divergent platforms—OpenStreetMap (OSM) and Google Maps—for extracting proximity-based amenities within the 15-min city framework. Across four European contexts—Versilia, Gothenburg, Nice, and Vienna—we compare (i) data completeness and spatial coverage; (ii) semantic categories; and (iii) the effects of data heterogeneity on accessibility modelling. Findings show that OSM, while semantically consistent and openly accessible, systematically underrepresents peripheral amenities, introducing bias towards urban cores in accessibility metrics. Conversely, Google Maps provides broader coverage but is constrained by dependencies on extraction methods, opaque data structures, and ambiguous classification schemes, which hinder reproducibility, reduce interpretability, and limit its analytical robustness. These divergences yield distinct accessibility landscapes and competing readings of functionality and spatial equity. We argue that data source choice and protocol design are epistemological decisions and advocate transparent, hybrid strategies with cross-platform semantic harmonisation to strengthen robustness, equity, and policy relevance.