The expansion of accessible, fine-grained city data has significantly increased opportunities for evidence-based and informed policy-making. Despite this evolution, extracting actionable insights from heterogeneous data sources and effectively communicating findings remain persistent challenges. Most existing visualisation approaches and research prioritise technical implementation by focusing on how to visualise, often neglecting the importance of policy-driven visualisation questions and data contexts. This led to flawed analyses, particularly in complex domains such as smart cities and urban policy-making using digital twins. This article presents a novel, practical, step-by-step policy visualisation methodology grounded in empirical smart city research, shifting the emphasis toward policy-element-based questions informed by data-informed evidence. The methodology was successfully applied, tested, and adapted, resulting in an implementable, structured, and integrative approach that aligns with policymakers’ established policy design, implementation, and evaluation cycles. Through this approach, 20 user-driven smart city policy visualisations were operationalised and implemented in strategic policy decision-making contexts across smart city domains, including mobility, spatial planning, and environment. The results demonstrate how dashboards, algorithmic simulations, and digital twins visualisations can be systematically deployed to support evidence-informed decision-making.
Raes et al. (2026) studied this question.