Rigour and Rigour Mortis? Planning, calculative rationality, and forces of stability and change

Blog by Iain White


Created
23 Jan 2020, 3:27 p.m.
Author
Iain White
DOI
10.1177/0042098019886764

Abstracthttps://journals.sagepub.com/doi/10.1177/0042098019886764#abstract

 

Like many research ideas this one started from a point of annoyance - one which many readers of this blog would find all-too-familiar.

 

After growing public critique in New Zealand concerning rising housing unaffordability and poor quality homes and urban environments, the governing centre-right National Party decided the planning system was to blame. There was too much red tape. It was too inefficient. Planners and policy needed to reduce regulation and release more land for development. The argument was that these changes would improve urban outcomes.

 

While the case for change was compelling, the simple causal links of much political discourse stood in stark contrast to the complexity of issues that urban researchers routinely discuss. Was this really about a new policy fix? For instance, there were some very good outcomes alongside poor ones, so perhaps it wasn’t just a matter of ‘policy’. It also wasn’t a problem of a lack of data. Running parallel to this critique we had also seen the rise of the evidential turn in planning. Practitioners used an ever more diverse range of decision support tools to improve outcomes, from population projections, to cost-benefit analyses, to complex traffic models and beyond.

 

I thought there was something interesting going on at the science–policy–practice interface. So I analysed the selection, application and wider effects of ‘calculations’ in planning to better understand why, when we say we want urban areas to be more affordable and liveable, and we enjoy a stronger evidence base than ever before, were the outcomes deemed poor?

 

It found that data played important political roles in mitigating professional liability and institutional risk, and that innovation was perceived as risky. The quote from a planner that ‘the first thing I look at it is: “am I going to get sued?”’ sums this view up. This strong epistemic culture didn’t just assign significant weight to data but also preferred certain types of evidence to others, as exemplified by the view that: ‘We are in a world of proof, and proof is numbers’. This privileged agencies and agendas with established quantitative calculative competences (e.g. traffic modelling) compared to emergent or qualitative agendas (e.g. walkability or liveability). To compound the authority assigned to data, there was also an acknowledged black box effect, whereby the growing complexity meant that planners struggled to ‘look under the bonnet’ of models.

 

Over time these various aspects also started to align, standardise and become efficient, so one tool drew from data in another, and one process reinforced another. While it was effective, it essentially served to foster a favoured spatial imaginary that was based on greenfield development and car use. To destabilise these existing relations and advance counterclaims of alternative futures required new investment that was seen as an uncertain value proposition by some.

 

This may all be understood as a tension between the human desire for a settled objective world, with the changing subjective expectations people have of places. A tension which will need to be navigated if we are to avoid the risk of abdicating professional judgement and planning by technical proxy. Key recommendations are to focus on revealing the underlying presuppositions that seek to categorise the world; consider how epistemic cultures assign value and agency to knowledge; and reflect how easy is it to advance counterclaims, open up alternative discourses, and facilitate systemic changes if desired.

 

If there is one broader message to this paper it's this: data is not just inherently political, it’s inherently conservative. From the autocorrected text message that replicates previous conversations, to the political preference for tried-and-tested approaches that replicate urban form, data can have a gravitational pull towards producing the same outcomes over and again. Which is fine, unless there are calls for change.

 

Read the accompanying article on Urban Studies OnlineFirst: https://journals.sagepub.com/doi/10.1177/0042098019886764

 


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