Housing Potential Common Data Model and City Digital Twin Framework
A new research paper presents the Housing Potential Common Data Model (HPCDM), designed to merge various datasets for analyzing housing potential and tackling data silos in urban planning. This model enhances interoperability among zoning, land use, population attributes, and service accessibility data. Additionally, a City Digital Twin focused on housing and a pilot dashboard application showcase its real-world application. The research also highlights challenges to adoption and suggests strategies for urban planners and stakeholders to overcome these obstacles.
Key facts
- The Housing Potential Common Data Model (HPCDM) is introduced to overcome data silos.
- HPCDM serves as a standard for integration and interoperability across datasets.
- Datasets include zoning, land use, population characteristics, and access to services.
- A City Digital Twin for housing was created as part of the research.
- A pilot dashboard application demonstrates practical implementation.
- The research identifies critical barriers to adoption.
- Actionable mitigation strategies are provided for urban planners and stakeholders.
- The paper is categorized under Computer Science > Artificial Intelligence.
Entities
Institutions
- arXiv