Regional configuration can reveal important aspects about city sustainability, as local-regional interactions shape the evolution and inner geography of urban settlements. However, modelling these large-scale structures remains a challenge, due to their sheer size as physical objects. Despite recent improvements in processing power and computing methods, extensive time periods are still required for ordinary microprocessors to model network centralities in road-graphs with high element counts, connectivity and topological depth. Generalization is often the chosen option to mitigate time-constraints of regional network complexity. Nevertheless, this can impact visual representation and model precision, especially when multiscale comparisons are desired. Tests using Normalized Angular Choice (NACH), a Space Syntax mathematical derivative of Betweenness Centrality, found recursive visual similitudes in centrality spatial distribution when modelling distinct scaled map sections of the same large regional network structure. Therefore, a sort of homothetic behavior is identified, since statistical analyses demonstrate that centrality values and distributions remain rather consistent throughout scales, even when considering edge effects. This paper summarizes these results and considers homotheties as an alternative to extensive network generalization. Hence, data maps can be constructed sooner and more accurately as “pieces of a puzzle”, since each individual lesser scale graph possesses a faster processing time.

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