Attribute Signatures: Dynamic Visual Summaries for Analyzing Multivariate Geographical Data

被引:33
|
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
  • [41] Analysis guided visual exploration of multivariate data
    Yang, Di
    Rundensteiner, Elke A.
    Ward, Matthew O.
    VAST: IEEE SYMPOSIUM ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY 2007, PROCEEDINGS, 2007, : 83 - 90
  • [42] ANALYZING POLARIMETRIC SIGNATURES FOR DIFFERENT FEATURES IN POLARIMETRIC SAR DATA
    Jafari, Mohsen
    Maghsoudi, Yasser
    Zoej, Mohammad Javad Valadan
    2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2014,
  • [43] Analyzing the notions of attribute, aggregate, part and member in data/knowledge modeling
    MotschnigPitrik, R
    JOURNAL OF SYSTEMS AND SOFTWARE, 1996, 33 (02) : 113 - 122
  • [44] Searchlight Back-Projection - A Tool for Analyzing Neural Signatures in Visual Space
    Stoll, Susanne
    Infanti, Elisa
    Schwarzkopf, D. Samuel
    PERCEPTION, 2019, 48 : 133 - 134
  • [45] Analyzing Information Transfer in Time-Varying Multivariate Data
    Wang, Chaoli
    Yu, Hongfeng
    Grout, Ray W.
    Ma, Kwan-Liu
    Chen, Jacqueline H.
    IEEE PACIFIC VISUALIZATION SYMPOSIUM 2011, 2011, : 99 - 106
  • [46] Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data
    Thao Vu
    Siemek, Parker
    Bhinderwala, Fatema
    Xu, Yuhang
    Powers, Robert
    JOURNAL OF PROTEOME RESEARCH, 2019, 18 (09) : 3282 - 3294
  • [47] A multivariate logistic model (MLM) for analyzing binary family data
    Karunaratne, PM
    Elston, RC
    AMERICAN JOURNAL OF MEDICAL GENETICS, 1998, 76 (05): : 428 - 437
  • [48] A visual data analysis for determining the geographical extent of the cabreves
    Zaragozi, Benito
    Gimenez-Font, Pablo
    JOURNAL OF CULTURAL HERITAGE, 2021, 48 : 141 - 152
  • [49] Visual Abstraction of Geographical Point Data with Spatial Autocorrelations
    Zhou, Zhiguang
    Zhang, Xinlong
    Yang, Zhendong
    Chen, Yuanyuan
    Liu, Yuhua
    Wen, Jin
    Chen, Binjie
    Zhao, Ying
    Chen, Wei
    2020 IEEE CONFERENCE ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY (VAST 2020), 2020, : 60 - 71
  • [50] Visual aggregation of large multivariate networks with attribute-enhanced representation learning q
    Liu, Yuhua
    Hu, Miaoxin
    Zhang, Rumin
    Xu, Ting
    Wang, Yigang
    Zhou, Zhiguang
    NEUROCOMPUTING, 2022, 494 : 320 - 335