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city
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10 values
latitude
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-37.8
59.9
longitude
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-122.68
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golden_drop_score
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cafe_001
Blue Bottle Coffee - Shinjuku
Tokyo
Japan
35.6894
139.6917
local
52
true
false
cafe_002
Cafe de Flore
Paris
France
48.854
2.3325
independent
49
true
true
cafe_003
Stumptown Coffee Roasters
Portland
United States
45.5231
-122.6765
local
54
true
true
cafe_004
Kaffeine
London
United Kingdom
51.5194
-0.1383
independent
51
true
false
cafe_005
Tim Wendelboe
Oslo
Norway
59.9167
10.7589
independent
55
true
false
cafe_006
Cafe Tortoni
Buenos Aires
Argentina
-34.6082
-58.3803
independent
47
false
true
cafe_007
% Arabica Kyoto
Kyoto
Japan
35.0116
135.7681
local
53
false
true
cafe_008
Starbucks Reserve Roastery
Seattle
United States
47.6145
-122.3283
global
44
true
true
cafe_009
The Barn
Berlin
Germany
52.5296
13.4091
independent
50
true
false
cafe_010
Onibus Coffee
Tokyo
Japan
35.633
139.7085
independent
48
true
false
cafe_011
Workshop Coffee
London
United Kingdom
51.5218
-0.109
independent
49
true
false
cafe_012
Fuglen
Oslo
Norway
59.9207
10.7341
independent
51
true
true
cafe_013
Heart Coffee Roasters
Portland
United States
45.5275
-122.6592
independent
50
true
true
cafe_014
Cafe Central
Vienna
Austria
48.2107
16.3653
independent
46
true
true
cafe_015
Sightglass Coffee
San Francisco
United States
37.7679
-122.4108
independent
52
true
true
cafe_016
Coutume Cafe
Paris
France
48.8501
2.3175
independent
48
true
true
cafe_017
Proud Mary
Melbourne
Australia
-37.7985
144.9835
independent
53
true
true
cafe_018
La Colombe Coffee
New York
United States
40.7195
-73.9987
local
51
true
false
cafe_019
Finca de Aroma
Medellin
Colombia
6.2476
-75.5658
independent
47
true
true
cafe_020
Bonanza Coffee
Berlin
Germany
52.5344
13.4105
independent
49
true
false

CoffeeTrove Global Cafe Dataset

A geospatial dataset of 440,000+ cafes worldwide, each scored using the proprietary Golden Drop scoring system. Compiled and maintained by CoffeeTrove, the world's most comprehensive open coffee discovery platform.

Dataset Description

This dataset provides structured records for cafes, roasters, and specialty coffee shops across 195 countries. Each entry includes geographic coordinates, address components, operating hours, amenity flags, and a computed quality score that synthesizes multiple data signals into a single comparable metric.

Coverage

Region Cafes Top Countries
North America 128,000+ United States, Canada, Mexico
Europe 112,000+ United Kingdom, Germany, France, Italy, Spain
Asia-Pacific 98,000+ Japan, South Korea, Australia, Thailand
Latin America 52,000+ Brazil, Colombia, Argentina
Middle East & Africa 31,000+ Turkey, UAE, South Africa
Other 19,000+ Various

Golden Drop Scoring

Every cafe receives a Golden Drop score (0-100) computed from weighted signals:

  • Data completeness (0-55 points) -- Website, phone, hours, photos, menu, social links
  • Chain classification -- Global chains (Starbucks, Costa), local chains (Blue Bottle, Intelligentsia), and independents each receive distinct treatment. Independent cafes earn a +10 point bonus, reflecting the platform's commitment to surfacing local businesses
  • Review synthesis -- Aggregated sentiment from multiple review platforms, normalized to remove platform bias
  • Freshness -- Recency of data verification and update frequency

Scores currently range from 28 to 57 across the full dataset, as manual review and community contribution features are still being rolled out. The scoring methodology is designed to scale as richer data sources are integrated.

Data Fields

Each record contains:

  • Identity: Name, slug, chain type (global/local/independent)
  • Location: Latitude, longitude, full address, city, country, neighborhood
  • Contact: Website URL, phone number
  • Operations: Opening hours (structured), amenity flags (wifi, outdoor seating, wheelchair access)
  • Scores: Golden Drop score, component sub-scores
  • Metadata: Data source, last verified date, photo count

Intended Use

Researchers and developers can use this dataset for:

  • Geospatial analysis -- Study cafe density patterns, urban coffee culture, and neighborhood walkability
  • Recommendation engines -- Build location-aware suggestion systems using scoring signals and amenity preferences
  • Economic research -- Analyze the relationship between cafe density and local economic indicators
  • NLP training -- Use cafe names and descriptions for named entity recognition in the food and beverage domain

The interactive map visualization is available at CoffeeTrove Map, and the full knowledge base covering brewing methods, bean origins, and equipment is at CoffeeTrove Knowledge.

Sample

The included sample_cafes.csv contains 20 representative cafes across multiple countries, with core fields and Golden Drop scores. The full dataset powers the live CoffeeTrove platform.

License

MIT License. Attribution appreciated.

Citation

@dataset{coffeetrove_cafes_2026,
  title={CoffeeTrove Global Cafe Dataset},
  author={CoffeeTrove},
  year={2026},
  url={https://coffeetrove.com}
}
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