CityFunIndex

Methodology

How the Fun Score works

The Fun Score is one number, 0–100, that answers a simple question: how much is there to do in this city? Here is exactly how it is built — the factors, the weights, and the math.

Every score is empirical, not editorial. Every factor traces to a real measurement drawn from federal, state and city open datasets, public APIs, and a small set of licensed commercial feeds. Nothing is made up.

One score, every city compared

Every city is measured on the same 23 factors and ranked head-to-head against every other city in the index. A score is never absolute — it always answers “compared with everywhere else, how does this city stack up?” 16 factors describe what makes a city fun; 7 describe what wears it down.

The 16 factors that lift a score

Each factor carries the weight shown — the share of the positive side of the score it accounts for. Follow any factor to see the cities that lead it.

  • Eats 9%

    Restaurants, food halls and the depth of the dining scene.

    Sources: OpenStreetMap restaurant counts, with Google Places ratings adjusting for venue quality.

  • Drinks 6%

    Bars, breweries, cocktail rooms and the nightlife spread.

    Sources: OpenStreetMap bars, pubs, nightclubs and craft breweries/distilleries, with Google Places ratings adjusting for venue quality.

  • Coffee 4%

    Cafés and the strength of the third-place coffee culture.

    Sources: OpenStreetMap café counts, with Google Places ratings adjusting for venue quality.

  • Live Music 9%

    Music venues and how often live shows actually happen.

    Sources: Ticketmaster and SeatGeek show listings, plus OpenStreetMap music venues.

  • Star Power 5%

    Notable people the city is known for, plus headliner acts that tour through.

    Sources: SeatGeek headliner popularity and Wikidata notable residents (rank-blended).

  • Outdoors 10%

    Parks, beaches and easy access to nature.

    Sources: OpenStreetMap parks, nature reserves, playgrounds and beaches, plus National Park Service units and Recreation.gov federal facilities.

  • Culture 8%

    Museums, theatres, galleries and arts institutions.

    Sources: OpenStreetMap museums, galleries and theatres plus geo-tagged Wikipedia landmarks, with Google Places ratings adjusting for venue quality.

  • Events 8%

    Festivals, fairs and a packed year-round events calendar.

    Sources: Ticketmaster and SeatGeek festivals, fairs and ticketed events.

  • Sports 6%

    Pro and college teams and the game-day energy around them.

    Sources: Ticketmaster and SeatGeek pro and college games, plus OpenStreetMap stadiums, sports centres and fitness centres.

  • Unique Hangouts 5%

    Escape rooms, arcades, trampoline parks and karaoke spots.

    Sources: OpenStreetMap escape rooms, arcades, trampoline parks and karaoke venues, with Google Places ratings adjusting for venue quality.

  • City Vibe 7%

    How much the city is talked about and searched for — news and search attention.

    Sources: GDELT news tone and Google Trends search interest (each percentile-ranked, then averaged).

  • Climate 4%

    How often the weather is good enough to get outside.

    Sources: NOAA climate normals — counts of comfortable days.

  • Transit Quality 7%

    How well a person without a car can move around the city.

    Sources: GTFS schedules via Transit.land, the Federal Transit Administration National Transit Database and US Census ACS B08301 commuter mode share.

  • Walkability 6%

    Density of fun within walking distance — sidewalks, intersections, mixed-use blocks.

    Sources: EPA National Walkability Index (block-group resolution, population-weighted to the city).

  • Higher Education 3%

    Density of college and university students — the engine behind a college town.

    Sources: NCES Integrated Postsecondary Education Data System (IPEDS) full-time-equivalent enrollment and US Census ACS age cohort data.

  • Bike & Micromobility 3%

    Protected bike lanes and a working public bikeshare system.

    Sources: OpenStreetMap protected cycleway segments and the MobilityData GBFS bikeshare-feed catalog.

The 7 factors that weigh a score down

Friction a city carries — the things that make all that fun harder to enjoy. These are scored too, then subtracted. Follow any factor to see the cities that carry the least of it.

  • Crime 24%

    Reported violent and property crime rates.

    Sources: FBI Crime Data Explorer and city open-data crime portals.

  • Traffic 14%

    Congestion and the time lost to commuting.

    Sources: US Census commute-time data (ACS).

  • Air Quality 14%

    Recent particulate matter and ozone levels — a trailing-week air-quality average.

    Sources: EPA AirNow and OpenAQ pollution readings (seven-day trailing average).

  • Disaster Risk 9%

    Exposure to natural hazards and severe events.

    Sources: FEMA National Risk Index.

  • Weather Extremes 9%

    Punishing heat, cold and storm extremes.

    Sources: NOAA Storm Events and climate-extremes data.

