Location Intelligence - EngD research presentation at CASA

Just a quick note that I will be giving a half hour presentation tomorrow at the Centre for Advanced Spatial Analysis on my Engineering Doctorate research. I will put up the slides of the presentation after tomorrow Its up here now.  This should be a good opportunity for anyone  interested in a good summary of my work over the past three years. Below an abstract and location details:

Wednesday, February 10, 2010: 17.30
CASA, Basement Lecture Theatre, 1-19 Torrington Place, UCL

Location Intelligence: an innovative approach to business location decision making
As one of the leading ‘world cities’ London is home to a highly internationalised workforce and is particularly reliant on these sources of foreign direct investment (FDI). In the face of increasing global competition and a very difficult economic climate, the capital must compete effectively to encourage and support such investors. Through a collaborative study with London’s official foreign direct investment agency, Think London, the need for a coherent framework for data, methodologies and tools to inform business location decision making became apparent. This presentation will discuss the development of a rich environment to iteratively explore, compare and rank London’s business neighbourhoods alongside ancillary data. This is achieved through the development, integration and evaluation of data and its manipulation to form a model for locational based decision support. Firstly, we discuss the development of a geo-business classification for London which draws upon methods and practices common to many geospatial neighbourhood classifications that are used for profiling consumers. In this instance a geo-business classification is developed by encapsulating relevant location variables using Principal Component Analysis into a set of composite area characteristics. Secondly, we discuss the implementation an appropriate Multi-Criteria Decision Making methodology, in this case Analytical Hierarchy Process (AHP), enabling the aggregation of the geo-business classification and decision makers preferences into discrete decision alternatives (Carver 1991; Jankowski 1995). Lastly, we present the preliminary results of the integration of both data and model through the development and evaluation of a web-based prototype and evaluate its usefulness through scenario testing.