ABSTRACT
In Chapter 9, Formula (9.3) expressed fitness-for-use of analytical outputs as a function of model, data, and the professional where each of these is a set of entities. Throughout this book, we have been progressively looking at the mapping:
f : Ω→ℜ (10.1)
which in this last chapter can be expressed as:
f : Ωu u→ℜ (10.2)
where Ω = set of domain inputs, ℜ = set of real decisions, u = uncertainty. In Chapter 8, we looked at the issues surrounding data uncertainties and
the evolving strategies for knowing and reducing the level of uncertainty in spatial data, the analytical products of GIS, and inputs to environmental simulation models. However, as Frank (2008) has observed, it is “difficult to observe directly the effect of data quality on decisions.” While good data = good decisions is a common-sense belief, there are, as we have seen, so many intermediate steps between data collection and model output-the transformation of data into information and understanding-that better data do not necessarily lead to better decisions. In Chapter 9, we considered a range of issues in model uncertainty and, again, the evolving strategies for knowing and reducing the level of uncertainty in the outputs of GIS and environmental simulation modeling. Here again, higher resolution, more detailed models are not necessarily the path to better decisions. In any case, as we saw from Equation (9.3), there will inevitably be some residual uncertainty. However, having gotten to this stage, a decision needs to be made by somebody: Is there a problem or isn’t there, what are the risks, should something be done about them and if so what, is it technically the most appropriate solution, will the majority agree with it, how much is it going to cost, can we afford it, should we afford it, and does it represent value for money? This typifies the decision space that needs to be explored and navigated. Many GIS analysts and professional modelers may well say it is not their decision, they just ascertain and present the facts as they see them. But as we discussed in Chapter 5 (Figure 5.7), there has to be communication with the policy
makers and the public in an iterative process that should ideally bring about an informed consensus. Bellamy et al. (1999), for example, have emphasized the political and social context of environmental decision making requiring an inclusive process of collaboration and participation of scientists, professionals, and stakeholders. The reduction of risks is not just a matter of science and engineering (Tansel, 2005), but needs to include people’s perceptions of hazard and how to deal with them. Social and economic factors are key in distinguishing disasters from ordinary events and, therefore, are crucial in assessing disasters (Wisner et al., 2004). Those affected by disasters tend to be people who are geographically marginalized to hazard-prone areas, socially marginalized because they suffer poverty and other inequalities, and politically marginalized because their voice is disregarded (Gaillard et al., 2007). Environmental decision making is inevitably negotiated in an arena of power relations where some actors have more power, resources, and better tactics with which to be heard (Few, 2002; Haque, 2003; Mercer et al., 2008).
