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values in January and February are highly correlated, while temperatures in January
and July are more distinct.
The main contribution of this paper is the design and application of
field-based
projection methods to geoscienti
eld datasets.
Our approach is based on two core ideas. First, we derive a number of
c multi
fields that
could potentially be of interest for the exploration of the data. Second, we de
ne a
global distance measure between pairs of
fields, and generate a difference-based
overview of all
fields as high-dimensional data points and
project them to a 2D space, where they are visualized in the form of a scatterplot.
We demonstrate how our approach supports the analysis process of a scientist in
an interactive visual set-up.
fields. Here, we interpret
2 Related Work
When exploring a multi
eld dataset, attention is often given to measuring per-point
similarity between
fields (Edelsbrunner et al.
2004
; Nagaraj and Natarajan
2011
;Sa-
uber et al.
2006
). Many of these approaches are based on gradients. Gosink et al. (
2007
)
used the normalized dot product between gradients and visualized it over statistically
important isosurfaces of a third field. Sauber et al. (
2006
) introduced the gradient
similarity measure (GSIM), which is combined from directional similarity and mag-
nitude similarity. Nagaraj et al. (
2011
) developed a measure as the norm of the matrix
that comprises the gradient vectors, and showed that it is robust to noise in input
fields.
Projection methods are commonly used to describe similarities between spatial
samples of a multi
eld, by placing points with similar multivariate attributes close
to each other in respective visualizations. There exist various linear (Kandogan
2001
) and non-linear (J
nicke et al.
2008
; Sammon
1969
) projection algorithms.
Our approach, however, depicts global similarities between data
รค
fields. Thus, it is
closer to Turkay et al. (
2011
,
2012
), who introduced a dual-space analysis of mul-
tivariate data using linked visualizations of the item space (where objects are entities
represented by their values in different attributes) and dimension space (where
objects are attributes represented by their values for the different entities). In their
approach, the item space represented results of multivariate analyses. The dimension
space was visualized with scatterplots, showing either multidimensional scaling
(MDS) projection results with a correlation-based distance measure, or 2D scatter-
plots of two selected dimension statistics (e.g. mean value vs. standard deviation).
Yuan et al. (
2013
) introduced a dimension projection matrix, which builds on the
concept of scatterplot matrices by assigning a group of dimensions to each row or
column and using projections instead of simple 2D plots. It leverages symmetric
property of the matrix to create a dual space visualization: the cells in the upper
triangle of the matrix contain projections of items in combined set of respective
dimensions, and the lower triangle contains projections of dimensions themselves.
Neither Turkay et al. (
2011
,
2012
) nor Yuan et al. (
2013
) were investigating
spatial data stemming from scienti
c simulations. Thus, our approach is the
rst to
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