ul 2004
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MULTIVARIATE VISUALIZATION OF DATA QUALITY ELEMENTS
FOR COASTAL ZONE MONITORING
D. E. van de Vlag and M. J. Kraak
International Institute for Geo-Information Science and Earth Observation (TTC), Dept. of Geo-Information Processing
, PO Box 6, 7500 AA Enschede, The Netherlands — (vandevlag, kraak)@itc.nl
KEY WORDS: GIS, coast, monitoring, identification, visualization, accuracy, quality
ABSTRACT:
Broad sandy beaches and extensive dune ridges dominate the Dutch coastal zone. The beach areas are subject to continuous
processes as beach erosion and sedimentation, which influence its morphology. This in turn has an economic impact on beach
management and public security. Beach nourishments are carried out if safety of the land is at risk. Here the problems are defined as:
(1) how to localize and quantify beach areas that require nourishment, and (2) how to assist the decision maker to manage the
process of nourishment in time. To tackle the above-mentioned problems we used geographic information of different sources. We
introduced an ontology-driven approach to integrate the different data sources and to conceptualize the beach areas, their attributes
and relationships. An ontological approach greatly helps to unde
rstand the role of the quality of the data sources and also the
required qualities for the decision maker. To express the data quality derived from metadata as well as from user-required qualities,
we presented a novel visual environment for illustrating quantitative values of quality elements using multivariate visualization
techniques. Quality elements that we studied for the beach nourishment process are: positional accuracy, thematic accuracy,
lemporal accuracy and completeness. By combining multivariate visualization with the technique of multiple linked views different
aspects of data quality can be conveyed in relation to the original data. We conclude that the prototype can be useful for interactive
and explorative purposes and has its strengths to deal with non temporal, as well as multi-temporal data.
I. INTRODUCTION
The Dutch coastal zone has an extremely dynamic morphology
due to tidal currents and storms. This morphology is influenced
by processes such as erosion, transportation and sedimentation.
Changes in morphology have consequences for the public
safety of the hinterland and beach management. Beach
nourishments are carried out if there is a risk to the hinterland.
For economic reasons, areas suitable for beach nourishment
need to be determined. This can be achieved with an
ontological approach, whereby the quality elements of each
area are described using quantitative methods. The ontological
approach integrates both data and semantics in a common
reasoning framework consisting of objects, attributes and
relationships. To reach this, we need an extensive dataset.
Large geospatial datasets are now easily available to the public.
These can be used to extract valuable information which
requires new interactive (usually) multivariate tools (Matange
et al, 1998). In recent years, applied researchers have become
increasingly interested in multivariate visualizations in order to
find low dimensional structures in higher dimensional data
(Schmid and Hinterberger, 1994). After all, graphic displays
Show patterns in the data more clearly than plain numbers,
leading to better descriptive and explorative models of the data.
For the visual representation of elements related to spatial data
quality, there are two major approaches: (1) symbolization of an
individual quality element such as uncertainty and (2) graphical
"presentations showing multiple quality elements. Regarding
Smbolization, ^ MacEachren (1992) ‘and others (eg,
McGranaghan, 1993, Van der Wel et al., 1994) have examined
Bertin's graphic variables for use in representing uncertainty
and have added new variables, notably saturation (i.e., purity)
of color and clarity. The latter can be further broken down into
crispness, resolution, and transparency (MacEachren, 1995).
Concerning graphical representations of quality elements,
traditionally this includes single bivariate tools, map pairs and
multiple maps, sequential presentation, and interactive displays
(MacEachren, 1992). When dealing with several elements
related to spatial data quality, the use of multivariate
visualization tools can be efficient, due to their ability to
simplify dimensions. However, this has mainly been studied for
a single quality element within a time series (McGranaghan,
1993) or, in remote sensing applications, as a single quality
element within the spectral behavior (Lucieer and Kraak, 2002).
Here, we propose a multivariate visualization prototype to
detect trends and associations in the data and their quality
elements and to present them in a visual form. Hence, the aim
of this paper is to visualize all quality elements - taking an
ontologically based approach - within an explorative use
environment, using multivariate visualization techniques with
dynamically linked views. The prototype will support in
understanding where to locate areas suitable for nourishments,
how they behave in time, and what the influences of several
data quality elements are.
2. BACKGROUND
2.1 Study Area and Dataset
The study area is located at the north-western part of Ameland,
a coastal barrier island on the fringe between the Wadden Sea
and the North Sea (figure 1). Geomorphological processes such
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