Identifying clusters within R&D intensive industries using local spatial methods

More recently, there has been a renewed interest in cluster policies for supporting industrial and regional development. By virtue of the linkage between growth and innovation, R&D intensive industries play a crucial role in cluster development strategies. Empirical cluster research has to contribute to the understanding the process of cluster formation. Some experiences with the use of local spatial methods like local Moran's Ii and Getis-Ord Gi tests in pattern recognition are already available. However, up to now the utilisation of spatial scan techniques in detecting economic clusters is largely ignored (Kang, 2010). In this paper, the performance of the above-mentioned local spatial methods in identifying German R&D clusters is studied. Differences in cluster detection across the tests are traced. In particular, the contribution of Kulldorff's spatial scan test in detecting industry clusters is critically assessed.