Delrieu, Varina Aurelie; (2012) GIS-based indicators for the social impacts of mega urban transport projects. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

This research explores the short to long-term impacts that Mega Urban Transport Projects (MUTPs) have on the communities they serve. In particular, intentional and unintentional social impacts that occur in the communities for the non-user of the MUTP. By their very nature of being ‘mega’, these MUTPs act as catalysts for change at the physical, economical and socio-demographic level. Current appraisal methods for planning and implementing MUTPs are relatively short on a standardised framework for assessing and monitoring the social impacts that communities under-go. This PhD research proposes that GIS can provide a fast and powerful overview of social patterns that can assist planners and decision-makers at local, regional and national levels to consider the ‘knock-on’ effects of the MUTP. This contributes towards understanding how to shape change in those communities to improve the socio-economic status for the whole population, beyond the users of the MUTP. The research also proposes the use of the Cynefin decision-making model with which to assess, act and respond to these impacts and to manage the outcomes so as to amplify the positive effects and dampen the negative. The case-studies are the two non-London hubs of the Channel Tunnel Rail Link; Ebbsfleet and Ashford, Kent. Building from the 1991 census to the most recent digital datasets the toolkit creates ‘planning-to-implementation’ stage profiles of the communities. Variables that are mapped include demographic diversity, socio-economic deprivation, accessibility, journey to work modes, and a pilot study to explore the impact of the MUTP upon changes in social exclusion and community cohesion. The findings of this study conclude that whilst useful lessons can be learnt and future guidelines created for planners and decision-makers for other MUTPs, this hypothetical toolkit has weaknesses related to the lack of spatial and temporal resolution in the datasets utilised.

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