Minneapolis Skyway Routing System Amenity-aware indoor navigation · Spatial Data Science and Python

This project builds a routing system for the Minneapolis skyway network that goes beyond shortest-distance navigation. It gives users route options based on what they want along the way, such as food, retail, or open space, turning the skyway into a navigable urban layer rather than an unmapped indoor system.

The aim was to create a routing tool that treats the Minneapolis skyway as a real pedestrian network, especially in a city where climate strongly affects how people move indoors.

I extracted the pedestrian network using OSMnx, converted it into a graph, and reduced it to the skyway area. I then mapped nearby amenities and attached them to the network so that each route segment carried both distance and place-based information.

This project used geospatial processing to clip the network, organize amenities, and match them to graph edges. The result was a routing system that combined network structure with urban context.

The routing logic modifies edge cost based on user preference. Instead of optimizing only for the shortest path, the system lowers route cost along edges with more relevant amenities nearby, allowing for food-oriented, retail-oriented, or open-space-oriented paths.

The final system produced meaningfully different route options depending on user priorities. It showed that once amenities are embedded into the network, routing begins to reflect urban experience rather than distance alone.