Application of Rural Landscape Visualisation for Decision Making and Policy Development
The ability to anticipate and adapt to today’s global environmental issues will significantly lower the biophysical, social and economic costs associated with adaptation to changing conditions. An abstract modeling process often supports an evidence based approach to predicting and analysing these complex challenges. Nonetheless, there are inherent difficulties in understanding these complex models and their impact on stakeholders. The difficulty arises because humans have two complementary approaches to processing information, one is experiential processing, and the other is analytic processing. It is thus important to develop communication systems that complement these two modes of processing in order to support understanding of complex models. This research used a Land Use Allocation (LUA) model as a case study for a complex environmental modelling process. LUA is an evidenced based approach for exploring future agricultural land use change scenarios. This can be broadly defined as the medium to long term strategic planning process by which land managers consider diverse environmental, social and economic factors, before choosing to produce one or more commodities, in a given region. This research distinguishes two interdependent challenges. The primary challenge is to identify interactive options which can reduce the difficulty stakeholders (people who have a vested interest in the outcome of land use management in the future, e.g. regional planners, farmers, etc.) have in understanding a complex model, such as LUA. This, as a second challenge, requires design and development of an interactive, modular, exploratory and integrated framework to provide the identified interactive features.. This research developed a Spatial Model Steering (SMS) exploratory framework that enables users to explore the effect of climate change on land suitability, as a key aspect of LUA, and thus increase their awareness of the influence of key factors. Within this framework a user can visually steer the key climate related factors (rainfall, market price and carbon price) of the LUA model, explore and compare “what if” future land use scenarios by changing these factors and visualizing a range of potential LUA outcomes. The hypothesis is that by doing so, users can develop increased confidence in their understanding of the key factors governing the underlying models, as well as greater awareness of the uncertainty in the outcomes. Equally important, modellers typically need to go back and re-run models every time some parameter changes. Spatial Model Steering enables stakeholders to change models in (near) real time in order to reassess specific, on-thespot interests and scenarios. Spatial Model Steering provides an important step in evidenced based approaches for providing policy, strategic planning and decision support. Statistically significant evidence shows that Spatial Model Steering contributes to a greater awareness of the impact of key factors and uncertainty inherent in a land use allocation process, and this could be the basis for further research into other environmental models that face the same climate change adaptation and mitigation challenges. The research also provides a model framework that can foster the interdisciplinary and comprehensive development of such complex models.