Spatial point pattern analysis and industry concentration

Traditional measures of spatial industry concentration are restricted to given areal units. They do not make allowance for the fact that concentration may be differently pronounced at various geographical levels. Methods of spatial point pattern analysis allow one to measure industry concentration at a continuum of spatial scales. While common distance-based methods are well applicable for sub-national study areas, they become inefficient in measuring concentration at various levels within industrial countries. This particularly applies in testing for conditional concentration where overall manufacturing is used as a reference population. Using Ripley’s K function approach to second-order analysis, we propose a subsample similarity test as a feasible testing approach for establishing conditional clustering or dispersion at different spatial scales. For measuring the extent of clustering and dispersion, we introduce a concentration index of the style of Besag’s (J R Stat Soc B, 25:294, 1977) L function. The new index can be employed to measure the extent of substantial clustering and dispersion. The K function approach is employed to identifying measuring industry concentration in Germany.