Development of an ontology-based visual approach for property data analytics

Ge, XJ; Zhang, J

Development of an ontology-based visual approach for property data analytics

Ge, XJ Zhang, J

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Publisher:: Penerbit UTM Press
Publication Type:: Journal Article
Citation:: International Journal of Real Estate Studies, 2021, 15, (1), pp. 1-14
Issue Date:: 2021-06-01

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Field	Value	Language
dc.contributor.author	Ge, XJ
dc.contributor.author	Zhang, J
dc.date.accessioned	2021-06-23T14:12:15Z
dc.date.available	2020-12-27
dc.date.available	2021-06-23T14:12:15Z
dc.date.issued	2021-06-01
dc.identifier.citation	International Journal of Real Estate Studies, 2021, 15, (1), pp. 1-14
dc.identifier.issn	1823-8505
dc.identifier.uri	http://hdl.handle.net/10453/149735
dc.description.abstract	Real estate is a complex market that consists of many layers of social, financial and economic data, including but not limited to price, rental, location, mortgage, demographic and housing supply data. The sheer number of real estate properties around the world means that property transactions produce an extraordinary amount of data that is increasing exponentially. Most of the data are presented through thousands of rows on a spreadsheet or described in long paragraphs that are difficult to understand. The emergent data visualisation techniques are intended to allow data to be processed and analytics to be displayed visually to enable an understanding of complex information and the identification of new patterns from the data. However, not all visualisation techniques can achieve such a thing. Most techniques are able to display only visual low-dimensional data. This paper introduces an ontology visualisation methodology to explore the ontologies of property data behaviour for multidimensional data. The visualisation combines real estate data statistical analysis with several high-dimensional data visualisation techniques, including parallel coordinates and stacked area charts. By using six residential suburbs in Sydney as a demonstration, we find that the developed data visualisation methodology can be applied effectively and efficiently to analyse complex real estate market behaviour patterns.
dc.language	English
dc.publisher	Penerbit UTM Press
dc.relation.ispartof	International Journal of Real Estate Studies
dc.relation.uri	https://intrest.utm.my/index.php/intrest/index
dc.relation.uri	https://intrest.utm.my/index.php/intrest/index
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Development of an ontology-based visual approach for property data analytics
dc.type	Journal Article
utslib.citation.volume	15
utslib.location.activity	Malaysia
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2021-06-23T14:12:13Z
pubs.issue	1
pubs.publication-status	Accepted
pubs.volume	15
utslib.citation.issue	1

Abstract:

Real estate is a complex market that consists of many layers of social, financial and economic data, including but not limited to price, rental, location, mortgage, demographic and housing supply data. The sheer number of real estate properties around the world means that property transactions produce an extraordinary amount of data that is increasing exponentially. Most of the data are presented through thousands of rows on a spreadsheet or described in long paragraphs that are difficult to understand. The emergent data visualisation techniques are intended to allow data to be processed and analytics to be displayed visually to enable an understanding of complex information and the identification of new patterns from the data. However, not all visualisation techniques can achieve such a thing. Most techniques are able to display only visual low-dimensional data. This paper introduces an ontology visualisation methodology to explore the ontologies of property data behaviour for multidimensional data. The visualisation combines real estate data statistical analysis with several high-dimensional data visualisation techniques, including parallel coordinates and stacked area charts. By using six residential suburbs in Sydney as a demonstration, we find that the developed data visualisation methodology can be applied effectively and efficiently to analyse complex real estate market behaviour patterns.

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http://hdl.handle.net/10453/149735