Analyse Property Data Through Visualisation

Ge, XJ; Zhang, J

Analyse Property Data Through Visualisation

Ge, XJ

Zhang, J

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Publisher:: PACIFIC RIM REAL ESTATE SOCIETY (PRRES)
Publication Type:: Conference Proceeding
Citation:: Proceedings from the PRRES Conference - 2019, 2019, pp. 1 - 1 (10)
Issue Date:: 2019-05-28

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Field	Value	Language
dc.contributor.author	Ge, XJ https://orcid.org/0000-0002-2252-7829	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0001-6580-1683	en_US
dc.date	2019-01-14	en_US
dc.date.issued	2019-05-28	en_US
dc.identifier.citation	Proceedings from the PRRES Conference - 2019, 2019, pp. 1 - 1 (10)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134177
dc.description.abstract	People’s activities create 2.5 Quintilian bytes of data every day (Marr, 2018). The examples of activities include shopping, sleeping, property purchasing, selling or leasing, etc. A large amount of data is usually with high-dimensional geometry and multivariate characters. Traditional text-based data may be able to record the facts of activities, but the hidden story behind the data may not be discovered. Data visualisation is an instrument for reasoning about quantitative information and allows us to analyse data behaviours by understanding data patterns, trends and correlations that could not be detected by the traditional text-based data. This paper focuses on analysing property data for six suburbs in Sydney using visualisation. Data with 31 elements from the three-year censuses were used to create visual patterns for analysis. Parallel coordinates and dashboard techniques are applied for data visualization for the selected six suburbs. The results suggest that the well-designed data graphics is a powerful tool, and property data visualisation provides us with visual access to huge amounts of data in easily digestible visuals.	en_US
dc.publisher	PACIFIC RIM REAL ESTATE SOCIETY (PRRES)	en_US
dc.relation.ispartof	Proceedings from the PRRES Conference - 2019	en_US
dc.relation.ispartof	25th Annual Pacific-RIM Real Estate Society Conference	en_US
dc.title	Analyse Property Data Through Visualisation	en_US
dc.type	Conference Proceeding
utslib.location	Online	en_US
utslib.location.activity	Melbourne, Australia	en_US
utslib.for	080401 Coding and Information Theory	en_US
utslib.for	080603 Conceptual Modelling	en_US
utslib.for	1202 Building	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
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	true	en_US
pubs.finish-date	2019-01-16	en_US
pubs.place-of-publication	Online	en_US
pubs.publication-status	Published online	en_US
pubs.start-date	2019-01-14	en_US

Abstract:

People’s activities create 2.5 Quintilian bytes of data every day (Marr, 2018). The examples of activities include shopping, sleeping, property purchasing, selling or leasing, etc. A large amount of data is usually with high-dimensional geometry and multivariate characters. Traditional text-based data may be able to record the facts of activities, but the hidden story behind the data may not be discovered. Data visualisation is an instrument for reasoning about quantitative information and allows us to analyse data behaviours by understanding data patterns, trends and correlations that could not be detected by the traditional text-based data. This paper focuses on analysing property data for six suburbs in Sydney using visualisation. Data with 31 elements from the three-year censuses were used to create visual patterns for analysis. Parallel coordinates and dashboard techniques are applied for data visualization for the selected six suburbs. The results suggest that the well-designed data graphics is a powerful tool, and property data visualisation provides us with visual access to huge amounts of data in easily digestible visuals.

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http://hdl.handle.net/10453/134177