Attribute Signatures: Dynamic Visual Summaries for Analyzing Multivariate Geographical Data

被引:34
|
作者
Turkay, Cagatay [1 ]
Slingsby, Aidan [1 ]
Hauser, Helwig [2 ]
Wood, Jo [1 ]
Dykes, Jason [1 ]
机构
[1] City Univ London, Dept Comp Sci, London, England
[2] Univ Bergen, Dept Informat, N-5008 Bergen, Norway
关键词
Visual analytics; multi-variate data; geographic information; geovisualization; interactive data analysis; VISUALIZATION; ISSUES; GRAPHICS; SCALE; MODEL;
D O I
10.1109/TVCG.2014.2346265
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The visual analysis of geographically referenced datasets with a large number of attributes is challenging due to the fact that the characteristics of the attributes are highly dependent upon the locations at which they are focussed. and the scale and time at which they are measured. Specialized interactive visual methods are required to help analysts in understanding the characteristics of the attributes when these multiple aspects are considered concurrently. Here, we develop attribute signatures interactively crafted graphics that show the geographic variability of statistics of attributes through which the extent of dependency between the attributes and geography can be visually explored. We compute a number of statistical measures, which can also account for variations in time and scale, and use them as a basis for our visualizations. We then employ different graphical configurations to show and compare both continuous and discrete variation of location and scale. Our methods allow variation in multiple statistical summaries of multiple attributes to be considered concurrently and geographically, as evidenced by examples in which the census geography of London and the wider UK are explored.
引用
收藏
页码:2033 / 2042
页数:10
相关论文
共 50 条
  • [11] Visual Neural Decomposition to Explain Multivariate Data Sets
    Knittel, Johannes
    Lalama, Andres
    Koch, Steffen
    Ertl, Thomas
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2021, 27 (02) : 1374 - 1384
  • [12] Analyzing dynamic data: A tutorial
    Revelle, William
    Wilt, Joshua
    PERSONALITY AND INDIVIDUAL DIFFERENCES, 2019, 136 : 38 - 51
  • [13] Interactive Visual Classification of Multivariate Data
    Zhang, Ke-Bing
    Orgun, Mehmet A.
    Shankaran, Rajan
    Zhang, Du
    2012 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2012), VOL 2, 2012, : 246 - 251
  • [14] Visual Discovery in Multivariate Binary Data
    Kovalerchuk, Boris
    Delizy, Florian
    Riggs, Logan
    Vityaev, Evgenii
    VISUALIZATION AND DATA ANALYSIS 2010, 2010, 7530
  • [15] Visual Analytics of Multivariate Intensive Care Time Series Data
    Brich, N.
    Schulz, C.
    Peter, J.
    Klingert, W.
    Schenk, M.
    Weiskopf, D.
    Krone, M.
    COMPUTER GRAPHICS FORUM, 2022, 41 (06) : 273 - 286
  • [16] ComBiNet: Visual Query and Comparison of Bipartite Multivariate Dynamic Social Networks
    Pister, A.
    Prieur, C.
    Fekete, J. -d.
    COMPUTER GRAPHICS FORUM, 2023, 42 (01) : 290 - 304
  • [17] Visual analytics of time-varying multivariate ionospheric scintillation data
    Soriano-Vargas, Aurea
    Vani, Bruno C.
    Shimabukuro, Milton H.
    Monico, Joao F. G.
    Oliveira, Maria Cristina F.
    Hamann, Bernd
    COMPUTERS & GRAPHICS-UK, 2017, 68 : 96 - 107
  • [18] DataMeadow: a visual canvas for analysis of large-scale multivariate data
    Elmqvist, Niklas
    Stasko, John
    Tsigas, Philippas
    INFORMATION VISUALIZATION, 2008, 7 (01) : 18 - 33
  • [19] DataMeadow: A visual canvas for analysis of large-scale multivariate data
    Elmqvist, Niklas
    Stasko, John
    Tsigas, Philippas
    VAST: IEEE SYMPOSIUM ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY 2007, PROCEEDINGS, 2007, : 187 - +
  • [20] Visual Methods for Analyzing Probabilistic Classification Data
    Alsallakh, Bilal
    Hanbury, Allan
    Hauser, Helwig
    Miksch, Silvia
    Rauber, Andreas
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2014, 20 (12) : 1703 - 1712