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Title: Location intelligence : a decision support system for business site selection
Author: Weber, P.
ISNI:       0000 0004 2729 5080
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2011
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As one of the leading ‘world cities’, London is home to a highly internationalised workforce and is particularly reliant on foreign direct investment (FDI) for its continued economic success. In the face of increasing global competition and a very difficult economic climate, the capital must compete effectively to encourage and support such investors. Given these pressures, the need for a coherent framework for data and methodologies to inform business location decisions is apparent. The research sets out to develop a decision support system to iteratively explore, compare and rank London’s business neighbourhoods. This is achieved through the development, integration and evaluation of spatial data and its manipulation to create an interactive framework to model business location decisions. The effectiveness of the resultant framework is subsequently assessed using a scenario based user evaluation. In this thesis, a geo-business classification for London is created, drawing upon the methods and practices common to geospatial neighbourhood classifications used for profiling consumers. The geo-business classification method encapsulates relevant location variables using Principal Components Analysis into a set of composite area characteristics. Next, the research investigates the implementation of an appropriate Multi-Criteria Decision Making methodology, in this case Analytical Hierarchy Process (AHP) allowing the aggregation of the geo-business classification and decision makers’ preferences into discrete decision alternatives. Lastly, the results of the integration of both data and model through the development of, and evaluation of a web-based prototype are presented. The development of this novel business location decision support framework enables not only improved location decision-making, but also the development of enhanced intelligence on the relative attractiveness of business neighbourhoods according to investor types.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available