  • Economic Strain 20%

    Cost of living and housing pressure on residents.

    Sources: Bureau of Labor Statistics unemployment, HUD Fair Market Rent and US Census median income plus poverty rate.

  • Going-Out Cost 10%

    What a typical night out actually costs in this metro.

    Sources: Bureau of Labor Statistics regional Consumer Price Index — food away from home, recreation services and transportation services.

How a score is built, step by step

  1. Measure

    For every city we gather raw data behind each of the 23 factors — how many music venues, how many parks, how bad the traffic, and so on.

  2. Rank

    Raw numbers are not comparable across factors, so each one is turned into a 0–100 percentile: where this city sits among all the others on that single factor.

  3. Temper the extremes

    Top-end scores get diminishing returns — the difference between great and world-class counts for less than the difference between poor and decent. No single factor can run away with the score.

  4. Combine

    The 16 positive factors are blended by their weights into one upside number; the 7 negative factors into one friction number. The friction is then subtracted from the upside.

  5. Calibrate

    That combined number is mapped onto the final, published 0–100 Fun Score, so the scale stays steady and a city’s score moves only when its own data moves.

A worked example, end-to-end

Watching the math on one city demystifies the whole index. Here is Nashville, TN — a mid-size city with a strong live-music and food scene, traded against a sharp cost-of-living climb — followed from raw inputs to a published Fun Score under the live formula.

  1. 1. Measure

    For each of the 23 factors, we collect a raw value — for example restaurant density on the Eats factor, hot-day count on Climate, or average rent on Cost. Every factor has its own units (places per capita, crime rate, average rent, hot-day count) and they are not comparable to each other in that form. The raw measured values are part of the paid dataset, so the worked example below starts one step later, from the public percentile.

  2. 2. Rank to a percentile

    Each raw value becomes a 0–100 percentile rank against every other city. Nashville’s restaurant density puts it at percentile 48.87 on Eats — squarely mid-pack nationally on restaurant density (it punches up on live music and unique hangouts). For negative factors the rank is inverted: a city with low crime sits high.

  3. 3. Soften the extremes

    The percentile is run through a diminishing-returns curve so a top-1% city cannot run away with the score on a single factor. Across the bottom half the curve barely bends; at the top end a jump from the 95th to the 99th percentile counts for less than a jump from the 50th to the 60th. The output is each factor’s saturated 0–100 score.

  4. 4. Weight each side

    The 16 positive factor scores are blended by their weights into one upside composite; the 7 negative factor scores into one friction composite. The weights are the percentages shown above, and they sum to 1.0 on each side independently.

    For Nashville: upside = 56.28 friction = 55.10

  5. 5. Combine into one raw composite

    Friction is subtracted from upside, weighted by a single drag coefficient of 0.4 — tuned so a really bad negative factor can move the score but never dominate it. 

    raw composite = upside − 0.4 × friction
              = 56.28 − 0.4 × 55.10
              = 56.28 − 22.04
              = 34.24

  6. 6. Calibrate onto the 0–100 scale

    The raw composite is mapped onto the published 0–100 Fun Score by an affine calibration. The anchors lo = -2.40 and hi = 59.35 are the 2nd and 98th percentiles of the live raw-composite distribution, so the bulk of US cities land in the middle of the band and the scale moves with the country, not with a single year’s outliers. 

    fun score = clamp(round(5 + 93 × (raw − lo) / (hi − lo)), 0, 100)
          = clamp(round(5 + 93 × (34.24 − (-2.40)) / 61.75), 0, 100)
          = clamp(round(5 + 55.18), 0, 100)
          = 60

    Sixty puts Nashville squarely in the Fun band — lifted by live music, food, and unique hangouts, dragged back by cost-of-living and traffic. Two of the upstream numbers (the per-factor saturated scores and the calibration anchors) can be inspected on every city page and through the API, so the entire derivation above is reproducible from public data.

    Ties are surfaced, not hidden. Because the final step clamps the affine-mapped value to the 0–100 range and then rounds to an integer, several cities at the top of the distribution can land on the same published Fun Score. Whenever that happens, every consumer surface (homepage, city page, state and pillar leaderboards) tags the rank with a T prefix — T#1, T-1st — and a short tail (tied with N other cities). The competition rank is computed as 1 + count(cities with a strictly higher score), so co-leaders share #1 rather than having one of them arbitrarily promoted. See tied rank for the full definition.

Every bolded term above — calibration, raw composite, pillar, percentile rank — is defined on the glossary.

What a score means

Every Fun Score falls into one of five bands.

