Framework for Assessing Building Potential Based on the Relational Model of the Multipurpose Cadastre

Abstract

Effective building potential is a critical metric for evidence-based urban planning, offering a comprehensive understanding of what is legally permissible, spatially feasible, and actually achievable on urban land. Unlike simplified theoretical estimates, this potential must account for multiple regulatory constraints, including environmental restrictions, urban planning rules, and legal incentives. This paper presents a relational model for a Multipurpose Cadastre (MTC) designed to accurately structure and spatialize this effective building potential. The core innovation lies in systematically linking land parcels to applicable Legal Territorial Objects (LTOs), which represent spatialized normative constraints and rights. The model's structure aligns conceptually with the Land Administration Domain Model (LADM – ISO 19152) and adheres to Brazilian national guidelines (Federal Ordinance MDR n° 3.242/2022), ensuring its conceptual soundness and interoperability. The methodology, which calculates the Effective Floor Area Ratio (FAReff) and the Effective Buildable Area (EBA) by combining regulatory factors, was successfully tested in a case study in Florianópolis, Brazil. The application demonstrated the model’s technical feasibility and its capacity to generate consistent, multi-scalar spatial indicators. Key findings revealed significant disparities between theoretical zoning potential and actual buildable capacity, with a large concentration of potential in only a few areas of the city. Operationally, the model required migration to a robust geospatial relational database environment (PostgreSQL/PostGIS) to manage the high volume and complexity of spatial data, overcoming performance limitations encountered in desktop GIS software. The results confirm the model's utility as a technical basis for territorial management, supporting the identification of regulatory gaps, the monitoring of urban instruments, and the design of targeted densification policies. This relational framework is highly replicable, scalable to other urban indicators, and essential for advancing evidence-based, sustainable urban governance.