Title:

380K Restaurants - Mostly USA Based

Subtitle:

An extensive dataset of restaurants spanning the USA, with select international entries.

Description:

Dataset Overview:
This dataset provides a comprehensive compilation of 380,000 restaurants. While the majority are from the USA, there is also a representation from international locales. It's an invaluable resource for researchers, business analysts, and enthusiasts looking into the restaurant industry.

Dataset Contents:
The dataset encompasses various attributes for each restaurant:

• Title: The name of the restaurant.
• Link: A direct link to the restaurant's online presence or review site.
• Category: The type or genre of the restaurant (e.g., "Fast Food" or "Sushi").
• Rating: A numerical representation of customer reviews and feedback.
• Website: The official website of the restaurant.
• Phone: Contact number for reservations or inquiries.
• Address: Physical location or address of the restaurant.
• Images: Visual representations or photographs related to the restaurant.
• Categories: Further details on the restaurant's specialties.
• Geo_Coordinates: Geographical data points related to the restaurant's location.
• Time_Zone: The time zone in which the restaurant operates.
• Latitude & Longitude: Geographical coordinates for map integrations.

Potential Use Cases:
- Regional Analysis: Study restaurant distributions across different states or regions. - Rating Trends: Analyze the correlation between restaurant categories and their ratings. - Mapping Projects: Visualize restaurant locations to identify dense clusters or potential opportunities. - Time-Based Analysis: Investigate restaurant operations across different time zones.

Feedback and Collaboration:

Share insights, visualizations, or projects derived from this dataset with the wider community.

Dive into the "1500 North American Restaurants Comprehensive Dataset," featuring a rich selection of dining establishments from cozy local eateries to renowned culinary destinations. This meticulously curated collection spans a diverse range of cuisines, dining experiences, and geographical locations across North America. Each entry encapsulates key details such as cuisine types, service options, and customer ratings, making it an indispensable resource for food enthusiasts, researchers, and industry analysts seeking insights into the continent's vibrant restaurant scene.

Column Descriptions:

name: The name of the restaurant. city: The city where the restaurant is located. state: The state or province where the restaurant is located. zipcode: The postal code of the restaurant. country: The country of the restaurant (US or CA). cuisines: The types of cuisines offered by the restaurant. pickup_enabled: Indicates if pickup service is available (TRUE or FALSE). delivery_enabled: Indicates if delivery service is available (TRUE or FALSE). weighted_rating_value: The average rating of the restaurant on a scale from 0 to 5. aggregated_rating_count: The total number of ratings the restaurant has received.

Dive into this dataset for captivating data visualizations mapping North America's culinary landscape, NLP-driven sentiment analysis on cuisine types, and machine learning models predicting restaurant success. It's perfect for enthusiasts keen on uncovering trends through viz, extracting insights via NLP, or enhancing recommendation systems with predictive analytics. A playground for data science exploration!

Image attributes : Image By freepik

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...

The Temporary Program, is no longer accepting applications. *Visit Permanent Dining Out website for information: https://www.diningoutnyc.info/ The New York City Open Restaurant is an effort to implement a citywide multi-phase program to expand outdoor seating options for food establishments to promote open space, enhance social distancing, and help them rebound in these difficult economic times. For real time updates on restaurants registered in the program, please visit NYC Open Restaurants dashboard: https://bit.ly/2Z00kn8 ** Please note this Open Restaurant Applications dataset may contain multiple entries (e.g. restaurants submitting 2 or more applications). The Open Restaurants dashboard website containing real time update, noted above, will have fewer total records due to the removal of multiple applications and only list the newest entry.

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

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.

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.

