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孙国道, 梁荣华, 何贤国, 蒋莉, 于明远. 高维时空房地产数据的可视分析[J]. 计算机辅助设计与图形学学报, 2013, 25(8): 1169-1176.
引用本文: 孙国道, 梁荣华, 何贤国, 蒋莉, 于明远. 高维时空房地产数据的可视分析[J]. 计算机辅助设计与图形学学报, 2013, 25(8): 1169-1176.
Sun Guodao, Liang Ronghua, He Xianguo, Jiang Li, Yu Mingyuan. Visual Analysis on High Dimensional Spatio-Temporal Real Estate Data[J]. Journal of Computer-Aided Design & Computer Graphics, 2013, 25(8): 1169-1176.
Citation: Sun Guodao, Liang Ronghua, He Xianguo, Jiang Li, Yu Mingyuan. Visual Analysis on High Dimensional Spatio-Temporal Real Estate Data[J]. Journal of Computer-Aided Design & Computer Graphics, 2013, 25(8): 1169-1176.

高维时空房地产数据的可视分析

Visual Analysis on High Dimensional Spatio-Temporal Real Estate Data

  • 摘要: 高维房地产数据中包含着复杂的空间和时间趋势,为了使用户能够创建自己的可视化形式并理解房地产市场中的内容,提出基于HTML5的在线房地产信息的、包含4个组件的可视分析方法.该方法提出了基于楼盘地理位置聚类的可视化方法来展示楼盘地理信息的地学可视化组件,结合多种布局和排序方式来展示楼盘销售数量变化的堆栈图组件,基于楼盘销量和价格的聚类方法来展示楼盘多维属性的像素条图组件,并结合多种节点布局和排序的方式展示数据层次结构的树图组件;最后对各组件设计了良好的交互操作,丰富了系统的分析能力.文中方法已用于杭州市房地产的真实数据分析中,用户和专家反馈效果良好.

     

    Abstract: The analysis of the real estate market is very challenging as the data is high dimensional and has complex spatial and temporal patterns.To enable end users to create their own visualizations and gain insight into the real estate market,a novel HTML5-based visual analytics approach which integrates four interactive visualizations is proposed.The method includes a map view to show the geographical information of houses using location-based clustering,a stacked graph view to show the evolution of house sale over time with combinations of various layouts and orderings,a pixel-bar view to visualize multiple attributes of houses using sale and price-based clustering,and a treemap view to present the hierarchal structure of the data with combinations of various nodes layouts and orderings.Rich user interactions are further proposed to enhance the flexibility and analytical ability of the entire system.We have applied our method to the real property market data and obtained some interesting findings.The feedback from the end users of our system is very positive.

     

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