Band Fun Score
Exceptional 85–100
Very Fun 70–84
Fun 50–69
Some Fun 30–49
Quiet 0–29

How this differs from other city indexes

Every existing city ranking takes one slice of livability and turns it into a number. CityFunIndex is built on one different premise: a city’s appeal as a place to spend your weekends — what’s open, who’s playing, what you can eat, what’s outside the door — is its own measurable dimension and deserves its own algorithm. Here is how the major indexes line up on the four things that matter to a serious reader: what they measure, how many factors they expose, whether you can see the math, and whether you can re-weight it.

Index Primary focus Public factors Open methodology Re-weightable
Walk Score Walkability (plus Transit / Bike as separate single-factor scores) 1 Partial — method described, weights not exposed No
AreaVibes Livability composite — amenities, cost, crime, employment, housing, schools, weather 7 Categories listed, formula not published No
Niche Place-to-live grade, leaning on K-12 schools and demographics, augmented by user reviews ~8 Letter-grade rollups, no per-factor weights No
BestPlaces.net Side-by-side demographic comparison — cost of living, crime, climate, jobs ~10 Indices defined, weighting opaque No
CityFunIndex Recreational quality — what a city offers when you’re not at work 23 Every weight, formula and source on this page; open JSON API Yes — slide every factor

We are not trying to replace livability indexes — we are scoring a different question. A retiree shopping for low crime and good hospitals should still read AreaVibes; a parent comparing K-12 catchments should still read Niche. A traveller, a weekender, a relocating remote worker or a small-venue operator looking at where to open the next room — that is the reader CityFunIndex was built for.

Where the data comes from

Every input is public, openly licensed or commercially licensed for our use. No single source decides anything — each factor blends several — and every per-factor source is named on each factor’s own row above. The full breakdown by source category:

Open government data

  • NOAA — climate normals and storm events for the climate and weather-extremes factors.
  • EPA AirNow — daily air-quality readings, averaged over a trailing 7-day window so a single clean morning or smoky afternoon does not swing a city's score.
  • FBI Crime Data Explorer — reported violent and property crime rates.
  • US Census Bureau (ACS) — commute times, household income and population baselines.
  • Bureau of Labor Statistics — metro unemployment series for the economic-strain factor.
  • HUD — small-area fair-market rents.
  • FEMA National Risk Index — per-county exposure to natural hazards.
  • National Park Service & Recreation.gov — national park and federal recreation sites.

Open public datasets

  • OpenStreetMap — restaurant, bar, café, music-venue, stadium, museum, park and beach counts per city — the spine of every venue-density factor.
  • Wikidata & Wikipedia — notable people born in the city and geo-tagged landmarks near it.
  • OpenAQ — community air-quality measurements where federal coverage is thin.
  • GDELT — news-tone signal feeding the City Vibe factor.
  • Google Trends — per-city search-interest signal feeding the City Vibe factor (orthogonal coverage where news mentions are thin).
  • City open-data crime portals — supplement to the federal feed for cities with active disclosure.

Licensed commercial APIs

  • Google Places — restaurant, bar, café, museum and arcade ratings, which adjust the OpenStreetMap counts for venue quality.
  • Ticketmaster — concerts, shows, pro and college sports, festivals.
  • SeatGeek — secondary-market event coverage and headline-performer popularity for the Star Power factor.

One boundary matters for the paid dataset: what we license and sell is only the data CityFunIndex computes — the per-city Fun Scores, factor scores, percentiles and aggregate measurements. We never redistribute the underlying third-party records themselves (no individual place listings, ratings or reviews), which keeps the dataset within each source provider’s redistribution terms, including Google’s.

Honest limitations

The Fun Score is a model, not a verdict. The factor weights and band cut points shown above are the current values and may be refined as the algorithm is re-calibrated against the live data. Every city page carries a freshness chip showing when its score was last computed and which algorithm version produced it. Fun is personal — treat the score as a starting point, not the last word.

Why a smaller city can outrank a big one

Most of the amenity factors — restaurants, bars, music venues, museums, parks — are counted per resident, not in raw totals. That is a deliberate choice: the score answers “how much is there to do for the people who actually live here?”, not “which metro is biggest?” A large city always has more venues in absolute terms, but spread across millions of residents the amount available per person can be lower than in a compact, amenity-dense small city.

One honest consequence: walkable, amenity-rich smaller cities and well-known visitor towns can rank above sprawling major metros. The current top of the index, Portland, Maine, is exactly this case — a small city with an outsized concentration of restaurants, bars and cultural venues per resident. If you are weighing the sheer scale and variety of a huge metro, read the per-factor breakdown on each city page rather than the headline number alone, or use the personalize tool to down-weight the per-capita density factors and re-rank on what matters to you.