AI 中文总结
介绍纽约市地形感知行人路径数据集Gridnberg，通过丰富NYCWalks网络顶点级海拔，计算三种路径成本，保留多数源路段，支持地形感知分析、场景比较及改善行人网络表示。
AI 中文摘要
城市并非平坦，但城市网络分析常将街道视为平面图。这种简化影响建模阻抗、路径选择和可达性解释，尤其在替代路径坡度不同时。本文介绍Gridnberg（网格与山脉之意），一个纽约市地形感知行人路径数据集。该数据集用纽约市平面数据库的顶点级海拔丰富NYCWalks网络。对每个行人网络几何顶点，工作流程在50米半径内平均选定海拔观测值，保留有完整顶点支持的路段，并使用特定方向的累积上升和下降来计算三种路径成本：水平距离、舒适度导向的坡度得分和可达性敏感的坡度得分。发布版本保留了315577个源路段中的313184个（99.24%）。Gridnberg支持可重复的地形感知分析、透明的场景比较，并改善纽约及其他城市的行人网络表示。
英文摘要
Cities are rarely flat, yet urban network analysis usually represents streets as planar graphs. This simplification affects modeled impedance, route choice, and the interpretation of accessibility, particularly where alternative paths differ in grade. This paper introduces Gridnberg ('grid-n-berg', grid and mountain), a topography-aware pedestrian routing dataset for New York City. The dataset enriches the NYCWalks network with vertex-level elevations derived from the New York City Planimetric Database. For each pedestrian-network geometry vertex, the workflow averages selected elevation observations within a 50 m radius, retains segments with complete vertex support, and uses direction-specific cumulative ascent and descent to calculate three routing costs: horizontal distance, a comfort-oriented slope score, and an accessibility-sensitive slope score. The release retains 313184 of 315577 source segments (99.24%). Gridnberg supports reproducible terrain-aware analysis, transparent scenario comparison, and improved pedestrian-network representations in New York and other cities.
Comments5 pages, 4 figures
