Open AccessDevelopment of an Ontology-Based Visual Approach for Property Data Analytics
Author(s) -
Xin Janet Ge,
Jinson Zhang
Publication year - 2021
Publication title -
malaysian journal of real estate/international journal of real estate studies
Language(s) - English
Resource type - Journals
eISSN - 2231-7643
pISSN - 1823-8505
DOI - 10.11113/intrest.v15n1.4
Subject(s) - visualization , real estate , computer science , visual analytics , ontology , data science , data visualization , property (philosophy) , parallel coordinates , data mining , data analysis , renting , analytics , identification (biology) , interactive visual analysis , bar chart , finance , engineering , philosophy , statistics , civil engineering , botany , mathematics , epistemology , biology , economics
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 visualization 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 visualization 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.