H3 hexagonal grids versus road segments: Spatial aggregation and built environment effects on cycling speed

Understanding cycling behavior is increasingly important for sustainable mobility planning, yet analyzing cycling data remains challenging due to route choice flexibility and the limited suitability of existing map-matching techniques for cycling. This study turns to hexagonal spatial aggregation as a potential alternative and examines how the choice of spatial unit affects modeling outcomes by comparing H3 hexagons and conventional street segments in a case study of cycling speeds. Parallel datasets are constructed from identical GPS traces, and equivalent Generalized Additive Models are estimated on each representation. The hexagon-based model achieves higher predictive accuracy ( 𝑅 2 = 0 . 4 3 ) than the segment-based model ( 𝑅 2 = 0 . 3 2 ), and produces smoother, more stable effect estimates. Differences in variable interpretation and model results show that the two representations capture the cycling environment in fundamentally different ways. These findings demonstrate that spatial unit choice is an important methodological decision that shapes both the interpretation and performance of mobility models.