Grepsr | Comprehensive Dataset of Fast-food Chains' Store (Starbucks, Mcdonalds, Subway, & more) Location

#	Store Name	display_link	link	address	plus_code	rating	reviews_cnt	latitude	longitude	claimed	open_hours_Monday	open_hours_Tuesday	open_hours_Wednesday	open_hours_Thursday	open_hours_Friday	open_hours_Saturday	open_hours_Sunday	fid	map_id_encoded	map_id	map_link	footdata	image_1	image_2	category_1	accessibility_1	accessibility_2	children_1	amenities_1	amenities_2	amenities_3	children_2	service_options_1	service_options_2	service_options_3	service_options_4	planning_1	payments_1	category_2	category_3	category_4	category_5	payments_2	payments_3	accessibility_3	planning_2	from_the_business_1	summary	description	highlights_1	offerings_1	category_6	category_7	category_8	crowd_1	crowd_2	getting_here_1	from_the_business_2	accessibility_4	category_9	category_10	payments_4	service_options_5	highlights_2	popular_for_1	popular_for_2	popular_for_3	offerings_2	offerings_3	offerings_4	offerings_5	offerings_6	offerings_7	offerings_8	offerings_9	offerings_10	offerings_11	dining_options_1	dining_options_2	dining_options_3	dining_options_4	dining_options_5	atmosphere_1	atmosphere_2	atmosphere_3	atmosphere_4	crowd_3	children_3	parking_1
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Volume

+ Millions Records

Data Quality

98%

filler rate

Avail. Formats

.bin, .json, and .xml

File

Coverage

249

Countries

History

weeks

[Sample] Fast Food Chains Store Location.csv

Attribute	Type	Example
Store Name	String	Starbucks
display_link	String	starbucks.com
link	String	https://www.starbucks.com/store-locator/store/18276/&opi=...
address	String	254-21 Horace Harding Expy, Queens, NY 11362
plus_code
rating	Float	4.1
reviews_cnt	Integer	370
latitude	Float	40.7645076
longitude	Float	-73.7262876
claimed	Integer	1
open_hours_Monday	String	5 AM–9 PM
open_hours_Tuesday	String	5 AM–9 PM
open_hours_Wednesday	String	5 AM–9 PM
open_hours_Thursday	String	5 AM–9 PM
open_hours_Friday	String	5 AM–9 PM
open_hours_Saturday	String	6 AM–9 PM
open_hours_Sunday	String	6 AM–9 PM
fid	String	0x89c289e723aa6131:0x16100dfe8fee2083
map_id_encoded	String	ChIJMWGqI-eJwokRgyDuj_4NEBY
map_id	String	0x89c289e723aa6131:0x16100dfe8fee2083
map_link	String	https://www.google.com/maps/place/data=!3m1!4b1!4m2!3m1!1...
footdata	String	[]
image_1	String	https://lh5.googleusercontent.com/p/AF1QipPag_i-nyacoWEcL...
image_2	String	https://lh5.googleusercontent.com/p/AF1QipPag_i-nyacoWEcL...
category_1	String	Coffee shop
accessibility_1	String	Has wheelchair accessible entrance
accessibility_2	String	Has wheelchair accessible parking lot
children_1	String	Good for kids
amenities_1	String	Has restroom
amenities_2	String	Has Wi-Fi
amenities_3	Text
children_2	Text
service_options_1	String	Offers delivery
service_options_2	String	Has drive-through
service_options_3	String	Offers takeout
service_options_4	String	Serves dine-in
planning_1	Text	Doesn't accept reservations
payments_1	String	Accepts credit cards
category_2	String	Breakfast restaurant
category_3	String	Cafe
category_4	String	Coffee store
category_5	String	Espresso bar
payments_2	String	Accepts debit cards
payments_3	String	Accepts NFC mobile payments
accessibility_3	String	Has wheelchair accessible restroom
planning_2	Text
from_the_business_1	Text
summary	String	Iconic Seattle-based coffeehouse chain
description	String	Seattle-based coffeehouse chain known for its signature r...
highlights_1	String	Serves great coffee
offerings_1	String	Serves coffee
category_6	String	Internet cafe
category_7	Text
category_8	Text
crowd_1	String	Popular with college students
crowd_2	String	Family-friendly
getting_here_1	Text
from_the_business_2	Text
accessibility_4	String	Has wheelchair accessible seating
category_9	Text
category_10	Text
payments_4	Text
service_options_5	Text
highlights_2	String	Has great tea selection
popular_for_1	String	Popular for breakfast
popular_for_2	String	Popular for lunch
popular_for_3	String	Good for solo dining
offerings_2	String	Sells prepared foods
offerings_3	String	Quick bite
offerings_4	String	Serves small plates
offerings_5	Text
offerings_6	Text
offerings_7	Text
offerings_8	Text
offerings_9	Text
offerings_10	Text
offerings_11
dining_options_1	String	Serves breakfast
dining_options_2	String	Serves lunch
dining_options_3	String	Serves dessert
dining_options_4	Text
dining_options_5	Text
atmosphere_1	String	Casual
atmosphere_2	String	Cozy
atmosphere_3	String	Trendy
atmosphere_4	Text
crowd_3	String	Good for groups
children_3	Text
parking_1	Text

