Visual Analytics of Paleoceanographic Conditions

Decade scale oceanic phenomena like El Nino are correlated with weather anomalies all over the globe. Only by understanding the events that produced the climatic conditions in the past will it be possible to forecast abrupt climate changes and prevent disastrous consequences for human beings and their environment. Paleoceanography research is a collaborative effort that requires the analysis of paleo time-series, which are obtained from a number of independent techniques and instruments and produced by a variety of different researchers and/or laboratories. The complexity of these phenomena that consist of massive, dynamic and often conflicting data can only be faced by means of analytical reasoning supported by a highly interactive visual interface. This paper presents an interactive visual analysis environment for paleoceanography that permits to gain insight into the paleodata and allow the control and steering of the analytical methods involved in the reconstruction of the climatic conditions of the past

[1]  Richard A. Becker,et al.  Brushing scatterplots , 1987 .

[2]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[3]  Ganesh S. Oak Information Visualization Introduction , 2022 .

[4]  E. L. Koua,et al.  A usability framework for the design and evaluation of an exploratory geovisualization environment , 2004 .

[5]  Peter Secretan Learning , 1965, Mental Health.

[6]  Alfred Inselberg,et al.  The plane with parallel coordinates , 1985, The Visual Computer.

[7]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[8]  Ben Shneiderman,et al.  Interactively Exploring Hierarchical Clustering Results , 2003 .

[9]  Peter N. Schweitzer ANALOG: a program for estimating paleoclimate parameters using the method of modern analogs , 1994 .

[10]  Heikki Mannila,et al.  Learning, Mining, or Modeling? A Case Study from Paleocology , 1998, Discovery Science.

[11]  J. Duplessy,et al.  Improving past sea surface temperature estimates based on planktonic fossil faunas , 1998 .

[12]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[13]  Jin Chen,et al.  Combining Usability Techniques to Design Geovisualization Tools for Epidemiology , 2005, Cartography and geographic information science.

[14]  M. Kučera,et al.  Comparison of statistical and artificial neural network techniques for estimating past sea surface temperatures from planktonic foraminifer census data , 2001 .

[15]  Kristin A. Cook,et al.  Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .

[16]  A. Inselberg Conflict detection and planar resolution for air traffic control , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[17]  U. Pflaumann,et al.  SIMMAX : a modern analog technique to deduce Atlantic Sea Surface Temperatures from planktonic foraminifera in deep sea sediments , 1996 .

[18]  Luc Girardin,et al.  Design study: using multiple coordinated views to analyze geo-referenced high-dimensional datasets , 2003, Proceedings International Conference on Coordinated and Multiple Views in Exploratory Visualization - CMV 2003 -.

[19]  G. W. Furnas,et al.  Generalized fisheye views , 1986, CHI '86.

[20]  Pascal Yiou,et al.  Macintosh Program performs time‐series analysis , 1996 .

[21]  W. H. Hutson The Agulhas Current During the Late Pleistocene: Analysis of Modern Faunal Analogs , 1980, Science.

[22]  Roberto Therón,et al.  Using data mining and visualization techniques for the reconstruction of ocean paleodynamics , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[23]  M. Loutre,et al.  Spectral analysis of climate data , 1996 .

[24]  R. T. eron,et al.  PaleoPlot; a tool for the analysis, integration and manipulation of time-series paleorecords , 2002 .