Food Scene
The availability of mapped places to eat and buy food.
- Data source
- OpenStreetMap
- Ranking layer
- Lifestyle Fit
- Base priority weight
- 15%
How CityFit computes this score
CityFit reuses its OSM daily-life collection within 8 km of the stored city center. Restaurants, cafes and fast-food places supply 75% of the score; food shops and markets supply 25%. Counts are adjusted for land area, scored logarithmically, and blended.
| Component | Relative weight | Full-score target |
|---|---|---|
| Restaurants, cafes and casual food | 0.75 | 20,000 mapped places |
| Food shops and markets | 0.25 | 7,000 mapped places |
From mapped places to a 0–100 score
- Collect mapped places. Query the relevant places around the stored city center through Overpass, an OpenStreetMap query service. Collections are cached instead of counted again on every visit.
- Adjust for land area. Use the land within the 8 km search circle, a reference area of about 201 km², and a maximum multiplier of four. If land-area data is missing, keep the original counts.
- Convert counts to scores. Logarithmic scoring gives additional places diminishing influence. Each component reaches 100 at its full-score target and is capped there.
- Combine the components. Take the weighted average of available scores and round to one decimal place. Divide relative weights by their total; they are not extra points.
View the scoring formula
adjusted_count = count × min(201.06 / max(land_area_km², 1), 4)
component_score = min(100 × ln(1 + adjusted_count) / ln(1 + target), 100)
score = sum(component_score × weight) / sum(available_weights)The 8 km circle is a consistent sampling area, rather than the city’s administrative boundary. Mapping completeness and tagging practices vary between cities. Missing collection values can be filled with zero by the score-building pipeline, so a low value can also reflect incomplete mapping.
How CityFit uses it
15% base weight in Lifestyle Fit. Your priority slider multiplies this weight, and CityFit takes the weighted average of the active lifestyle scores.
Changing priorities adjusts the blend; it does not recalculate the underlying food scene score. The overall Practical Fit and Lifestyle Fit sliders determine how those two layers contribute to CityFit Score.
Country and region filters select which cities you see. Metric colors continue to use the global scale; percentiles describe a city’s standing across the full dataset and are separate from the raw score.
What the score can tell you
The score measures mapped availability. It does not rate food quality, cuisines, reviews or local reputation.