An ontology-based spatial data harmonisation for urban analytics

Abstract

Data heterogeneity is one of the most challenging problems in urban data analytics. When obtained from various providers or custodians, datasets for the same domain themes may dramatically differ in formats due to many reasons such as historical legacies, changing definitions or standards across jurisdictions etc. It hinders urban analysts and researchers from understanding and using these data and makes results comparison and interpretation obscure. Ontology, usually created by domain experts, offers a comprehensive representation of knowledge including concepts, relations and properties in a domain. It defines the real world in abstract and offers a universal and stable schema for data har..