Product Attributes

Attribute	Type	Example
Store Name	String	Starbucks
display_link	String	starbucks.com
website_link	String	https://www.starbucks.com/store-locator/store/18276/&opi=...
address	String	254-21 Horace Harding Expy, Queens, NY 11362
plus_code	String	87G8M625+8F
rating	String	4.1
reviews_cnt	String	370
latitude	String	40.7645076
longitude	String	-73.7262876
claimed	String	1
open_hours_Monday	String	5 AM–9 PM
open_hours_Tuesday	String	5 AM–9 PM
open_hours_Wednesday	String	5 AM–9 PM
open_hours_Thursday	String	5 AM–9 PM
open_hours_Friday	String	5 AM–9 PM
open_hours_Saturday	String	6 AM–9 PM
open_hours_Sunday	String	6 AM–9 PM

This dataset serves as a valuable resource for businesses, researchers, and analysts seeking in-depth insights into the distribution, characteristics, and operational details of fast food chains and their locations worldwide.

Potential Applications of the Dataset: 1. Geospatial Information: Precise geographical coordinates for each Walgreens store, enabling accurate mapping and spatial analysis. State-wise and city-wise breakdown of store locations for a comprehensive overview. 2. Store Details: Store addresses, including street name, city, state, and zip code, facilitating easy identification and location-based analysis. Contact information, such as phone numbers, providing a direct link to store management. 3. Operational Attributes: Store opening and closing hours, aiding businesses in strategic planning and market analysis. Services and amenities are available at each location, offering insights into the diverse offerings of Walgreens stores. 4. Historical Data: Historical data on store openings and closures, providing a timeline perspective on Walgreens' expansion and market presence. 5. Demographic Insights: Demographic information of the areas surrounding each store, empowering users to understand the local customer base. 6. Comprehensive and Up-to-Date: Regularly updated to ensure the dataset reflects the latest information on Walgreens store locations and attributes. Detailed data quality checks and verification processes for accuracy and reliability. The dataset is structured in a flexible format, allowing users to tailor their queries and analyses based on specific criteria and preferences.

Africa (58)

Algeria

Angola

Benin

Botswana

Burkina Faso

Burundi

Cabo Verde

Cameroon

Central African Republic

Chad

Comoros

Congo

Congo (Democratic Republic of the)

Côte d'Ivoire

Djibouti

Egypt

Equatorial Guinea

Eritrea

Ethiopia

Gabon

Gambia

Ghana

Guinea

Guinea-Bissau

Kenya

Lesotho

Liberia

Libya

Madagascar

Malawi

Mali

Mauritania

Mauritius

Mayotte

Morocco

Mozambique

Namibia

Niger

Nigeria

Rwanda

Réunion

Saint Helena, Ascension and Tristan da Cunha

Sao Tome and Principe

Senegal

Seychelles

Sierra Leone

Somalia

South Africa

South Sudan

Sudan

Swaziland

Tanzania, United Republic of

Togo

Tunisia

Uganda

Western Sahara

Zambia

Zimbabwe

Asia (51)

