The design and practice of a semantic-enabled urban analytics data infrastructure. (May 2020)

• Record Type: Journal Article
• Authors: Chen, Yiqun; Sabri, Soheil; Rajabifard, Abbas; Agunbiade, Muyiwa Elijah; Kalantari, Mohsen; Amirebrahimi, Sam
• Is Part Of: Computers, environment and urban systems. Volume 81(2020)
• Publication Date: 2020-05
• Subjects: Urban analytics -- Ontology -- Semantic enrichment -- OGC standards; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2020.101484
• Languages: English
• ISSNs: 0198-9715

Abstract

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 consolidate heterogeneous datasets into a uniformed structure. This is presented in ontology schemas and hence offers a new means for developing generic tools for urban analytics. By applying bi-level mapping between data and ontology, the datasets are semantically enriched. This strategy allows data harmonisation, thus, heterogeneity problems could be mitigated. When building an analytics tool, researchers can simply declare the input data type as a specific concept of ontology and then follow the ontology schema to implement the code. The developed tool can be registered into the UADI system and then can work with any data mapped to the concept. The core components of the system include data registration, data enrichment, ontology management, translation engine, tool development and tool management. These are elaborately designed and developed to meet the design goals. The system usability and performance are validated by building a series of ISO 37120 indicators (for city services and quality of life) within the UADI. Highlights: An innovative urban analytics platform is proposed, using ontologies for data harmonisation and analytics tools development. It explains the practice and benefits of utilising ontology in the real world to overcome urban data analysis challenges. With a service-oriented design, the system comprises the six components utilising ontologies respectively. Three ISO37120(City Services and Quality of Life) indicators were built to demonstrate its usablilty and performance.