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Australasian Database Conference

ADC 2016: Databases Theory and Applications pp 435–439Cite as

Visualization-Aided Exploration of the Real Estate Data

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9877))

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/.

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Notes

  1. 1.

    http://www.realestate.com.au/.

  2. 2.

    http://www.domain.com.au/.

  3. 3.

    http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/2901.0Chapter23102011.

  4. 4.

    https://bettereducation.com.au/results/vce.aspx.

References

  1. Javed, W., Elmqvist, N.: Exploring the design space of composite visualization. In: IEEE PacificVis (2012)

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  2. Li, M., Bao, Z., Yan, S., Sellis, T.: A visual analytics framework for the housing estate data. In: IEEE PacificVis (Poster) (2016)

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  3. 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

    Google Scholar 

  4. 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)

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Author information

Authors and Affiliations

  1. RMIT University, Melbourne, Australia

    Mingzhao Li, Zhifeng Bao & Shi Yan

  2. Swinburne University of Technology, Melbourne, Australia

    Timos Sellis

Authors
  1. Mingzhao Li
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  2. Zhifeng Bao
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  3. Timos Sellis
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  4. Shi Yan
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Corresponding author

Correspondence to Mingzhao Li .

Editor information

Editors and Affiliations

  1. Monash University , Clayton, Australia

    Muhammad Aamir Cheema

  2. School of Comp. Science a. Engineer, University of New South Wales School of Comp. Science a. Engineer, Sydney, Australia

    Wenjie Zhang

  3. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

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© 2016 Springer International Publishing AG

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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

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  • 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)

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