Exploring the potential of technology in enabling the inclusive co-production of space

The potential of emerging technology to address poly-urban issues is a growing focus on the agendas of cities worldwide. However, there is a lack of consensus regarding how and in whose interests it should be applied - should the aim be to establish 'smart cities' or to encourage 'smart citizens'? The 'bottom-up' approach advocates the latter and recognises the potential of technology to facilitate the prioritisation of issues and co-production of spaces. Particularly in a developing context where resources are severely limited, the ability to prioritise interventions to have maximum impact is exciting. However, these projects and the processes which enable them are under-researched. In this dissertation, a combination of Network Action Research and case study methods are used to guide the application of a selection of digital tools in combination with semi-structured and indepth interviews, surveys, and focus groups to a specific context. The products of this are insights regarding the processes which enable inclusive bottom-up smart city projects; the application of the Network Action Research method; and a context-specific resource of information to guide the future prioritisation of projects and planning in the study area. This dissertation explores the value of inclusive participation in planning, and the role that technology can play in facilitating this. However, it also uncovers the complex and non-linear nature of these projects, ultimately arguing that although technology is a valuable resource, it is not a catch-all. A hybridised approach to bottom-up smart city projects is crucial to their success.

Grantor dc:publisher.institution

School of Architecture, Planning and Geomatics

Year dc:date.issued

2016

Author dc:creator

• Corbett, David

Advisor dc:contributor.advisor

• Odendaal, Nancy

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/22727

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/22727