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Title: A learning-enhancing, web-based public participation system for spatial planning : an application to the wind farm siting problem
Author: Simao, A. C. R.
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2008
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Public participation in government is a hallmark of a democratic society. In the planning realm, public participation has come to define good planning and is seen as fundamental to achieving lasting and possible solutions. The effectiveness of public participation, however, is directly related to the volume and quality of available information and the support that citizens have in making sense of this information, both to develop positions and to make informed contributions. This thesis develops a learning-enhancing framework for public participation in spatial planning. Departing from the analysis of a classic theory of learning, Personal Construct Theory by George Kelly (1955), an innovative design for a spatial public participation system is proposed. This system integrates three elements: an information exploration element a Multi-criteria Spatial Decision Support System (MC-SDSS) and an Argumentation Map (AM). In concert, these elements make information available and address the two components of learning suggested by Kelly: the individual component (making sense of one's own experiences of the real world) and the social component (social interaction). A web-based system is developed and used to assess the adequacy of the proposed conceptual framework. The prototype system is applied to the pertinent and controversial spatial problem of onshore wind farm siting using a study area in the county of Norfolk, England. The prototype, called WePWEP (Web-based Participatory Wind Energy Planning), is tested in a quasi- naturalistic experiment. Test results evaluate the proposed framework favourably and highlight some aspects of the prototype that can be improved.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available