Afghanistan

Armenia

Azerbaijan

Bahrain

Bangladesh

Bhutan

Brunei Darussalam

Cambodia

China

Cyprus

Georgia

Hong Kong

India

Indonesia

Iran (Islamic Republic of)

Iraq

Israel

Japan

Jordan

Kazakhstan

Korea (Democratic People's Republic of)

Korea (Republic of)

Kuwait

Kyrgyzstan

Lao People's Democratic Republic

Lebanon

Macao

Malaysia

Maldives

Mongolia

Myanmar

Nepal

Oman

Pakistan

Palestine, State of

Philippines

Qatar

Saudi Arabia

Singapore

Sri Lanka

Syrian Arab Republic

Taiwan

Tajikistan

Thailand

Timor-Leste

Turkey

Turkmenistan

United Arab Emirates

Uzbekistan

Vietnam

Yemen

Europe (51)

Albania

Andorra

Austria

Belarus

Belgium

Bosnia and Herzegovina

Bulgaria

Croatia

Czech Republic

Denmark

Estonia

Faroe Islands

Finland

France

Germany

Gibraltar

Greece

Guernsey

Holy See

Hungary

Iceland

Ireland

Isle of Man

Italy

Jersey

Latvia

Liechtenstein

Lithuania

Luxembourg

Macedonia (the former Yugoslav Republic of)

Malta

Moldova (Republic of)

Monaco

Montenegro

Netherlands

Norway

Poland

Portugal

Romania

Russian Federation

San Marino

Serbia

Slovakia

Slovenia

Spain

Svalbard and Jan Mayen

Sweden

Switzerland

Ukraine

United Kingdom

Åland Islands

North America (13)

Belize

Bermuda

Canada

Costa Rica

El Salvador

Greenland

Guatemala

Honduras

Mexico

Nicaragua

Panama

Saint Pierre and Miquelon

United States of America

Oceania (25)

American Samoa

Australia

Cook Islands

Fiji

French Polynesia

Guam

Kiribati

Marshall Islands

Micronesia (Federated States of)

Nauru

New Caledonia

New Zealand

Niue

Norfolk Island

Northern Mariana Islands

Palau

Papua New Guinea

Pitcairn

Samoa

Solomon Islands

Tokelau

Tonga

Tuvalu

Vanuatu

Wallis and Futuna

Other (9)

Antarctica

Bouvet Island

British Indian Ocean Territory

Christmas Island

Cocos (Keeling) Islands

French Southern Territories

Heard Island and McDonald Islands

South Georgia and the South Sandwich Islands

United States Minor Outlying Islands

South America (42)

Anguilla

Antigua and Barbuda

Argentina

Aruba

Bahamas

Barbados

Bolivia (Plurinational State of)

Bonaire, Sint Eustatius and Saba

Brazil

Cayman Islands

Chile

Colombia

Cuba

Curaçao

Dominica

Dominican Republic

Ecuador

Falkland Islands (Malvinas)

French Guiana

Grenada

Guadeloupe

Guyana

Haiti

Jamaica

Martinique

Montserrat

Paraguay

Peru

Puerto Rico

Saint Barthélemy

Saint Kitts and Nevis

Saint Lucia

Saint Martin (French part)

Saint Vincent and the Grenadines

Sint Maarten (Dutch part)

Suriname

Trinidad and Tobago

Turks and Caicos Islands

Uruguay

Venezuela (Bolivarian Republic of)

Virgin Islands (British)

Virgin Islands (U.S.)

