Environmental Engineering Reference
In-Depth Information
Basic Approaches
Figure
22.1
illustrates the two basic, structural approaches of using GIS for model
integration (Brandmeyer et al. 2000; Argent 2004): (1) The integration proceeds
with the GIS being used as a control instance that manages, analyses and maps the
spatial data. Retrieved by the GIS, the modelling components create this data. (2)
Alternatively, a cascade of models and tools run independently from the GIS, using
the GIS-services to get, manipulate and push data. While the first approach is using
GIS to execute models with all the limits of a GIS, the second uses the abilities of
the models running a GIS as spatial database and service-provider, with all the
limits of access and performance.
On a low level of integration (Fig.
22.1
right), the GIS is limited to managing the
data generated by one or more independent models. In this context, loose coupling
would be done by plain interfaces allowing data exchange. Tight coupling via
programming might allow a dynamic use of the components as a “running”
model, while loose coupling is limited to static “put and get”. On a high level of
integration, GIS-components build a functional unit within a model framework (or
vice versa). Having flexible programming environments or macro-languages avail-
able is a precondition for tight coupling of models and GIS, while effective
software-components are the basis of an embedded modelling approach (Sui et al.
1999).
Conceptual and Technical Aspects
Expanding on the basic approach above, there are two contextual approaches for
integrating data, models and methods from different disciplines:
The vertical path, overarching the different sources of data, focuses on the more
technical aspects of integration. Especially in a GIS framework, vertical integration
interconnects information from different disciplines on one common level. It
couples data, models and tools with the same content on a spatial level. Vertical
integration follows the idea of trans-disciplinarity: using information from a tribu-
tary submodel on specific aspects to support a comprehensive model addressing an
overarching question (Oxley et al. 2007). Vertical integration of data and models to
a coupled framework strongly supports fundamental investigations on the charac-
teristics of the system described. It enables the analysis of interactions of entities on
the same organizational level based on information from different levels.
The horizontal path generalizes complex information gained from the coupling
of models and connects it to the knowledge gathered from the analysis of the
coupled data (Yokozawa et al. 1999). It aggregates data, combines different infor-
mation and uses them for conceptual generalizations. Horizontal integration builds
on the results of the vertical approach, which is the first step of the integration
process within the model framework. The horizontal, conceptual orientation is