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

State and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance. Data were collected to determine when restaurants in states and territories were subject to closing and reopening requirements through executive orders, administrative orders, resolutions, and proclamations for COVID-19. Data can be used to determine when restaurants in states and territories were subject to closing and reopening requirements through executive orders, administrative orders, resolutions, and proclamations for COVID-19. Data consists exclusively of state and territorial orders, many of which apply to specific counties within their respective state or territory; therefore, data is broken down to the county level. These data are derived from publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly close or reopen restaurants found by the CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, and the CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program from March 11, 2020 through May 31, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. Effective and expiration dates were coded using only the date provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

The Delta Food Outlets Study was an observational study designed to assess the nutritional environments of 5 towns located in the Lower Mississippi Delta region of Mississippi. It was an ancillary study to the Delta Healthy Sprouts Project and therefore included towns in which Delta Healthy Sprouts participants resided and that contained at least one convenience (corner) store, grocery store, or gas station. Data were collected via electronic surveys between March 2016 and September 2018 using the Nutrition Environment Measures Survey (NEMS) tools. Survey scores for the NEMS Corner Store, NEMS Grocery Store, and NEMS Restaurant were computed using modified scoring algorithms provided for these tools via SAS software programming. Because the towns were not randomly selected and the sample sizes are relatively small, the data may not be generalizable to all rural towns in the Lower Mississippi Delta region of Mississippi. Dataset one (NEMS-C) contains data collected with the NEMS Corner (convenience) Store tool. Dataset two (NEMS-G) contains data collected with the NEMS Grocery Store tool. Dataset three (NEMS-R) contains data collected with the NEMS Restaurant tool. Resources in this dataset:Resource Title: Delta Food Outlets Data Dictionary. File Name: DFO_DataDictionary_Public.csvResource Description: This file contains the data dictionary for all 3 datasets that are part of the Delta Food Outlets Study.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset One NEMS-C. File Name: NEMS-C Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for convenience stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Two NEMS-G. File Name: NEMS-G Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for grocery stores.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel Resource Title: Dataset Three NEMS-R. File Name: NEMS-R Data.csvResource Description: This file contains data collected with the Nutrition Environment Measures Survey (NEMS) tool for restaurants.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Snapshot img

US Fast Casual Restaurants Market Size 2025-2029

The US fast casual restaurants market size is valued to increase USD 84.5 billion, at a CAGR of 13.7% from 2024 to 2029. Demand for innovation and customization in food menus will drive the US fast casual restaurants market.

Major Market Trends & Insights

By Channel - Dine-in segment was valued at USD 48.90 billion in 2022
By Application - Franchised segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 148.40 billion
Market Future Opportunities: USD 84.50 billion
CAGR from 2024 to 2029 : 13.7%

Market Summary

The Fast Casual Restaurants Market in the US continues to expand, driven by consumer preferences for fresh, customizable meal options. According to recent data, the market is projected to reach a value of USD115.5 billion by 2026, growing at a steady pace. This growth is fueled by the demand for innovation and personalization in food menus, with fast casual restaurants offering a middle ground between the limited offerings of quick-service establishments and the higher prices and longer wait times of full-service restaurants. In response to this trend, fast casual chains have been increasingly focusing on digitalization, streamlining ordering processes and enhancing the customer experience through mobile apps and contactless payment options.
However, this market segment faces intense competition from quick-service restaurants, which have also been adopting similar strategies to cater to evolving consumer preferences. As a result, fast casual restaurants must continue to differentiate themselves through unique menu offerings, efficient operations, and exceptional customer service to maintain their market share. Despite these challenges, the future of the fast casual market in the US remains promising, with opportunities for growth in both urban and suburban areas and the potential to expand beyond traditional brick-and-mortar locations through delivery and catering services.

What will be the Size of the US Fast Casual Restaurants Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Fast Casual Restaurants in US Market Segmented and what are the key trends of market segmentation?

The fast casual restaurants in US industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Channel

  Dine-in
  Takeaway


Application

  Franchised
  Standalone


Food Type

  Burger/Sandwich
  Pizza/Pasta
  Asian
  Latin American
  Chicken
  Others


Target Audience

  Millennials
  Working Professionals
  Families


Distribution Channel Specificity

  Specialty Chains
  Online Platforms
  Retail Foodservice


Geography

  North America

    US

By Channel Insights

The dine-in segment is estimated to witness significant growth during the forecast period.

