Housing Potential Common Data Model and City Digital Twin
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866909020183330816 |
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| author | Katsumi, Megan Fox, Mark Wong, Anderson Chatha, Divnoor |
| author_facet | Katsumi, Megan Fox, Mark Wong, Anderson Chatha, Divnoor |
| contents | The evaluation of housing potential requires consideration of a location from multiple perspectives, ranging from zoning and land use to population characteristics and access to services. This research introduces the Housing Potential Common Data Model (HPCDM) to overcome existing data silos, serving as a standard to support integration and interoperability across the diverse range of datasets that are required for housing potential analysis. This report details the evaluation of the model along with the creation of a City Digital Twin for housing and a pilot dashboard application to demonstrate a practical implementation. Beyond the technical framework, this work identifies critical barriers to adoption and provides actionable mitigation strategies for urban planners and stakeholders. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05535 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Housing Potential Common Data Model and City Digital Twin Katsumi, Megan Fox, Mark Wong, Anderson Chatha, Divnoor Artificial Intelligence I.2.4 The evaluation of housing potential requires consideration of a location from multiple perspectives, ranging from zoning and land use to population characteristics and access to services. This research introduces the Housing Potential Common Data Model (HPCDM) to overcome existing data silos, serving as a standard to support integration and interoperability across the diverse range of datasets that are required for housing potential analysis. This report details the evaluation of the model along with the creation of a City Digital Twin for housing and a pilot dashboard application to demonstrate a practical implementation. Beyond the technical framework, this work identifies critical barriers to adoption and provides actionable mitigation strategies for urban planners and stakeholders. |
| title | Housing Potential Common Data Model and City Digital Twin |
| topic | Artificial Intelligence I.2.4 |
| url | https://arxiv.org/abs/2605.05535 |