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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXX V, Part B4. Istanbul 2004 
  
  
3.1 Dynamically Linked Views 
With dynamically linked views we mean that graphs (e.g. 
multivariate visualization techniques) and maps are displayed 
separately but dynamically linked. If one element in a map is 
clicked, the corresponding elements in other maps or graphs 
will be highlighted. Conversely, by clicking on these 
information elements in a graph, the particular object will be 
highlighted in the map. Dynamically linked views increase the 
user interactivity and are now considered indispensable for 
supporting data exploration. 
In the beach nourishment application, these information 
elements concern the quality elements from table 2, which can 
be activated by pressing the mouse button at a compartment. A 
separate graphic will show the quality values for that particular 
compartment. Also, by clicking on a phenomenon in a graphic, 
the compartment concerned will be highlighted in the map. 
32 Multivariate Visualization Tools 
There are several methods to visualize spatial data quality 
(McGranaghan, 1993; Lucieer and Kraak, 2002; Van der Wel et 
al, 1994) Here, we focus on multivariate visualization 
techniques for illustrating the quality elements for beach 
nourishments derived from the ontological approach. 
Over the past decade, many different visualization techniques 
have been developed (Card et al., 1999). For geographic data, 
visualization techniques can be categorized in geometrically 
transformed displays or iconic displays. The geometrically 
transformed displays include the scatterplot matrix, a 
commonly used method in statistics, and the parallel coordinate 
plot (Inselberg, 1985), that is a popular technique in exploratory 
visualization. Star plots (Chambers et al., 1983) and Chernoff 
faces (Chernoff, 1973) are techniques of iconic displays that 
visualize each data item as an icon and the multiple variables as 
features of the icons. From all these techniques, the dynamic 
parallel coordinate plot has been demonstrated as a powerful 
multivariate visualization technique (Spence, 2001) and has 
been used in several applications (McGranaghan, 1993; Lucieer 
and Kraak, 2002). 
The dynamic parallel coordinate plot was introduced in 1985 by 
Inselberg (Inselberg, 1985). The display is obtained by taking 
dimensions as vertical axes thereby arranging them parallel to 
each other. The individual data values are then marked off for 
each dimension onto corresponding coordinate with the highest 
data value as maximum value and the lowest as minimum. 
From the structure of the resulting display one can draw 
conclusions for the relationship of the corresponding data 
values. A group of lines with a similar gradient can, for 
example, indicate that their data records correlate positively. 
Furthermore, outliers in values are easy to detect. 
33 Temporal Ordered Space Matrix 
To deal with multi-temporal datasets, we propose a novel 
visualization technique, whereby linear elements (as with 
coastline. compartment) are portrayed against the temporal 
Variations of a particular user-defined quality element. 
Therefore, we construct a matrix of squares, named temporal 
Ordered space matrix. In horizontal direction, the matrix is a 
Sort of schematized map and represents each compartment as a 
Cll in identical order as in Cartesian space.’ Hence, 
compartments in the west of the study area are depicted in the 
matrix left of compartments in the east. The decision maker 
needs to reflect each quality element with a conformance 
quality level, i.e. a threshold value. When quality elements 
fulfil the threshold value, they are shown as green cells. 
However, if they fail, they are show as red cells. Quality 
elements close to the threshold value are shown as orange cells. 
The time is projected in vertical direction. Hence, the evolution 
of quality elements can be easily interpreted from the temporal 
ordered space matrix. 
The main advantage of using ordered space is the preservation 
of the adjacency relations between the compartments. This will 
assist the decision maker in understanding and detecting areas 
where effects of quality elements are important. 
4. PROTOTYPE 
Using the results of table 2 and visualization techniques as 
mentioned above, we designed a prototype for multivariate 
visualization, composed of three sections (see figure 3). The 
first section is the main interface that displays a base map of the 
study area. A pull-down menu gives the user the selection of 
maps illustrating the beach volume or particular quality 
elements for each compartment. 
The second section consists of a parallel coordinate plot (PCP). 
In the PCP each line represents a compartment. The vertical 
axis shows the quality elements derived from table 2 for each 
compartment. Here, the lower a line is located in the plot, the 
better the dataset meets the user's preferences. In the PCP the 
user can also the possibility to display the available datasets for 
all times. Consequently, the PCP will display a particular 
quality element as a time series, with the recorded date on the 
vertical axis. A menu next to the display will give the 
opportunity to display the quality elements per compartment or 
as a timeserie. 
The third section is the temporal ordered space matrix. Next to 
the matrix is an entry menu, where the user can select a 
particular quality element. Additionally, the user can enter a 
threshold value for a particular quality element. The distribution 
of this quality element in (ordered) space and time, as well as 
its ability to meet the threshold value, will be depicted in this 
matrix. 
The prototype enables interactivity between the sections. The 
user can explore the dynamically linked displays by pointing 
the mouse on a compartment in the map and the corresponding 
PCP-axis or matrix cell becomes highlighted. Vice versa, the 
user can point on specific line or matrix cell leading to the 
highlighted contour of the corresponding compartment. Even 
more, the user can select a dataset by a moving window in the 
temporal ordered space matrix. Besides, the user can select a 
threshold value, changing the color legend of the map and the 
PCP (see figure 3). 
5. DISCUSSION 
The prototype for multivariate visualization of spatial data 
quality elements can be useful for interactive and explorative 
purposes. Further elaboration of the prototype will help in 
understanding datasets and their quality elements by instant 
view. Its effectiveness towards insight of the data and their 
shortcomings related to their quality, help decision makers to 
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