Gridnberg dataset brings topography-aware pedestrian routing to NYC
Most city routing ignores hills—Gridnberg adds elevation data for accurate paths.
Ariel Noyman’s new Gridnberg dataset tackles a blind spot in urban network analysis: hills. While most city routing models treat streets as flat planar graphs, Gridnberg (“grid-n-berg”) layers elevation data onto New York City’s pedestrian network. Built on the NYCWalks dataset, it pulls vertex-level elevations from the New York City Planimetric Database, averaging observations within a 50-meter radius for each geometry point. The final release retains 313,184 of 315,577 source segments—a 99.24% preservation rate—ensuring minimal data loss while adding crucial topography information.
Gridnberg computes three distinct routing costs: standard horizontal distance, a comfort-oriented slope score that minimizes steep climbs, and an accessibility-sensitive slope score designed for users with mobility constraints. This allows researchers, urban planners, and app developers to compare routes based on terrain difficulty rather than just distance. The dataset is fully reproducible, supporting transparent scenario analysis and enabling similar topography-aware modeling in other cities. For anyone building pedestrian navigation tools or studying walkability, Gridnberg provides the missing piece: realistic, hill-aware routing.
- Gridnberg enriches NYCWalks with elevation data from the NYC Planimetric Database, averaging points within 50m radius.
- Retains 99.24% of original segments (313,184 of 315,577).
- Provides three routing costs: horizontal distance, comfort-oriented slope, and accessibility-sensitive slope.
Why It Matters
Enables apps and planners to create realistic pedestrian routes that account for hills, improving accessibility and comfort.