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The UDSA ontology: An ontology to support real time urban sustainability assessment

Kuster, Corentin, Hippolyte, Jean-Laurent and Rezgui, Yacine 2020. The UDSA ontology: An ontology to support real time urban sustainability assessment. Advances in Engineering Software 140 , -. 10.1016/j.advengsoft.2019.102731
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Abstract

Urban sustainability assessment frameworks have emerged during the past decade to address holistically the complexity of the urban landscape through a systems approach, factoring in environmental, social and economic requirements. However, the current assessment schemes are (a) static in nature, and as such don't reflect the dynamic and real-time nature of urban artefacts, (b) are not grounded in semantics (e.g. BIM and GIS), and (c) are at best used to assist in regulatory compliance, for instance in energy design, to meet increasingly stringent regulatory requirements. Information and communication technologies provide a new value proposition capitalizing on the Internet of Things (IoT) and semantics to provide real-time insights and inform decision making. Consequently, there is a real need in the field for data models that could facilitate data exchange and handle data heterogeneity. In this study, a semantic data model is considered to support near real-time urban sustainability assessment and enhance the semantics of sensor network data. Based on an extensive review of urban sustainability assessment frameworks and ontology development methodologies, the Urban District Sustainability Assessment (UDSA) ontology has been developed and validated using real data from the site of “The Works”, a newly refurbished neighbourhood in Ebbw Vale, Wales. This novel approach reconciles several domain-specific ontologies within one high-level ontology that can support the creation of real-time urban sustainability assessment software. In addition, this information model is aligned with 29 authoritative urban sustainability assessment frameworks, thus providing a useful resource not only in urban sustainability assessment, but also in the wider smart cities context.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0965-9978
Date of First Compliant Deposit: 14 November 2019
Date of Acceptance: 17 October 2019
Last Modified: 15 Mar 2020 07:59
URI: http://orca-mwe.cf.ac.uk/id/eprint/126817

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