Detecting spatial homogeneity in the World Trade Web with Detrended Fluctuation Analysis

• We applied DFA analysis to detect homogeneity of space in embedded networks.
• We encoded the weighted links according to the distance of nodes.
• We proved that the methodology is sound and practical.
• We enhanced the measures with null models from exponential random graphs.
• We solve the so called “distance puzzle”, finding that distance became less binding.

In a spatially embedded network, that is a network where nodes can be uniquely determined in a system of coordinates, links’ weights might be affected by metric distances coupling every pair of nodes (dyads). In order to assess to what extent metric distances affect relationships (link’s weights) in a spatially embedded network, we propose a methodology based on DFA (Detrended Fluctuation Analysis). DFA is a well developed methodology to evaluate autocorrelations and estimate long-range behavior in time series. We argue it can be further extended to spatially ordered series in order to assess autocorrelations in values. A scaling exponent of 0.5 (uncorrelated data) would thereby signal a perfect homogeneous space embedding the network. We apply the proposed methodology to the World Trade Web (WTW) during the years 1949–2000 and we find, in some contrast with predictions of gravity models, a declining influence of distances on trading relationships.