addition, time series animation capabilities have been built into most scientific visualization systems. 
While the data models for scientific visualization systems are powerful in their generality and 
flexibility, they contain no specifically geographic information, and have no knowledge of geographic 
coordinate systems. Geographic transformation and analysis functions are not built into these 
systems. 
GIS and scientific visualization systems have distinct features that can benefit environmental 
modelers, but each system also comes with inherent limitations. Some sort of integration between 
these systems is desirable. 
Owing to the diverse functionality of the different tools used in environmental modeling, and 
their inherent limitations, the best results will not be achieved by using one single tool, but rather 
will be attained by using the most appropriate tool for each step in the overall process. The main 
reason why this simple principle is often neglected is the time and effort required for coupling 
disparate systems — the so-called integration effort. Another reason is that many users, even in 
a research environment, are simply not aware of the availability of more appropriate tools for a 
given task, even when these tools are commonly used in other research fields, or else the cost and 
expertise required to invest in these other tools is prohibitive. For every application, the advantages 
of using heterogeneous tools has to be balanced against the cost of implementing and integrating 
these tools into the overall process. 
It is interesting to note the role that GIS have played as an integrating technology. GIS have 
evolved by bringing together diverse technologies and applications, and they allow researchers to 
integrate their data and methods in ways that support both new and traditional forms of geographic 
analysis. Although GIS have been succcessful in integrating geographic analysis functions, they 
could be even more powerful if they were well-linked to other systems and tools such as complex 
models and visualization packages. 
In general, different tools can be coupled through either tight or loose integration approaches [Liv 
ingstone and Raper 1994]. The aim of tight integration is to merge different tools into a single pow 
erful system. This system will be superior to its constituent parts, offering full functionality and 
full interactivity between the utilities originally belonging to separate systems. Tight integration 
can only be achieved by creating a common datamodel. The question to be asked is whether it is 
desirable or even possible to integrate complex tools tightly into a GIS [Maidment 1993]. Numerical 
models often lack well-defined datamodels. while scientific visualization systems usually have their 
own. different, datamodels. The aim of loose integration is to facilitate the use of different tools 
within a single application by converting data into the correct data formats (which are tool depen 
dent). and by providing interfaces to guide the user through the different steps that are involved in 
the overall process. 
While the tight integration approach may deliver full functionality, interactivity, user friendli 
ness. and speed, this kind of integration usually requires profound changes in the constituent tools, 
leading to excessive implementation time and costs. Loose integration places the different tools 
within a common framework rather than absorbing them into a single system. For any given appli- 
4.0 SYSTEMS INTEGRATION 
TECHNiouns- 
INFORM ATIONSBIBLIOTHEK 
UNIVERSITATSBISLiO i HEK 
HANNOVER 
89