Planning Support Systems

Couclesis’s article on planning support systems appears to contradict the communicative model of Aitken’s piece on critical GIS. Couclesis argues that planning models have suffered a trade-off effect of including public sentiment in brainstorming sessions at the expense of hard expertise. The author draws a sharp distinction between the social fluidity of planning and the technical-objectivity of models.

I am uncomfortable with the assertion that participatory planning has a negative impact on the scientific and expert rigor of its products. The more relevant issue is inviting and drawing in an appropriate audience for the task at hand. Locals of an area have detailed knowledge of how processes affect them that rivals scientific knowledge. More often than not, their reporting of changes in the system is more timely and appropriate for model corrections than scientifically verified corrections (the convenience and fitness vs. rigor and accuracy argument). The challenge, as Couclesis points out, is to ensure the mutual cooperation of public and experts. As a geographer it also seems silly to paint experts as divorced from the social implications of their work; wouldn’t experts be the most aware of the limitations of their work? As GIS-users, wouldn’t experts also be grateful for the wealth of knowledge VGI and cooperation with the public produces?

Finally, the author’s suggestion that “neither the reactive response to changing circumstances nor the futile reliance on forecasts built on variations of business-as-usual scenarios can deal with the mix of pattern and noise that is the future.” initially confused me. It represents a key understanding though that linear extrapolations are insufficient for temporal analyses since one change may have a disproportionately large impact on other factors in the future. In such an unpredictable case, aggregating to a larger temporal scale is a step in reducing uncertainty. A more bold approach may be to rely on a mosaic of deep local understandings to represent a process across a larger extent. Such a method would be incredibly dynamic and may produce counter intuitive results such as those revealed by agent based modeling.


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