Development of an Ontology-Based Visual Approach for Property Data Analytics

oai:ojs.pkp.sfu.ca:article/4Real 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

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

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

Visual sensitivity analysis in real estate prediction system

Visual Sensitivity analysis has proven its feasibility in data exploration and exposing relationships between the variables in the model. However, it lacks the ability of user direct interaction of outputs. To overcome this drawback for this interdisciplinary field, in this paper we introduce a novel approach of integrating an interactive visualization and a sensitivity analysis method into a visual interactive sensitivity analysis environment. This environment is applied to a property prediction system. The interactive visualization will facilitate user interaction with the sensitivity analysis method to adjust the outputs of the analysis procedure. The results are presented graphically to the non-expert users without the need to a prior knowledge of the sensitivity analysis method being used. Additionally, all the results that the non-expert is presented with, are all compiled together and presented to an expert user for further analysis by means of multidimensional scatter plots. © 2013 IEEE