Posts Tagged ‘couclelis’

Storytelling and integrated land-use models

Friday, March 30th, 2012

Clouclelis (2005) outlines the rethinking of integrated land-use models by orienting the article around three main roles that are interconnected: scenario writing, visioning, and storytelling. The details of the article more than suffice the upsides and downsides of urban planning history with regard to the computational and spatial planning world. The one role that intrigued me the most was that of storytelling. Storytelling, according to the article, strives to “build consensus by presenting particular desired or feared future developments in terms meaningful enough to be credible to non-specialists” (1354). I believe it to be a significant connection between qualitative, and quantitative attributes of planning systems. Clouclelis notes that there is much room for “interpretation and facts” derived from models, however planning emphasizes interpretation and values, a much more arbitrary combination (from a scientific stance anyways). There is a specific comfort that we find when relying on facts rather than values. The concreteness makes them somehow more plausible and tangible than individual intentions and agendas, hence having “models codify uncertain knowledge” (1359). We hold planning accountable for a particular outcome. We expect it to “lead to certain action” (1359). The pressure only accelerates on planning to provide solutions to problems at hand. If we eliminate the jargon in expert language to enhance meaning to implemented models for the non-expert, we should develop methods that are creative, and can facilitate the process of finding a balance between non-specialist, and specialist interaction. What can we learn from both camps? In my opinion, storytelling in itself is not enough to be evocative. The way we tell it has to be compelling. Ideas, experimentation, and actions by means of imagination and sharing, can be significant contributions to successful storytelling.

Another problem I want to address is the lack of clarity of what type of planning support system is indeed necessary, and in need of support (1355). The individuals, groups and communities involved all hold multiple agendas. “At the metropolitan level, transportation, commuting, growth, and sprawl cannot be addressed by one community without direct implications for several others” (1358). Will it ever be possible to address everyone’s needs? Is that feasible, realistic or practical? If that is not an option, will compromise be enough for a potential solution? Or will it be inevitable that certain groups’ requests will be sacrificed and overlooked?

-henry miller

Agent-Based Models and Land-use Planning

Thursday, March 29th, 2012

I couldn’t help think about our lecture on agent-based modeling when I started to read Helen Couclelis’s article on coupling better models with land-use planning. I know that, here, Couclelis is thinking about a different kind of modeling than that often implied in agent-based modeling approaches. She first notes how “sour” the relationship between planning and the academy has gone (135). Then, after detailing the debate over whether or not a planning process utilizing models has any effect on actual land-use plans, Couclelis delves into how, in practice, actual physical planning has become a decentralized activity that doesn’t incorporate strategy (1357).

I get it and I agree. The work being done on the ground, at least in North America, Couclelis argues no longer resembles any of the model runs done in academia. I think the quote Outdoor Addict utilized from the article sums up this problem well. In sum, the models have failed to predict the future accurately enough or, at least, keep up with land-use in practice. Even the “systematic effort to understand what makes certain things about the future predictable and others not, or how to prepare for genuinely unpredictable futures, have so far had only a negligible impact on land-use planning and modeling” (1360). So, what do we do?

Perhaps this is the problem inherent in any modeling process. We do not know that the future will follow the path layed-out in a model. We do not even know that the models initial parameters catch all the various variables and their interactions. Clearly, although she doesn’t say it quite so directly, modelers in this field are struggling with this problem. As Madskiier_JWong points out, uncertainty is the name of the game when it comes to modeling or trying to predict the future (essentially what modeling is when you unpack all its sophistication).

My suggestion: perhaps this is where agent-based modeling might come in. It seems from our previous lecture that agent-based modeling excels at representing lots of competing variables (or agents) and representing the emergent phenomenon that result from their disparate actions. In a land-use context, this might help us to better understand how certain sites might be used in future years and how they might handle such use. Couclelis does suggest this approach on page 1369 when she talks about assessing the cognitive and social dimensions of model interpretation so that modelers can move away from the macro level they currently operate on. Yet, she doesn’t devote much time to it, and such an approach  doesn’t fit within the confines of models she describes that try to predict varying types of land-use cover and changes to infrastructure. But it may add a valuable dimension. A quick search online shows that many people are, indeed, already trying to couple these two ideas.