Multiscale recurrence analysis of spatio-temporal data

被引:12
|
作者
Riedl, M. [1 ]
Marwan, N. [1 ]
Kurths, J. [1 ,2 ]
机构
[1] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany
[2] Humboldt Univ, Dept Phys, D-12489 Berlin, Germany
关键词
SPATIOGRAMS; HISTOGRAMS; DYNAMICS;
D O I
10.1063/1.4937164
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The description and analysis of spatio-temporal dynamics is a crucial task in many scientific disciplines. In this work, we propose a method which uses the mapogram as a similarity measure between spatially distributed data instances at different time points. The resulting similarity values of the pairwise comparison are used to construct a recurrence plot in order to benefit from established tools of recurrence quantification analysis and recurrence network analysis. In contrast to other recurrence tools for this purpose, the mapogram approach allows the specific focus on different spatial scales that can be used in a multi-scale analysis of spatio-temporal dynamics. We illustrate this approach by application on mixed dynamics, such as traveling parallel wave fronts with additive noise, as well as more complicate examples, pseudo-random numbers and coupled map lattices with a semi-logistic mapping rule. Especially the complicate examples show the usefulness of the multi-scale consideration in order to take spatial pattern of different scales and with different rhythms into account. So, this mapogram approach promises new insights in problems of climatology, ecology, or medicine. (C) 2015 AIP Publishing LLC.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Spatio-temporal analysis using a multiscale hierarchical ecoregionalization
    Handcock, RN
    Csillag, F
    [J]. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2004, 70 (01): : 101 - 110
  • [2] A Spatio-Temporal Linked Data Representation for Modeling Spatio-Temporal Dialect Data
    Scholz, Johannes
    Hrastnig, Emanual
    Wandl-Vogt, Eveline
    [J]. PROCEEDINGS OF WORKSHOPS AND POSTERS AT THE 13TH INTERNATIONAL CONFERENCE ON SPATIAL INFORMATION THEORY (COSIT 2017), 2018, : 275 - 282
  • [3] Data analysis and processing for spatio-temporal forecasting
    Lee, Hyoungwoo
    Choo, Jaegul
    [J]. 20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2020), 2020, : 737 - 739
  • [4] Spatio-Temporal Analysis for Smart City Data
    Bermudez-Edo, Maria
    Barnaghi, Payam
    [J]. COMPANION PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2018 (WWW 2018), 2018, : 1841 - 1845
  • [5] Fuzzy cluster analysis of spatio-temporal data
    Liu, ZJ
    George, R
    [J]. COMPUTER AND INFORMATION SCIENCES - ISCIS 2003, 2003, 2869 : 984 - 991
  • [6] Interactive exploratory analysis of spatio-temporal data
    Dreesman, JM
    [J]. COMPSTAT 2002: PROCEEDINGS IN COMPUTATIONAL STATISTICS, 2002, : 407 - 412
  • [7] Action Recognition with Multiscale Spatio-Temporal Contexts
    Wang, Jiang
    Chen, Zhuoyuan
    Wu, Ying
    [J]. 2011 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2011,
  • [8] Spatio-Temporal Multiscale Denoising of Fluoroscopic Sequence
    Amiot, Carole
    Girard, Catherine
    Chanussot, Jocelyn
    Pescatore, Jeremie
    Desvignes, Michel
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2016, 35 (06) : 1565 - 1574
  • [9] Mining spatio-temporal data
    Gennady Andrienko
    Donato Malerba
    Michael May
    Maguelonne Teisseire
    [J]. Journal of Intelligent Information Systems, 2006, 27 : 187 - 190
  • [10] Statistics for Spatio-Temporal Data
    Mills, Jeff
    [J]. JOURNAL OF REGIONAL SCIENCE, 2012, 52 (03) : 512 - 513