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Martin Laplante

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Thu, 11 Nov 2010

Evidence-based Urban Planning, Part 2: Setting Objectives

This is a continuation of the previous post on Evidence-Based Urban Planning.

The first task of an evidence-based plan is to set objectives. But the types of objectives that go into these plans have to be re-examined. I donít mean that the objectives themselves have to be evidence-based. By and large in a democratic society it is a political process that should be setting objectives. But there is also a great tendency in a democratic process to confuse the objectives with the means of achieving them. If the stated objective is to build more roads when the actual intent is to reduce congestion, or to build more shelters when the intent is to reduce homelessness, then the objectives themselves are the wrong ones. Planners will have to comply with the stated objectives, but without any expectation that they are solving any problem.

What is required is to step back. Is "increasing density" an end in itself, or is it a presumed means of achieving some other end? The technique to use is one that has been invented by every 3-year old, the endless question "why?". Itís a simple epistemological quest for root causes, and itís a question we donít ask nearly enough. Why do you want to increase density? Is it because you believe that it reduces vehicle use, that it reduces the cost of providing services, that it preserves farmland, etc? In that case maybe those are ends in themselves, and density may just be a presumed means to an end. Maybe there are other ways of achieving the same objectives either instead of, or in addition to, what you originally thought. Maybe some other measure that will be taken in order to achieve some other objective will increase vehicle use or increase the cost of services, in which case you may have cancelled out all beneficial effects for lack of defining them as objectives.

The objectives that are chosen must be measurable and to some degree predictable. There is no point in having a plan if no one can tell whether its objectives were achieved. For instance if you are going to do a CPTED analysis (one of the better existing uses of evidence-based planning) to inform urban design, you have to not only be able to measure crime rates before and after, but you have to be able to predict what the crime rate would have been if the design changes had not been applied. The objectives have to be specific enough that they can be measured with some certainty, and that they can be predicted with some certainty, so that five years later you can say that without these design changes the crime rate would have gone down by x% but after these changes it went down by y%. These predictions have to be more than guesswork and extrapolation of current trends. Think for a second. Your hypothesis is that urban design changes will change crime rate. Does your predictive method agree? If your predictive method is insensitive to these design features, then the prediction that you intend to relying on to evaluate your plan will give the same result for the scenario with those features and without, that is to say that it predicts that the design will have no effect.

The other desirable property of the objectives is that they can be achieved in large part through urban planning or urban design. For instance some studies show a strong link between urban form and childhood obesity. Other studies donít. However, all of them agree that many other factors are also involved, including nutritional, educational, social, health, family life, and so on. Itís an important enough issue that public health and educational authorities are also tackling the problem from their end. That makes the objective a difficult one to evaluate in an evidence-based planning framework. The attribution problem, determining whether changes in urban form were the ones that had a major contribution on outcomes or a minor one, is very challenging. That means that this type of objective, although laudable and important, doesnít lend itself well to an evidence-based approach. That is not to say that it shouldnít be attempted. It can benefit from some other aspects of an evidence-based approach, but an objective that suffers from an acute attribution problem may not be the one you want in your plan. Perhaps a more limited objective, like the distances walked and biked or the percentage who walk or bike would be more appropriate, even though they are not ends in themselves, because they are a sub-objective that can be tackled mostly through planning.

Finally, the objectives must be politically legitimate. There are laws that govern what planning authorities are allowed to regulate and I assume that those are respected. But there are also predictable side-effects of certain patterns of development that must be made clear and avoided, like racial segregation, limiting of freedom of movement and freedom of assembly, and impacts on the most vulnerable. There is a moral imperative to ensure these impacts are well understood. But it is all too easy to obscure the true meaning of what is being planned in the belief that elected officials or community members would be less or more willing to support it with partial or misleading information. That is why loaded words must be carefully defined so that they donít mean different things to different people. To some "density" means highrises and to others it means duplexes, while many assume that you're only talking about downtown. Will some be surprised when they find out the implications of the plan after it has been passed? What is meant by "infill", by "mixed use", by "scattered site housing", by "community involvement", by "range of housing types"? Clear communication is required at the time of setting objectives for these objectives to be politically legitimate.


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