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.

Space Syntax comprises a set of techniques that emphasize both material and immaterial characteristics of urban space. However, its inherent lack of a time component and land-use variables limits its effectiveness in investigating crime dynamics at the micro-urban scale. On the other hand, ABMs are simulations of interacting agents capable of perceiving their environment, influencing each other, and making autonomous decisions without central control, whose behaviour depends on time and environmental components. Nonetheless, ABMs face challenges in terms of the required amount of input data and model transparency, as well as regards the matter of validation. Despite being based on different modelling approaches – embodying the top–down versus bottom–up contraposition – both Space Syntax and ABMs can qualitatively and quantitatively analyse crucial components for crime analysis, such as flows, copresence, and visibility, deemed key aspects underlying the crime opportunity concept. This paper establishes a bridge between Space Syntax and ABM through the study of crime in urban settings, scrutinising the fundamental metrics that can significantly contribute to the identification of high-risk locations – specifically, pedestrian flow and visibility. It presents two new models, respectively, using Space Syntax methods and ABM, applied to two separate case studies in the historic city centre of Pisa, Italy. It has three objectives: first, to compare the diverse outcomes the distinct approaches provide in terms of people flow and visibility estimation in the built environment; second, to discuss their potential in forecasting risky areas; and, lastly, to propose an integration between Space Syntax metrics for movement within ABMs as parameters to guide agents’ movement. In essence, this study proposes a methodology oriented towards creating a model capable of simulating the relations between people’s behaviour, urban configuration, environmental conditions, and crime distribution, thereby representing a useful decision-support tool for crime prevention purposes and for broadly exploring and modelling pedestrian behaviour.