Fast casual restaurants in the US, a hybrid of fast food and casual dining, have been continuously evolving since their inception, offering better quality meals with less frozen or processed ingredients. Operational efficiency improvements, such as revenue management techniques and table management systems, have been key to their success. Cost control strategies, including digital menu boards, inventory management software, and marketing automation tools, help maintain profitability. Third-party delivery services and brand positioning strategies cater to the growing demand for convenience. Sustainability initiatives, like food waste reduction and customer loyalty programs, enhance the dining experience and foster long-term relationships.

Kitchen display systems, food safety management, energy efficiency measures, and wait time optimization ensure consistent quality and customer satisfaction. Sales forecasting models, employee retention strategies, labor scheduling software, and restaurant management systems facilitate efficient operations. Data analytics dashboards, social media marketing, online reputation management, and order fulfillment process enhance customer engagement. Peak hour management, online ordering platforms, guest feedback systems, and customer experience metrics provide valuable insights for continuous improvement. Supply chain optimization and employee training programs ensure consistency and quality in menu offerings. According to a recent report, fast casual restaurants account for over 5% of total US foodservice sales.

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The Dine-in segment was valued at USD 48.90 billion in 2019 and showed a gradual increase during the forecast period.

Request Free Sample

Market Dynamics

Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and

New York Times Best Restaurants in America 2024 Dataset

A dataset of top restaurants featured in the New York Times Best Restaurants in America 2024. The dataset includes details about various restaurants, their locations, cuisines, chefs, and other relevant information, curated from the official NYTimes recommendations and sourced from public sources, including official listings and restaurant websites. For more information, visit NYTimes Best Restaurants in America 2024.… See the full description on the dataset page: https://huggingface.co/datasets/RummageLabs/nytimes_best_restaurants_2024.

LA Restaurants Dataset

  Overview

This dataset is generated by llama3 based on Instagram posts of popular LA based food bloggers. The dataset consists of restaurant information extracted from Instagram captions of popular LA food pages. It includes details such as restaurant names, addresses, Instagram handles, famous dishes, location tags, and types of cuisine.

  Dataset Description

The dataset contains structured information extracted from Instagram captions.… See the full description on the dataset page: https://huggingface.co/datasets/shanto268/la_restaurants.

This map shows the average amount spent on meals away from home at restaurants or other per household in the U.S. in 2020 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Average annual spending on meals at restaurants per householdAverage annual spending on all food away from home per householdAverage annual spending on food by meal typeThis map shows Esri's 2020 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2020 U.S. Consumer Spending database provides the details about which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in more than 15 categories, including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Graph and download economic data for Retail Sales: Restaurants and Other Eating Places (MRTSSM7225USN) from Jan 1992 to Aug 2025 about restaurant, retail trade, sales, retail, and USA.

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

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

Tripadvisor, an American travel company, offers extensive information on hotels, restaurants, and tourist attractions worldwide. The Crawl Feeds team has extracted a detailed hotels dataset from Tripadvisor, which includes over 45,000 records of hotel and restaurant data for research and analysis purposes. This Tripadvisor dataset enables businesses, researchers, and analysts to gain valuable insights into traveler preferences, ratings, and reviews.

Currently, the dataset includes minimal datapoints focused on core hotel information. For those requiring bulk records or additional attributes—such as customer sentiment, review summaries, hotel amenities, room types, or location-specific insights—please explore our Travel & Tourism Data page or get in touch with us for customized data solutions tailored to your specific research and analysis needs.

Graph and download economic data for Sectoral Output Price Deflator for Accommodation and Food Services: Full-Service Restaurants (NAICS 722511) in the United States (IPUTN722511T051000000) from 1988 to 2024 about restaurant, accommodation, output, NAICS, IP, food, services, and USA.

Comprehensive dataset containing 1,362 verified Dim sum restaurant businesses in United States with complete contact information, ratings, reviews, and location data.