Spatial autocorrelation study in urban transport networks using Voronoi diagram-based interpolation
Abstract
This study uses geoinformatics-cartographic methods to investigate the spatial correlation patterns of the Budapest transport network on two independent databases (independent traffic counting node sets, a total of 19.6 million measurements). Spatial assignment was performed using Voronoi diagram (Thiessen polygon)-based interpolation, which ensured an objective spatial division between four meteorological stations and 1196 nodes georeferenced in the EOV coordinate system. Graph theory modeling and Pearson spatial autocorrelation analysis revealed sample size-dependent results: the larger sample (A1) shows a 12.4% higher average correlation coefficient (r=0.579 vs. r=0.507). The distance-independence test contradicts Tobler's first geographical law: node pairs above 5 km and below 500 m show practically the same spatial similarity (42.8% vs. 41.7%). The results support that topological relationships are more important in spatial autocorrelation than Euclidean distance. The study contributes to the methodological development of GIS-based transport network analysis.
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