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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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