This is a list of 10,000 fast food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do With This Data

You can use this data to rank cities with the most and least fast food restaurants across the U.S. E.g.:

• Cities with the most and least McDonald's per capita
• Fast food restaurants per capita for all states
• Fast food restaurants with the most locations nationally
• Major cities with the most and least fast food restaurants per capita
• Small cities with the most fast food restaurants per capita
• States with the most and least fast food restaurants per capita
• The number of fast food restaurants per capita

Original Data

Acknowlegement

Foto von Haseeb Jamil auf Unsplash

About this dataset

Do you love the convenience of being able to drive through and pick up your food without having to wait? Well, you're not alone. According to a new study by Datafiniti, there are over 10,000 fast food restaurants across the United States.

That's a lot of restaurants! But what does that mean for the average person living in America? Well, it means that there are more than enough options for those who want to grab a quick bite on the go. And it also means that there are plenty of opportunities for those who want to open their own fast food restaurant.

So, if you're thinking about starting your own fast food business, or if you're just curious about where the most (and least) fast food options are in America, then this dataset is for you!

How to use the dataset

To find out where the most fast food restaurants are in the United States, you can use this dataset. The dataset includes the restaurant's name, address, city, state, and website. You can use this information to rank cities with the most and least fast food options

Research Ideas

1. Fast food delivery service that delivers from multiple restaurants

2. An app that allows users to find the healthiest fast food options near them

3. A website that ranks cities by their number of fast food restaurants per capita

Acknowledgements

The original source of the data is Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more

Columns

File: Datafiniti_Fast_Food_Restaurants.csv | Column name | Description | |:----------------|:---------------------------------------------------------------------| | dateAdded | The date the restaurant was added to the database. (Date) | | dateAdded | The date the restaurant was added to the database. (Date) | | dateUpdated | The date the restaurant was last updated in the database. (Date) | | dateUpdated | The date the restaurant was last updated in the database. (Date) | | address | The street address of the restaurant. (String) | | address | The street address of the restaurant. (String) | | categories | The category or categories the restaurant is classified as. (String) | | categories | The category or categories the restaurant is classified as. (String) | | city | The city the restaurant is located in. (String) | | city | The city the restaurant is located in. (String) | | country | The country the restaurant is located in. (String) | | country | The country the restaurant is located in. (String) | | keys | The unique identifier for the restaurant. (String) | | keys | The unique identifier for the restaurant. (String) | | latitude | The latitude coordinate of the restaurant. (Float) | | latitude | The latitude coordinate of the restaurant. (Float) | | longitude | The longitude coordinate of the restaurant. (Float) | | longitude | The longitude coordinate of the restaurant. (Float) | | name | The name of the restaurant. (String) | | name | The name of the restaurant. (String) | | postalCode | The postal code of the restaurant. (String) | | postalCode | The postal code of the restaurant. (String) | | province | The province or state the restaurant is located in. (String) | | province | The province or state the restaurant is located in. (String) | | sourceURLs | The source URL of the restaurant. (String) | | sourceURLs | The source URL of the restaurant. (String) | | websites | The website of the restaurant. (String) | | websites | The website of the restaurant. (String) |

File: FastFoodRestaurants.csv | Column name | Description | |:---------------|:-------------------------------------------------------------| | address | The street address of the restaurant. (String) | | address | The street address of the restaurant. (String) | | city | The city the restaurant is located in. (String) | | city | The city the restaurant is located in. (String) | | country | The country the restaurant i...

Xavvy fuel is the leading source for location data and market insights worldwide. We specialize in data quality and enrichment, providing high-quality POI data for restaurants and quick-service establishments in the United States.

Base data • Name/Brand • Adress • Geocoordinates • Opening Hours • Phone • ... ^

30+ Services • Delivery • Wifi • ChargePoints • …

10+ Payment options • Visa • MasterCard • Google Pay • individual Apps • ...

Our data offering is highly customizable and flexible in delivery – whether one-time or regular data delivery, push or pull services, and various data formats – we adapt to our customers' needs.

Brands included: • McDonalds • Burger King • Subway • KFC • Wendy's • ...

