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• W4210550000 abstract "Pedestrian route choice, wayfinding behaviour and movement pattern research rely on objective spatial configuration model and analysis. In 3D indoor and outdoor multi-level buildings and urban built environments (IO-ML-BE), spatial configuration analysis allows to quantify and control for route choice and wayfinding complexity/difficulty. Our contribution is to compare the interaction of the level of definition (LOD) of indoor and outdoor multi-level pedestrian network spatial models and complexity metric analyses. Most studies are indoor or outdoor and oversimplify multi-level vertical connections. Using a novel open data set of a large-scale 3D centreline pedestrian network which implement transport geography 2D data model principles in 3D, nine spatial models and twelve spatial complexity analyses of a large-scale 3D IO-ML-BE are empirically tested with observed pedestrian movement patterns ( N = 17,307). Bivariate regression analyses show that the association with movement pattern increases steadily from R 2 ≈ 0.29 to 0.56 (space syntax, 2.5D) and from R 2 ≈ 0.54 to 0.72 (3D sDNA) as the 3D transport geography spatial model LOD and completeness increases. A multivariate stepwise regression analysis tests the bi-variate findings. A novel 3D hybrid angular-Euclidean analysis was tested for the objective description of 3D multi-level IO-ML-BE route choice and wayfinding complexity. The results suggest that pedestrian route choice, wayfinding and movement pattern analysis and prediction research in a multi-level IO-ML-BE should use high-definition 3D transport geography network spatial model and include interdependent outdoor and indoor spaces with detailed vertical transitions." @default.
• W4210550000 created "2022-02-08" @default.
• W4210550000 creator A5044178255 @default.
• W4210550000 creator A5071029793 @default.
• W4210550000 date "2022-02-23" @default.
• W4210550000 modified "2023-10-14" @default.
• W4210550000 title "Walking in the cities without ground, how 3d complex network volumetrics improve analysis" @default.
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• W4210550000 doi "https://doi.org/10.1177/23998083211070567" @default.
• W4210550000 hasPublicationYear "2022" @default.
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