Gridnberg: A Topography-Aware Pedestrian Routing Dataset for New York City
2026-07-24 • Human-Computer Interaction
Human-Computer InteractionComputers and Society
AI summaryⓘ
The authors point out that city maps used for analyzing pedestrian routes usually assume that streets are flat, which isn't true in hilly areas. They created Gridnberg, a new dataset for New York City that adds elevation information to street maps to better represent slopes and hills. This helps calculate walking routes considering not only distance but also how steep or comfortable a path is, improving accessibility analysis. Their dataset keeps almost all original street data while making routing more realistic by including height differences.
urban network analysisplanar graphspedestrian routingelevation dataNYCWalks networkslope scoreaccessibilitytopography-awarecumulative ascentroute impedance
Authors
Ariel Noyman
Abstract
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.