Optimised spatial planning to meet long term urban sustainability objectives. (November 2015)

• Record Type: Journal Article
• Authors: Caparros-Midwood, D.; Barr, S.; Dawson, R.
• Is Part Of: Computers, environment and urban systems. Volume 54(2015)
• Page Start: 154
• Page End: 164
• Publication Date: 2015-11
• Subjects: Spatial planning -- Sustainability objectives -- Spatial optimisation -- Planning conflicts -- Simulated annealing; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2015.08.003
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Urbanisation, environmental risks and resource scarcity are but three of many challenges that cities must address if they are to become more sustainable. However, the policies and spatial development strategies implemented to achieve individual sustainability objectives frequently interact and conflict presenting decision-makers a multi-objective spatial optimisation problem. This work presents a developed spatial optimisation framework which optimises the location of future residential development against several sustainability objectives. The framework is applied to a case study over Middlesbrough in the North East of the United Kingdom. In this context, the framework optimises five sustainability objectives from our case study site: (i) minimising risk from heat waves, (ii) minimising the risk from flood events, (iii) minimising travel costs to minimise transport emissions, (iv) minimising the expansion of urban sprawl and (v) preventing development on green-spaces. A series of optimised spatial configurations of future development strategies are presented. The results compare strategies that are optimal against individual, pairs and multiple sustainability objectives, such that each of these optimal strategies out-performs all other development strategies in at least one sustainability objective. Moreover, the resulting spatial strategies significantly outperform the current local authority strategy for all objectives with, for example, a relative improvement of up to 68% in the performance of distance to CBD. Based on these results, it suggests that spatial optimisation can provide a powerful decision support tool to help planners to identify spatial development strategies that satisfy multiple sustainability objectives. Highlights: Present the use of computational spatial optimisation framework to enable planners to identify optimal spatial plans in the presence of competing sustainability objectives Application over case study provides a set of best trade-off spatial plans Many optimised spatial plans outperform local authorities development plan Rich set of diagnostic information provides an evidence basis to assist planners to achieve more sustainable patterns of development