4 weeks of historical data

50	+ Millions Records

Free sample available

10% discount if you buy via Datarade

License	Starts at
One-off purchase	Available
Monthly License	Available
Yearly License	Available
Usage-based	Available

Request detailed pricing

Self-reported by the provider

98%

filler rate

99%

match rate

Methods

Frequency

Format

Location Analytics

Business Intelligence (BI) Competitive Intelligence Point of Interest (POI) Mapping

POI Enrichment

Location Data Map Data Point of Interest (POI) Data Geographic Data Restaurant Location Data

320 Billion Pieces of Online Content Analyzed Each Day

80% match rate

249 countries covered

Connect with us for comprehensive Location Data, Point of Interest (POI) Data, and Map Data solutions! Discover invaluable insights with our Business Locatio...

50 + Millions Records

99% match rate

249 countries covered

This dataset serves as a valuable resource for businesses, researchers, and analysts seeking in-depth insights into the distribution, characteristics, and op...

420M MAU

95% Match rate

248 countries covered

We provide POI Data, which helps power geographical information system (GIS) tools and provides data-driven insights across a wide range of use cases, from m...

11M POI (Points of Interest)

240 countries covered

1 years of historical data

Global business listings information about points of interest (POI), such as brand, address, location, lat/log, open-hours, and NAICS category. This high-qua...

What is Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location?

What is Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location used for?

This product has 5 key use cases. Grepsr recommends using the data for Location Analytics, Business Intelligence (BI), Competitive Intelligence, Point of Interest (POI) Mapping, and POI Enrichment. Global businesses and organizations buy Location Data from Grepsr to fuel their analytics and enrichment.

Who can use Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location?

This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Location Data. Get in touch with Grepsr to see what their data can do for your business and find out which integrations they provide.

How far back does the data in Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location go?

This Tabular Data has 4 weeks of historical coverage. It can be delivered on a hourly, daily, weekly, monthly, quarterly, yearly, and on-demand basis.

Which countries does Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location cover?

This product includes data covering 249 countries like USA, China, Japan, Germany, and India. Grepsr is headquartered in United States of America.

How much does Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location cost?

Pricing information for Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location is available by getting in contact with Grepsr. Connect with Grepsr to get a quote and arrange custom pricing models based on your data requirements.

How can I get Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location?

Businesses can buy Location Data from Grepsr and get the data via S3 Bucket, SFTP, Email, and UI Export. Depending on your data requirements and subscription budget, Grepsr can deliver this product in .bin, .json, .xml, .csv, .xls, .sql, and .txt format.

What is the data quality of Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location?

Grepsr has reported that this product has the following quality and accuracy assurances: 99% match rate, 98% filler rate. You can compare and assess the data quality of Grepsr using Datarade’s data marketplace. Grepsr has received 2 reviews from clients. Grepsr appears on selected Datarade top lists ranking the best data providers, including Best 5 Amazon Data Scrapers for Product Research & Analysis and Who’s New on Datarade? September Edition.

What are similar products to Grepsr Comprehensive Dataset of Fast-food Chains’ Store (Starbucks, Mcdonalds, Subway, & more) Location?

This Tabular Data has 3 related products. These alternatives include The Data Appeal Location Data, Point of Interest (POI) Data and Map Data API, Dataset 200 Million + POI Data Mapped Coverage from 2019, Grepsr Comprehensive Dataset of Walgreens US Stores Across the United States, and Factori Point of Interest:POI Data Map Data Global Visitation Data. You can compare the best Location Data providers and products via Datarade’s data marketplace and get the right data for your use case.

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Grepsr | Comprehensive Dataset of Fast-food Chains' Store (Starbucks, Mcdonalds, Subway, & more) Location

Data Dictionary

Description

Geography

History

Volume

Pricing

Suitable Company Sizes

Quality

Delivery

Use Cases

Categories

Related Searches

Related Products

Frequently asked questions

Grepsr
Unleash the power of Data

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Grepsr | Comprehensive Dataset of Fast-food Chains' Store (Starbucks, Mcdonalds, Subway, & more) Location

Data Dictionary

Description

Geography

History

Volume

Pricing

Suitable Company Sizes

Quality

Delivery

Use Cases

Categories

Related Searches

Related Products

Frequently asked questions

Grepsr Unleash the power of Data

Trusted by

Grepsr
Unleash the power of Data