Flows and inter-linkages between and within polycentric metropolitan regions have become a fundamental topic in regional sciences. The knowledge economy as a primary driver of spatial restructuring is forming these relations by generating knowledge within a spatially fine graded division of labor. This process drives companies to cooperate in intra firm and extra firm networks which in turn evoke patterns of interdependent spatial entities. The aim of the paper is twofold. Firstly, we analyze spatial patterns within these firm networks and secondly we combine this network approach with the development of the economic and spatial structure of German agglomerations. Inspired by formal social network analysis and spatial association statistics we apply methods to discover spatial clustering within relational data. We assume that relations between and within polycentric Mega-City Regions in Germany and its neighboring areas constitute a new form of hierarchical urban systems. Network analysis will help to detect locations of high centrality; cluster analyses of location-based data may show specific regional patterns of connectivity. We hypothesize that the position of locations within the functional urban hierarchy depends on the spatial scale of analysis: global, European, national or regional. Furthermore, we combine this relational perspective with an analysis of the economic development within these spatial entities. Here we assume that intensive interaction between functional urban areas has a high influence on their performance over time with regard to output indicators like labor, value-added and gross domestic product. Therefore we apply methods of spatial and network autocorrelation. We hypothesize that relational proximity influences economic development more intensively than effects of agglomeration and geographical proximity do.
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