Number of Businesses statistics on the Fast Food Restaurants industry in the US

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.

The Allegheny County Health Department has generated this list of fast food restaurants by exporting all chain restaurants without an alcohol permit from the County’s Fee and Permit System. A chain restaurant defined by the County is any restaurant that has more than one location in the County. Chain restaurants capture both local and national chains (including locally owned national chains) so long as there is one or more establishments in operation within the County.

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

Number of Businesses statistics on the Global Fast Food Restaurants industry in Global

The Fast Food Market Report is Segmented by Product Type (Burger, Sandwich, Meat Based Cuisines, and More), Restaurant Type (Quick Service Restaurants (QSR), Fast-Casual Restaurants, and More), Ordering Channel (Dine-In, Drive-Thru/Take-Away, and More), Outlet Type (Independent Outlet, and Chain Outlet), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

As of November 2025, there were nearly ** thousand fast-food establishments in Mexico, out of which more than ***** were located in the homonymous state. Veracruz was the Mexican state with the second largest number of outlets preparing this type of food, with around *****.

Between October and November of 2020, fast food consumers primarily from the United States were asked about the reasons they ate fast food. Approximately **** percent of those surveyed stated convenience as the leading reason they ate fast food. Meanwhile, the second highest percentage of respondents, **** percent, said it was because it tasted good.

1) Data Introduction • The Fast Food Classification Dataset is designed for image classification of various fast food items, consisting of a total of 10 classes: Burger, Donut, Hot Dog, Pizza, Sandwich, Baked Potato, Crispy Chicken, Fries, Taco, and Taquito.

2) Data Utilization (1) Characteristics of the Fast Food Classification Dataset: • The dataset includes images captured under various lighting conditions, backgrounds, and angles, making it suitable for evaluating the generalization performance of classification models in real-world scenarios.

(2) Applications of the Fast Food Classification Dataset: • Training fast food image classification models: Can be used to develop deep learning–based image classifiers that accurately distinguish between various fast food items. • Building automated food recognition systems: Applicable to real-time food identification and classification in self-order kiosks, smart POS systems, and food recognition apps.

The U.S. Fast Food & Quick Service Restaurant (QSR) Market Size Was Worth USD 405 Billion in 2023 and Is Expected To Reach USD 663 Billion by 2032, CAGR of 10.3%.

Comprehensive list containing 52 verified Fast food restaurant businesses in MC with latest contact information, ratings, reviews, and location data.

{"units": "% change"}

This statistic shows the results of a survey conducted by Cint on the average number of times fast food from quick service restaurants was consumed per week in the United States between 2016 and 2018. In 2018, ***** percent of respondents in the United States stated they eat fast food less than once per week.

Dataset

This dataset was created by Chris Anderson

Contents

The global fast-food market is booming, projected to reach $1.2 trillion by 2033. Explore key trends, market size analysis, leading players (McDonald's, Starbucks, Subway), and regional growth projections in this in-depth report. Discover the forces driving expansion and the challenges faced by industry giants.

China Chain: Fast Food: Number of Store data was reported at 29,221.000 Unit in 2023. This records an increase from the previous number of 25,205.000 Unit for 2022. China Chain: Fast Food: Number of Store data is updated yearly, averaging 11,892.000 Unit from Dec 2003 (Median) to 2023, with 21 observations. The data reached an all-time high of 29,221.000 Unit in 2023 and a record low of 1,966.000 Unit in 2003. China Chain: Fast Food: Number of Store data remains active status in CEIC and is reported by Ministry of Commerce, China General Chamber of Commerce. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.CCAC: Fast Food.

The global fast food market size was estimated at USD 836.20 Billion in 2025 and will grow at 4.90 & CAGR to USD 1349.16 Billion by 2035.

This service provides statistics relevant to food availability in restaurants. These data are organized into the two following groups:

• Fast-food Restaurants

• Full-service Restaurants

Comprehensive list containing 19 verified Fast food restaurant businesses in FO with latest contact information, ratings, reviews, and location data.

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!