Abstract
An efficient analysis of the real estate data is critical for buyers to understand the real estate market and seek appropriate properties to live in or rent. In this paper, we first collect data from different channels which are not provided in existing commercial real estate systems, and integrate them to build a location-centred comprehensive real estate dataset, including information other than the house itself, such as education profile, transportation profile and regional profile. Then we develop HouseSeeker, a visualization-aided system for buyers to explore the real estate data, find appropriate properties based on their individual requirements, and compare properties/suburbs from different aspects to discover the strengths and weaknesses of each property/suburb. We demonstrate the effectiveness of our system based on a real-world dataset in Melbourne metropolitan area: it is able to help zero-knowledge users better understand local real estate market and find preferred properties based on their individual requirements. A preliminary implementation of the system is available at http://115.146.89.158/.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Javed, W., Elmqvist, N.: Exploring the design space of composite visualization. In: IEEE PacificVis (2012)
Li, M., Bao, Z., Yan, S., Sellis, T.: A visual analytics framework for the housing estate data. In: IEEE PacificVis (Poster) (2016)
Sun, G., Liang, R., Qu, H., Wu, Y.: Embedding spatio-temporal information into maps by route-zooming. IEEE Trans. Vis. Comput. Graph. (2016). Early Access
Turkay, C., Slingsby, A., Hauser, H., Wood, J., Dykes, J.: Attribute signatures: dynamic visual summaries for analyzing multivariate geographical data. IEEE Trans. Vis. Comput. Graph. 20(12), 2033–2042 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Li, M., Bao, Z., Sellis, T., Yan, S. (2016). Visualization-Aided Exploration of the Real Estate Data. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-46922-5_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46921-8
Online ISBN: 978-3-319-46922-5
eBook Packages: Computer ScienceComputer Science (R0)