Multi-criteria site selection using an ontology: the OntoZoning ontology of zones, land uses and programmes for Singapore

Abstract

Data related to urban planning is diverse both in terms of sources and formats. To facilitate urban analyses and public access to regulatory information, greater data interoperability is needed. Semantic web technologies, which use ontologies to link diverse data, are a promising solution to this problem. In this paper, we describe OntoZoning, an ontology representing relationships between zoning types, land uses and programmes (more specific land uses) in Singapore. We link the ontology to geospatial data stored in a knowledge graph, which allows executing multi-domain queries on urban data. We demonstrate how such queries can improve access to urban data, and in particular facilitate site selection and exploration. These are common tasks in urban planning and urban development processes. We also discuss how certain parts of zoning regulations are difficult to represent through ontologies, and would likely need to be defined more explicitly to fully represent city planning knowledge digitally.