Solomou, S., Sengupta, U., Oredein, O., Hyde, R.J., 2018.

A Strategic Planning Problem: Examining the unpredictability of urban transformation based on the changing temporal order of planned projects

Local authority planning departments (UK) typically consist of two separate entities. One deals with the operational aspects of planning applications and the other the strategic planning for the desired future city. The latter (in Manchester) works towards politically influenced, economic, social and environmental future visions. This paper presents research undertaken as part of CPU (Complexity Planning and Urbanism) at the Manchester School of Architecture, in co-operation with the East Manchester Strategic Planning team. The hypothesis examined through this research is the falsity of the notion that more than 50 projects already granted planning permission in the strategic development area of East Manchester lead to a predictable outcome in line with a desired vision. The problem is examined through the development of a new computational model simulating East Manchester as temporal spatial and programmatic morphology with identified relational drivers examined through the statistical analysis of twenty-five years of historic transformation. The computational model is based on the identification of repeated historical correlations between land use data and socio-economic data sets. These relationships have been formulated into rules to simulate extrapolated futures. This model is run multiple times with different starting points (specific projects) and/or different programmatic elements being realised before others, to examine how statistically supported self-organisation is highly influenced by minor changes that do not challenge the known parameters of development (i.e. the known projects). Based on the wide range of future outcomes acquired by adjusting just the order of realisation of the projects that have already been granted planning permission, we argue that the 'vision' based approach, while useful to establish aims, is incompatible with strategizing in the complex and open ended nature of urban transformation. This experiment is aimed at better understanding the challenges a complexity theory understanding posits for strategic spatial planning and the historical notions of multi-equilibrium and/or vision based approaches still in use today. An approach based on the use of large datasets and temporal simulation models does not promise predictability. However, we hope to demonstrate that the possibility to test probable outcomes of strategic controls as well as catalysts within different temporal scenarios has the potential to contribute significantly to the development of strategic future planning.

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