GIS-Based Approach to Analyze the Spatial Opportunities for Knowledge-Intensive Businesses

Knowledge-intensive businesses (KIBs) are one of the fastest developing subsectors that play an integral role in the world’s economy; they have been outperforming the total economy in terms of economic and regional growth and job creation recently. The KIBs are usually driven by qualified and skilled workers capable of contributing to the creativity and innovation in a particular business sector. The location preferences of KIBs, therefore, are significantly different from other product-driven businesses. This study uses spatial analysis in a geographical information system (GIS) together with a multicriteria decision-making (MCDM) process to examine the spatial opportunities for growth of KIBs by integrating spatial data with the decision-makers’ opinion collected through questionnaire survey. The proposed approach was tested by applying to a case study area of Cambridge, the United Kingdom, which has a large number of knowledge-based firms. Two major sectors, biotechnology and computer technology, were analyzed using 11 factors; the data on companies’ perception regarding importance of location and other variables was collected through questionnaire survey of 17 firms and discussion with the field experts. We applied an “analytic hierarchy process,” which consists of a MCDM method, in order to obtain the relative importance of each factor contributing to the location of new firms. After this step, all spatial and aspatial data were translated to spatial coverages in order to build a GIS-based model to identify the areas suitable for biotechnology and computer technology businesses. The results indicated that KIBs prefer to be located close to research and development centers and Cambridge University Departments, which fuel these businesses by providing skilled workforce. The proposed approach presents robust results and provides the flexibility to integrate the variables fitting well to the local context to generate more realistic and accurate results.