مدل‌سازی اطلاعات شهر به‌عنوان چارچوب پشتیبان تصمیم در برنامه‌ریزی و مدیریت شهری

Introduction: Urban decision-making increasingly relies on rich, fast-arriving spatial and non-spatial data, yet current practices remain fragmented, with geographic information systems (GIS), building information modeling (BIM), and dashboards operating in parallel rather than as a coherent whole. This paper advances City Information Modeling (CIM) as a holistic decision-support framework that integrates heterogeneous datasets, links models and indicators across multiple scales (parcel–district–city) and sectors (land use, mobility, energy, public health), and operationalizes participatory, evidence-based planning. CIM is not merely a 3D representation nor a city-scale extension of BIM; it is a socio-technical information infrastructure coupling semantics, geometry, and time with governance mechanisms, institutional workflows, and citizen engagement.
 
The Purpose of the Research: Our analytical lens foregrounds decision support: Which functions are enabled at strategic, tactical, and operational levels (scenario building, cross-agency coordination, real-time monitoring)? What benefits and barriers recur? Which enabling conditions move efforts from visualization to institutionalized decision-making? The paper makes three contributions.
 
Methodology: Methodologically, the study combines a conceptual literature review with a comparative analysis of three prominent implementations—Virtual Singapore, Zurich’s city-scale digital twin, and Boston’s 3D GIS model. The review clarifies CIM’s relationship to GIS, BIM, and urban digital twins, and the cases illustrate how governance models, data standards, funding arrangements, and platform choices shape what CIM can achieve in practice.
 
Findings and Discussion: First, it delineates CIM’s theoretical underpinnings vis-à-vis GIS, BIM, and digital twins. GIS contributes coverage and spatial analytics at urban and regional scales; BIM adds parametric depth at the asset scale; digital twins provide synchronization with live data and operational control. CIM orchestrates these capabilities through shared ontologies and open standards (e.g., CityGML, IFC), creating a common semantic backbone enabling indicators and models to interoperate across tools, agencies, and time horizons. Second, it specifies decision functions by level. At the strategic level, CIM supports long-range scenario exploration, policy trade-off analysis, and cross-sector alignment. At the tactical level, it enables program design and interdepartmental coordination for area-based redevelopment. At the operational level, it underpins near-real-time monitoring, asset management, and crisis response, especially where architectures are coupled to live sensor streams. Third, it surfaces recurrent benefits and barriers. Benefits include a coherent information infrastructure, enhanced transparency and public communication (through interactive 3D/VR/AR and web dashboards), improved predictiveness via what-if simulations and multi-criteria decision analysis (MCDA), and reduced duplication across agencies. Barriers include data silos and legacy formats, limited interoperability, vendor lock-in risks, uneven organizational capacity, and ethics and privacy concerns surrounding sensitive spatial data. Building on these insights, the paper proposes actionable policy and practice recommendations. Cities should adopt and enforce common data and metadata standards; implement open yet safeguarded data governance with role-based access and auditable provenance; invest in cross-agency capacity-building (data stewardship, 3D modeling, simulation, engagement); and sequence deployment through domain-specific pilots explicitly linking analytics to implementation. A staged approach—prioritizing foundational layers, establishing semantic alignment, and progressively integrating live data—can deliver early value while managing risk and cost. Procurement should minimize lock-in, favor interoperability, and require documentation of APIs, schemas, and governance processes alongside deliverables.
 
Conclusion: The paper’s originality lies in reframing CIM as connective tissue between models, data, institutions, and publics—a socio-technical platform that augments rather than replaces expert judgment. By tying semantic modeling to decision functions and governance safeguards, we move beyond technology-first narratives to show how CIM enables more coordinated, transparent, and democratically legitimate urban decisions. The framework clarifies how multi-scale (parcel–district–city) and multi-sector (land use, mobility, energy, public health) integration can be operationalized through shared ontologies and interface layers supporting both expert analysis and lay participation. Finally, we chart a research agenda prioritizing evaluation metrics focused on decision quality and implementation outcomes (not only visualization fidelity); advancing participatory modeling at scale through accessible web/VR interfaces and place-based feedback; and integrating responsible AI for city analytics, with attention to bias, explainability, and data minimization in sensitive contexts. Comparative studies across resource-rich and resource-constrained settings are needed to understand scalability and adaptation pathways, including low-cost, standards-led approaches that broaden access and reduce dependency on proprietary stacks. Centering decision support, governance, and ethics positions CIM as foundational infrastructure for the next generation of urban planning and management—fostering inclusive, transparent, equitable, resilient, and adaptive governance.