Skip to main content
Log in

A Web-based visual analytics system for real estate data

  • Research Paper
  • Special Focus
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Real estate is an important industry in most countries. However, the analysis of the real estate market is very challenging as the data are high dimensional and have complex spatial and temporal patterns. In this paper, we present a novel Web-based visual analytics system, which integrates state-of-the-art interactive visualizations to enable end users to create their own visualizations and gain insight into the real estate market. The system is implemented using the new features in HTML5, which are natively supported in current browsers. We adopt a coordinated view design in our system consisting of four major components: a map view to show the geographical information of houses, a stacked graph view to show the evolution of house sales over time, a pixel-bar view to visualize multiple attributes of houses, and a treemap view to present the hierarchical structure of the data. Novel clutter reduction methods and rich user interactions are further proposed to enhance the flexibility and analytical ability of the whole system. We have applied our system to real property market data and obtained some interesting findings. Moreover, feedback from the end users of our system is very positive.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. MacEachren A, Kraak M. Research challenges in geovisualization. Cartogr Geogr Inf Sci, 2001, 28: 3–12

    Article  Google Scholar 

  2. Takatsuka M, Gahegan M. GeoVISTA Studio: a codeless visual programming environment for geoscientific data analysis and visualization. Comput Geosci, 2002, 28: 1131–1144

    Article  Google Scholar 

  3. Hardisty F, Robinson A. The GeoViz Toolkit: Using component-oriented coordination methods for geographic visualization and analysis. Int J Geogr Inf Sci, 2011, 25: 191–210

    Article  Google Scholar 

  4. Malik A, Maciejewski R, Maule B, et al. A visual analytics process for maritime resource allocation and risk assessment. In: IEEE Conference on Visual Analytics Science and Technology, Providence, 2011. 221–230

    Google Scholar 

  5. Slingsby A, Dykes J, Wood J. Configuring hierarchical layouts to address research questions. IEEE Trans Vis Comput Graph, 2009, 15: 977–984

    Article  Google Scholar 

  6. Havre S, Hetzler B, Nowell L. ThemeRiver: visualizing theme changes over time. In: IEEE Symposium on Information Visualization, Salt Lake, 2000. 115–123

    Google Scholar 

  7. Byron L, Wattenberg M. Stacked Graphs—Geometry & Aesthetics. IEEE Trans Vis Comput Graph, 2008, 14: 1245–1252

    Article  Google Scholar 

  8. Keim D, Hao M C, Ladisch J, et al. Pixel bar charts: a new technique for visualizing large multi-attribute data sets without aggregation. In: IEEE Symposium on Information Visualization, San Diego, 2001. 113–120

    Google Scholar 

  9. Ziegler H, Jenny M, Gruse T, et al. Visual market sector analysis for financial time series data. In: IEEE Symposium on Visual Analytics Science and Technology, Salt Lake, 2010. 83–90

    Chapter  Google Scholar 

  10. Liao T W. Clustering of time series data—a survey. Pattern Recognit, 2005, 38: 1857–1874

    Article  MATH  Google Scholar 

  11. Liao T W, Bolt B, Forester J, et al. Understanding and projecting the battle state. In: Army Science Conference, Orlando, 2002. 2–5

    Google Scholar 

  12. Fu T, Chung F, Luk R, et al. Financial time series indexing based on low resolution clustering. In: 4th IEEE International Conference on Data Mining, Brighton, 2004. 5–14

    Google Scholar 

  13. Oates T, Firoiu L, Cohen P R. Clustering time series with hidden Markov models and dynamic time warping. In: Proceedings of the IJCAI-99 Workshop on Neural, Symbolic, and Reinforcement Learning Methods for Sequence Learning, Stockholm, 1999. 17–21

    Google Scholar 

  14. Wang L, Mehrabi M G, Kannatey-Asibu E. Hidden Markov model-based tool wear monitoring in turning. J Manuf Sci Eng-Trans ASME, 2002, 124: 651–658

    Article  Google Scholar 

  15. MacQueen J. Some methods for classification and analysis of multivariate observations. In: 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, 2007. 281–297

    Google Scholar 

  16. Harrower M, Brewer C A. ColorBrewer.org: An online tool for selecting colour schemes for maps. Theor Mapp Pract Cartogr Represent, 2003, 40: 27–37

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to RongHua Liang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sun, G., Liang, R., Wu, F. et al. A Web-based visual analytics system for real estate data. Sci. China Inf. Sci. 56, 1–13 (2013). https://doi.org/10.1007/s11432-013-4830-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-013-4830-9

Keywords

Navigation