Methodology
We turn open, derived and modeled data into clear scores — and we show our confidence.
Data
Where our intelligence comes from
Open & community data
OpenStreetMap, Overture Maps and city open data provide the base layer of infrastructure, amenities and transport.
Transit & mobility
Public transport schedules (GTFS where available) inform commute and transit-access scoring.
Enrichment after intent
Paid enrichment (e.g. places data) is only used after a report is purchased — never during browsing.
AI narrative
A language model turns the structured signals into clear, decision-focused narrative and recommendations.
Scoring
How scores are built
Every score is normalized to a 0–100 scale and grouped into clear bands (Low, Moderate, Good, High, Excellent) so a number always has a plain-language meaning. Scores combine multiple weighted indicators relevant to the decision at hand.
We report risk indicators rather than absolute judgements. We avoid unsafe claims like “safe” or “unsafe” neighborhoods and instead surface objective evidence and let you weigh it for your situation.
Confidence
We show our confidence
Every report includes a confidence note describing data freshness and coverage. When data is sparse, we say so.