Why Close

Close publishes the travel-time matrix behind close.city: for every US census block, how long it takes to reach the nearest amenity of each kind, on foot, by bike, and by transit. It is the raw material for walkability analysis, not a finished score.

What you can build

• Neighbourhood access profiles. For any block or point, the walk, bike, and transit time to groceries, schools, parks, transit, healthcare, and more.
• Coverage and equity analysis. Which blocks can reach a full basket of amenities within fifteen minutes, and which cannot — joined to census demographics by GEOID.
• Catchments and walksheds. The blocks that can reach a given store, or the area reachable from a location within a time budget, as GeoJSON contours.
• Site selection. Rank candidate locations by what is reachable around them.

The SDKs carry three worked examples of this — an amenity basket, a competitor walkshed, and a home search — in both Python and R.

How this differs from a walkability score

A walkability score gives one number per address. It is convenient, but it is opaque: you cannot see which amenities drove it, change the weighting, or join it to other data.

Close gives the measurements underneath instead — the actual travel time to each amenity type, per block. That means you can:

• Choose what matters. Weight groceries over gyms, or count only what is reachable by transit. The score is yours to define.
• Reproduce it. Every component carries a vintage (GET /v1/meta/vintage), so an analysis can be pinned to a data version and repeated. See Citing Close.
• Join it. Block GEOIDs link directly to census demographics and boundaries.
• Map it. The SDKs return geopandas / sf objects ready to plot.

If you want a single readable score, you can still compute one — but you compute it from data you can inspect, rather than trusting a black box.

Who it is for

• Researchers studying access, equity, and the built environment, who need documented methods and a citable, versioned source.
• Planners measuring coverage and gaps across a city or county.
• Data scientists who want tidy travel-time frames in Python or R.
• AI agents doing spatial analysis on a user’s behalf — see For AI agents.

Start with the Quickstart.