The total number of restaurants per region, market share distribution among competitors, or the ideal location for new branches – our restaurant data provides valuable insights into the food service market and serves as the perfect foundation for in-depth analyses and statistics. Our data helps businesses across various industries make informed decisions regarding market development, expansion, and competitive strategies. Additionally, our data contributes to the consistency and quality of existing datasets. A simple data mapping allows for accuracy verification and correction of erroneous entries.

Especially when displaying information about restaurants and fast-food chains on maps or in applications, high data quality is crucial for an optimal customer experience. Therefore, we continuously optimize our data processing procedures: • Regular quality controls • Geocoding systems to refine location data • Cleaning and standardization of datasets • Consideration of current developments and mergers • Continuous expansion and cross-checking of various data sources

Integrate the most comprehensive database of restaurant locations in the USA into your business. Explore our additional data offerings and gain valuable market insights directly from the experts!

The dataset is provided by one of the largest fast-food restaurant chains in the US. It includes (1) transaction information such as menu items that were purchased and quantities of each item; (2) ingredient lists for individual menu items; (3) metadata on restaurants, including location, and store type. The data observation window is from early March, 2015 to 06/15/2015 and includes transactional data from 2 stores in Berkeley, CA and 2 stores in New York, NY.

Dataset updated:

Feb 14, 2024

Dataset provided by:

data.world, Inc.

Authors:

Datafiniti

Area covered:

North Pacific Ocean, Pacific Ocean

Data Description:

This is a list of 10,000 fast food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more. Note that this is a sample of a large dataset. The full dataset is available through Datafiniti. You can use this data to rank cities with the most and least fast food restaurants across the U.S. E.g.:

Cities with the most and least McDonald's per capita
Fast food restaurants per capita for all states
Fast food restaurants with the most locations nationally
Major cities with the most and least fast food restaurants per capita
Small cities with the most fast food restaurants per capita
States with the most and least fast food restaurants per capita
The number of fast food restaurants per capita

Xtract.io's comprehensive McDonald's location data provides a detailed view of the global fast-food chain's network. Restaurant investors, market researchers, and business analysts can utilize this dataset to analyze market penetration, identify expansion opportunities, and develop a sophisticated understanding of McDonald's geographical strategy.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geospatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

Cooks working in fast food restaurants in the United States had a median hourly wage of 14.50 U.S. dollars as of May 2024. Meanwhile, 10 percent of fast food cooks earned less than 10.76 U.S. dollars per hour.

Xtract.io's Restaurant POI data delivers a comprehensive view of the brand's extensive QSR and fast food restaurant locations across the United States and Canada. Franchise investors, business analysts, and market researchers can utilize this QSR and fast food location data to understand Subway and other fast food market penetration, identify potential growth areas, and develop targeted strategic insights for quick service restaurant analysis.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive restaurant location intelligence landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including: -Retail -Restaurant chain locations -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ for Fast Food POI Data? At LocationsXYZ, we: -Deliver restaurant POI data with 95% accuracy -Refresh QSR location data every 30, 60, or 90 days to ensure the most recent information -Create on-demand fast food chain datasets tailored to your specific needs -Handcraft boundaries (geofences) for restaurant locations to enhance accuracy -Provide restaurant POI data and polygon data in multiple file formats

Unlock the Power of Restaurant Location Data With our point-of-interest data for food service establishments, you can: -Perform thorough market analyses for QSR expansion -Identify the best locations for new restaurant stores -Gain insights into consumer behavior and dining patterns -Achieve an edge with competitive intelligence in the fast food industry

LocationsXYZ has empowered businesses with geospatial insights and restaurant location intelligence, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge Subway restaurant POI data.

About this Dataset

Contact information for over 20,000 restaurants across the US. All restaurants from the NAICS code 72251: Restaurants and Other Eating Places. This includes all set down, fast casual, fast food, and ethnic restaurants. List includes name, address, phone number, website, contact email address, and a brief description. Data was collected from a combination of web scrapping and manual data entry. Similar lists cost over $1500 from lead generation and business data companies.

