A spatial agent-based modeling to allocate land use for brownfield sites on an urban scale

• Tehran study maps 110 brownfields, modeling land use with 39,551 parcels. • Land use allocation uses entropy, suitability, and zoning constraints. • Dynamic Python model supports sustainable urban growth and decision-making. • Agent-based GIS model boosts brownfield redevelopment in urban planning. Cities worldwide face rapid growth, necessitating improved land use planning and redevelopment strategies. Brownfield sites—abandoned, contaminated, or underused parcels within urban areas—present challenges and opportunities for sustainable urban development. Redeveloping these lands can improve environmental and social indicators and enhance the overall quality of life. This study proposes a spatial agent-based model to support the allocation of appropriate future land use subclasses to previously identified brownfield parcels, using Geographic Information Systems (GIS) and a rule-based, parcel-level ABM implemented in Python (Mesa-Geo). The model employs a fully vector-based structure with cadastral parcels as computational decision units (agents). The research was conducted in Region 7 of Tehran, encompassing 39,551 parcels, of which 110 were identified as brownfields. Land use subclasses were allocated based on planning indicators such as dependency, consistency, entropy, physical suitability, and per-capita service indicators, while zoning codes and parcel-size thresholds act as exogenous spatial constraints. The findings demonstrate the model’s effectiveness in supporting planning-oriented sustainable redevelopment through spatially informed, indicator-driven land use decisions. The approach provides urban planners with a transparent, replicable tool for improving environmental, social, and spatial conditions in data-scarce urban contexts.