住宅ポテンシャル共通データモデルと都市デジタルツイン Housing Potential Common Data Model and City Digital Twin

This paper proposes a Common Data Model (CDM) for assessing housing potential in urban areas and discusses how it can be embedded within a City Digital Twin. Decisions about housing supply, densification and redevelopment depend on a wide range of inputs — building footprints and attributes, zoning and land-use classifications, demographics, infrastructure capacity, and regulatory constraints — yet these datasets are typically fragmented across municipal agencies and private providers, each using its own schema and update cadence.

The authors argue that unifying these heterogeneous sources under a shared schema enables a consistent pipeline for simulation, visualization and policy evaluation. A digital twin, in this framing, is a virtual representation of the physical city that mirrors its state in near real time. The proposed housing CDM sits on top of that twin as a machine-readable layer that answers the question of where, and how much, additional housing capacity exists under given constraints.

The broader context is a rapidly growing ecosystem of city-scale digital twin technologies. Standards such as OGC CityGML and the 3D Tiles specification, Europe's INSPIRE directive for spatial data, and commercial platforms like Microsoft Azure Digital Twins, NVIDIA Omniverse and Bentley iTwin all aim to provide reusable substrates for urban modeling. Flagship deployments such as Virtual Singapore and Helsinki's 3D city model demonstrate that municipal twins are moving from research prototypes toward operational tools. A housing-focused CDM can be seen as a domain layer that rides on top of such general-purpose infrastructure, translating geometric and semantic city data into planning-relevant indicators.

Key design questions that any such model must address include how to reconcile differing definitions of a dwelling unit across jurisdictions, how to encode regulatory envelopes such as height limits or floor-area ratios in a computable form, and how to keep the model synchronized as parcels are subdivided, buildings are permitted and demographics shift. Interoperability with existing GIS stacks — PostGIS, Esri ArcGIS, QGIS — and with open municipal data portals will likely determine practical adoption.

Readers should note that, based on the venue and presentation, this appears to be a conceptual or position paper rather than a fully validated system, so the maturity of implementation and the breadth of empirical testing may be limited. Even so, the direction is timely: housing affordability pressures in many metropolitan regions are pushing planners to seek more transparent, data-driven methods for identifying capacity, and a standardized CDM could plausibly reduce the integration overhead that currently slows such analyses. Future work will likely need to address privacy considerations around fine-grained residential data, governance of model updates, and alignment with emerging international digital twin reference architectures.