Real data, one month, one grid
The map runs on public data only: every Citi Bike trip in October 2026 and every shop, restaurant, bar and office mapped in OpenStreetMap. Both are joined on the same quadkey grid, so each cell carries its own 24-hour pulse and its own business mix.
Activity data shows when a place is busy. A field check shows what is there today.
The cells where the business mix and the daily pulse disagree are the ones worth a visit. Open, vacant or for lease, a 5-minute count and a photo go into the same grid the same day, even with no signal on the street.
Flag. The map ranks cells by mismatch between pulse and mix. The top ones become a visit list.
Check. A field app records status, a foot-traffic count, a photo and GPS. It saves on the phone when there is no signal.
Sync. One server receives every check and updates the cell, so the score and the evidence sit together.
Visit list · cell 1 of 5
Where Geolink plugs in
Small analytics teams usually have the models. What slows them down is everything around the model: the map clients see, the data getting in on time, and proof from the street. These are the pieces we build.
A time-aware web map on your own grid
A browser map like the one above, reading straight from your grid store (ClickHouse, Postgres or files), with a time slider, cell inspector and your brand.
Clients see the pulse behind a score, not only the number.
Upload a list, get every address scored
Lenders and insurers start with a whole portfolio. A screen that geocodes a CSV, scores each address, sorts by risk and exports back to Excel or BI tools.
One screen that turns a property list into a ranked risk view.
Offline field app that feeds the score
Inspectors or partners record status, counts and photos. It works without signal and syncs to one server. Each check is tied to its cell and timestamp.
Public storefront vacancy data is self-reported once or twice a year. This captures what is on the street the day it is visited.
Monthly loads that check themselves
Scheduled jobs that pull new mobility and business data, compare it to last month, log where it came from, and stop a release when a source drops or drifts.
Fewer surprises when a data vendor changes, and an audit trail for consent and provenance rules.