The design and practice of a semantic-enabled urban analytics data infrastructure

Abstract

The complexity, variety and volume of urban datasets have soared in the past decade. By utilising these datasets, urban planners and researchers have built a wide range of evidence-based methods and analytical tools for planning and decision-making purposes. However, the data heterogeneity remains one of the key problems in this process. Building generic urban analytics tools adaptable to diverse data formats remains largely unresolved in practice. This work proposes an innovative system called Urban Data Analytics Infrastructure (UADI) to tackle these challenges by leveraging on the advantages of ontology technologies. The proposed technique implements a bi-level mapping approach to consoli..