SDI Convergence - Global Spatial Data Infrastructure Association

lative effects of activities on the landscape. Spatially explicit models which rely on such
common parameters as magnitude, frequency and extent are used extensively to simulate
potential change, highlight patterns and identify critical impacts such as habitat
fragmentation, dry land salinity and soil erosion within agricultural areas.
Models can help in understanding the impacts of changes in climate, highlighting at-risk
portions of the natural environment and immediate threats to agriculture. For example,
the current dry period (below average rainfall over the last 12 years across south eastern
Australia) places significant pressure on limited water resources. The application of
water balance models in specific catchments is recognized as crucial in protecting
those resources (Boughton, 2005; Ranatunga et al., 2008). Furthermore the impact on
the environment is felt when wildfires race through communities and forests alike due
to the prolonged drying effect on the land. Review studies highlight the need to develop
search and discovery systems to locate each model instance, the data used and its
spatial/temporal extents.
The MIKE prototype has been populated with a number of land use change and impact
models as reported by (Nichol et al., 2005). Many of these models have been applied
in Victoria to better understand: adaptive management of native vegetation, rural land
use change, groundwater dependencies and socio-economic conditions. These research
methodologies and results are only accessible via a recent published volume on
spatial models for natural resource management and planning (Pettit et al., 2008). This
typically is how models and model outputs are shared within the modelling community.
The outputs from a number of land use change and impact model also make their way
into Government reports which are accessible via planners and decision-makers. This
raises all kinds of issues around discoverability, reuse of models and model outputs.
Thus, the fundamental question our research endeavours to address is: how can the
plethora of models for understanding land use change and impact be made more accessible
to end users? We believe the key lies in model metadata, a term which refers
to ‘data about models’. This article concludes by outlining on-going research in designing
a methodology for evaluating the MIKE prototype and identifying future research
priorities.
2. METADATA A CRITICAL COMPONENT TO SDI
Spatial Data Infrastructures are platforms that facilitate a wide variety of users to access
data in an easy and secure manner, assist stakeholders to cooperate and enable
interaction with spatial technologies in more cost effective ways (Rajabifard 2002).
SDIs can be characterized by their sphere or scale of influence. Rajabifard (2002) describes
5 levels of SDI hierarchy based on local, state, national, regional and global
scales. In this context the MIKE aligns more strongly in respective order at the state,
local and national scales. The use of metadata is a critical component within an SDI.
While the application of a variety of standards provides commonality underpinning a
reliable SDI it is the metadata content within the system that delivers the ‘contextual
intelligence’ required to support the diversity of data and applications utilizing the infrastructure.
There are a number of research initiatives both nationally in Australia such as
the Dataset Acquisition Accessibility and Annotation e-Research Technologies (DART)
(see Website 1) and Australian Research Repositories Online to the World (ARCHER)
(see Website 2) projects and the international Infrastructure for Spatial Information in
Europe (INSPIRE, 2003) that are investigating development of SDI’s and their inherent
metadata components. Additionally projects such as the Science Collaboration Environment
for New Zealand Grid (SCENZ-GRID, 2008) are beginning to combine spatial
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