Category

Lead Generation

Keywords

restaurants,contact,mailing

Row Count

21210

Price

$499.00

This graph depicts the number of quick service restaurants (QSR) per capita, by city, in the United States as of March 2018. In 2018, Orlando, Florida is the city with the highest number of fast food restaurants per capita, accounting for **** QSRs per ** thousand residents.

Comprehensive dataset containing 10 verified Fast food restaurant businesses in American Samoa with complete contact information, ratings, reviews, and location data.

The United States is known as the home of fast food. But in this competitive market, what kind of fast food do consumers order the most? During a 2023 survey,****** emerged as the most frequently ordered fast food in the U.S., with ** percent of respondents indicating it as their top choice. Comparatively, only **** percent of participants reported TexMex as their most ordered type of fast food.

Abstract (en): This project is superceded by:Clarke, Philippa, Gomez-Lopez, Iris, Li, Mao, and Finlay, Jessica. National Neighborhood Data Archive (NaNDA): Eating and Drinking Places by Census Tract, United States, 2006-2015. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-12-05. https://doi.org/10.3886/E115404V1The objective of the National Neighborhood Data Archive (NaNDA) is to create measures of neighborhood context that impact clinical, social, and psychological health and healthy aging outcomes. This dataset contains measures of the number and per capita density of fast-food restaurants per United States census tract from 2006 through 2015. Fast-food restaurants are those classified as “limited-service restaurants” under the North American Industry Classification System (NAICS code 722513). According to the NAICS website, code 722513 “comprises establishments primarily engaged in providing food services (except snack and nonalcoholic beverage bars) where patrons generally order or select items and pay before eating.” Examples include pizza delivery shops, fast-food restaurants, and takeout sandwich shops. Fast-food and limited-service restaurants in all census tracts in the United States, excluding US island territories.Smallest Geographic Unit: census tract

About this Dataset

A complete list of all McDonald's US restaurant locations including Guam (GU) and Northern Mariana Islands (MP).

Category

Location Lists

Keywords

mcdonalds,mcdonald's in us,mcdonald's,us restaurants,fast food

Row Count

13465

Price

$99.00

In 2024, the month with the highest Quick Service Restaurant (QSR) sales in the United States was May, reaching around ** billion U.S. dollars. Meanwhile, the months of August and July ranked second and third, with QSR sales surpassing **** and **** billion U.S. dollars respectively.

This dataset provides restaurant inspections, violations, grades and adjudication information

Comprehensive dataset containing 29 verified Fast food restaurant businesses in U.S. Virgin Islands with complete contact information, ratings, reviews, and location data.

Vidore Benchmark 2 - ESG Restaurant Dataset

This dataset is part of the "Vidore Benchmark 2" collection, designed for evaluating visual retrieval applications. It focuses on the theme of ESG reports in the fast food industry.

  Dataset Summary

Each query is in french. This dataset provides a focused benchmark for visual retrieval tasks related to ESG reports of fast food companies. It includes a curated set of documents, queries, relevance judgments (qrels), and page… See the full description on the dataset page: https://huggingface.co/datasets/vidore/esg_reports_eng_v2.

Quick-Ratio Time Series for US Foods Holding Corp. US Foods Holding Corp., together with its subsidiaries, engages in marketing, sale, and distribution of fresh, frozen, and dry food and non-food products to foodservice customers in the United States. Its customers include independently owned single and multi-unit restaurants, regional concepts, national restaurant chains, hospitals, nursing homes, hotels and motels, country clubs, government and military organizations, colleges and universities, and retail locations. The company was formerly known as USF Holding Corp. and changed its name to US Foods Holding Corp. in February 2016. US Foods Holding Corp. was incorporated in 2007 and is headquartered in Rosemont, Illinois.

Context

This is a list of 10,000 fast-food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more.

Inspiration

You can use this data to rank cities with the most and least fast-food restaurants across the U.S. E.g.:

1. Cities with the most and least McDonald's per capita
2. Fast food restaurants per capita for all states
3. Fast food restaurants with the most locations nationally
4. Major cities with the most and least fast food restaurants per capita
5. Small cities with the most fast-food restaurants per capita
6. States with the most and least fast food restaurants per capita
7. The number of fast-food restaurants per capita

If you like the dataset, do upvote!