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The detailed results of the M function analysis for the different variables can be found in Appendix 11.2 – “M function Analysis”. The main findings from running the M function, regarding differences between industry sectors, functions and investors’ country of provenance, as well as the spatial scale at which such effects appear, are discussed in this section.

There was evidence of excess concentration in all three classifications. Some origin countries, including the US, India, Canada, Italy, and Korea, presented excess concentration, although for some of these countries, the sample was quite small. There were also some industry sectors such as financial services, creative industries and ICT, tourism and leisure, and life sciences, as well as some other sectors with very small samples, which presented evidence of

concentration. Some of these sectors were previously investigated in cluster studies, for

26 Available at http://e.marcon.free.fr/Ripley/cadre.fre.htm

example financial services in the City of London, or the creative industries cluster in Soho or Hoxton. Headquarters were spatially concentrated, which could be explained by the

preference for high value and prestigious office space in the best sites in Central London. R&D showed strong evidence of concentration, which could be explained by the co-location of research-intensive business activities with universities, for example. Distribution showed concentration as well. However, it should be noted that the analysis using the M function was hampered by the relatively small sample size of the dataset, once the sector, functional, or country classifications were applied.

An important conclusion to take from the analysis is the spatial scale at which clustering seems to happen. In most cases, the analysis highlighted the concentration to be highest at short distances of less than one kilometre. For the most part, significant concentration tapered off as the distance increased above a few kilometres. This informs the assumptions about the spatial scale at which economic activities locate and form clusters. In this case, they are observed at the scale of local neighbourhoods, areas such as local town centres or other local business, retail, or industry parks. This information guides the development of the geographic framework adopted in this study. As a result, the spatial scale of investigation for this study of the geography of London’s business locations can be justified from these observations to be the local neighbourhoods and town centres level.

3.3 Conclusion

This chapter builds on the previously presented review of relevant academic research in the context of business location decision-making, along with a competitiveness and branding framework for cities and results of third party surveys, to build a more detailed understanding of how sector, function, or corporate culture influence the decision-making process and location variables considered by firms. Together with primary research into FDI-relevant location variables for London, this chapter develops a unified data framework for business location decision-making.

This data framework limits itself to the modelling of different urban business environments, applicable to a wide range of firms from different sectors, functions, or origins, capturing the following generic business location variables:

1. the discovery, quantification, and qualification of industry sector clusters (Companies),

2. the characterisation of the available talent pool and daytime population (Working Population),

3. the quantity and quality of the Property Stock,

4. a more general appreciation of the Living Environment of London neighbourhoods, and 5. accessibility through public transport of different neighbourhoods inside London.

It is important to note that the highly individual and potentially complex co-location

preferences of individual firms are not supported by this data framework, owing to the focus of the development of a generic database relevant to a wide selection of different investors.

Apart from the development of a relevant data framework, the spatial nature and most relevant spatial scale of analysis and integration is also considered in this chapter. The empirical analysis of historic FDI investment patterns first enabled a more nuanced understanding of the complex nature of London’s business landscape, and specifically of historic investment patterns. The most important conclusion from the analysis is evidence of the uneven nature of spatial economic development, highlighting the spatial scale at which firms agglomerate in London. London’s local neighbourhoods (1-2km) are proven to be the dominant spatial scale at which FDI investors agglomerate. The spatial scale of investigation of business location decision-making in London which the data framework needs to take into account then can justified to be at the local neighbourhoods and town centres level. These results justify the need for a more relevant spatial subdivision for FDI promotion and location consultancy activities in London, other than the predominant subdivision of London into five subregions (see section 5.2 - "Geographic framework” for the development of the spatial framework). In conclusion, this chapter helped define the structure of a parsimonious data framework apt to capture and characterise London’s diverse business neighbourhoods, enabling the design and implementation of a spatial database component.

This chapter introduces the principal methodologies and processes used in this thesis, formalised and encapsulated in the research framework, guiding the further development of this research and implementation of the case study. The development of this research

framework starts with an introduction and review of relevant Decision Support methodologies.

Decision support system implementations have been driven mainly by advances in computing, resulting in the application not only to management problems, but increasingly to spatial problems as well. This has resulted in the emergence of Spatial Decision Support Systems (SDSS), integrating spatial data and processes into decision support. Through a discussion of relevant conceptual SDSS frameworks, the potential for efficient support of spatial decision problems such as business location decision-making is highlighted. Given this potential, this chapter is mainly concerned with the comparison and linkage of such a SDSS framework, including key definitions, characteristics, and concepts, with the previously stated research aims and objectives.

This evaluation enables the formulation of a research framework for this thesis, guiding not only the research into business location decision-making support, but also defining the salient characteristics of a SDSS for business location decision support. This chapter concludes with the application of this research framework to the specific case study of a FDI business location decision-making prototype. This work defines high level functional and data requirements for the effective provision of location decision support, as well as a relevant system structure guiding the system design. This discussion then leads to the implementation process of the proposed SDSS, discussed in subsequent chapters.