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

Our interactive restaurant location dashboard offers a comprehensive view of all restaurant locations across the United States, allowing you to explore, filter, and analyze this rich dataset. From popular restaurant chains to independent eateries and fine dining, this tool provides you with a complete overview.

This dashboard is a demo version of a much larger points-of-interest (POI) dataset, which is available to premium users. In the demo, you can access restaurant locations for free. By upgrading to a premium account, you can explore the full POI dataset, including all points-of-interest across the US, from bus stops and schools to shopping centers and bars.

With a Spotzi account, you can also analyze this data for free using the Spotzi platform. Spotzi allows you to run detailed analyses, visualize trends, and compare different points-of-interest, giving you valuable insights into the locations that matter most.

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

Comprehensive dataset containing 944 verified German restaurant businesses in United States with complete 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!

In 2018, food waste in the United States was a significant issue with substantial environmental and economic consequences. Here are some key statistics:

Overall Waste Volume and Percentage:

Approximately 103 million tons (206 billion pounds) of food waste were generated in the US in 2018, according to the EPA.

This amounted to between 30-40% of the entire US food supply going uneaten.

On a per-person basis, it was roughly one pound of food wasted per person per day.

Economic Impact:

The annual food waste in America had an approximate value of $161 billion to $218 billion.

The average American family of four reportedly threw out $1,500 in wasted food per year (based on 2010 price data, which would be higher in 2018).

The restaurant industry alone incurred an estimated $162 billion in costs related to wasted food.

Environmental Impact:

Food waste was the number one material in American landfills, accounting for 24.1% of all municipal solid waste (MSW).

When food rots in landfills, it produces methane, a potent greenhouse gas that is 28 times more powerful than CO2 at trapping heat. Food waste was responsible for an estimated 58% of landfill methane emissions to the atmosphere.

The production of wasted food in the US was equivalent to the greenhouse gas emissions of 37 million cars.

Wasted food also means wasted resources like land, water, and energy. Annually, food loss and waste took up an area of agricultural land the size of California and New York combined, and wasted enough energy to power 50 million US homes for a year.

Approximately 21% of agricultural water resources and 19% of US croplands were wasted for food that was ultimately thrown away.

Sources of Food Waste:

Food waste occurs across the entire supply chain, with significant contributions from:

Households: An estimated 43% of food waste came from homes.

Grocery stores, restaurants, and food service companies: Accounted for about 40% of food waste.

Farms: Responsible for around 16% of food loss.

Manufacturers: Contributed about 2% of food waste.

Breakdown by Material (within MSW):

Food waste comprised the fourth largest material category in total MSW generation, estimated at 63.1 million tons or 21.6% in 2018.

These statistics highlight the significant scale of food waste in the US in 2018 and its wide-ranging negative impacts on the economy and the environment

Dataset's Link: https://catalog.data.gov/dataset/u-s-food-waste-flows-between-sectors-2018-v1-3-2

Food waste flows between waste-generating sectors and waste management routes are captured by these Flow-By-Sector (FBS) databases. Typically, the sectors use codes from the 2012 North American Industry Classification System (NAICS). Method 1 (m1 dataset file), the first dataset, assigns sectors to food waste creation and disposal statistics from the USEPA Wasted Food Report. The National Commercial Non-Hazardous Waste (CNHW) FBS dataset's discarded food data is attributed to sectors using the second approach, method 2 (m2 dataset file).

Column's Information

The CSV file "Food_Waste_national_2018_m2_v1.3.2_9b1bb41.csv" contains the following columns with their likely meanings:

Flowable: The type of material being tracked, in this case, "Food Waste".

Class: A classification for the "Flowable" material, here "Other".

SectorProducedBy: A numerical code indicating the sector that produced the food waste.

SectorConsumedBy: A numerical code indicating the sector that consumed or received the food waste.

SectorSourceName: The source of the sector classification, which is "NAICS_2012_Code" (North American Industry Classification System 2012 Code).

Context: This column appears to be empty in the provided data.

Location: This column seems to contain a location code, e.g., "=""00000""".

LocationSystem: The system used for location identification, which is "FIPS" (Federal Information Processing Standards).

FlowAmount: The quantity of food waste.

Unit: The unit of measurement for "FlowAmount", which is "kg" (kilograms).

FlowType: The type of flow, which is "WASTE_FLOW".

Year: The year the data pertains to, in this case, "2018".

MeasureofSpread: This column appears to be empty in the provided data.

Spread: A value related to the spread of the data, here "0.0".

DistributionType: This column appears to be empty in the provided data.

Min: Minimum value, here "0.0".

Max: Maximum value, here "0.0".

DataReliability: Data reliability value, here "0.0".

TemporalCorrelation: Temporal correlation value, here "0.0".

GeographicalCorrelation: Geographical correlation value, here "0.0".

TechnologicalCorrelation: Technological correlation value, here "0.0".

DataCollection: Data collection method or source, here "CalRecycle_WasteCharacterization".

**MetaSources...

This data section provides information about publicly available national surveys that include questions from the U.S. Food Security Survey Module. Information on each survey and directions for accessing data files are available in the documentation.

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.

Comprehensive dataset containing 5,181 verified New American restaurant businesses in United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 12,653 verified Indian restaurant businesses in United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 5,759 verified Traditional American restaurant businesses in United States with complete contact information, ratings, reviews, and location data.

MyPyramid Food Data provides information on the total calories; calories from solid fats, added sugars, and alcohol (extras); MyPyramid food group and subgroup amounts; and saturated fat content of over 1,000 commonly eaten foods with corresponding commonly used portion amounts. This information is key to help consumers meet the recommendations of the Dietary Guidelines for Americans and manage their weight by understanding how many calories are consumed from "extras." CNPP has created an interactive tool from this data set available on the web at MyFood-a-pedia.gov. A mobile version is coming soon to provide consumers with assistance on-the-go.

The Current Population Survey Food Security Supplement (CPS-FSS) is the source of national and State-level statistics on food insecurity used in USDA's annual reports on household food security. The CPS is a monthly labor force survey of about 50,000 households conducted by the Census Bureau for the Bureau of Labor Statistics. Once each year, after answering the labor force questions, the same households are asked a series of questions (the Food Security Supplement) about food security, food expenditures, and use of food and nutrition assistance programs. Food security data have been collected by the CPS-FSS each year since 1995. Four data sets that complement those available from the Census Bureau are available for download on the ERS website. These are available as ASCII uncompressed or zipped files. The purpose and appropriate use of these additional data files are described below: 1) CPS 1995 Revised Food Security Status data--This file provides household food security scores and food security status categories that are consistent with procedures and variable naming conventions introduced in 1996. This includes the "common screen" variables to facilitate comparisons of prevalence rates across years. This file must be matched to the 1995 CPS Food Security Supplement public-use data file. 2) CPS 1998 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1998 data file. 3) CPS 1999 Children's and 30-day Food Security data--Subsequent to the release of the April 1999 CPS-FSS public-use data file, USDA developed two additional food security scales to describe aspects of food security conditions in interviewed households not captured by the 12-month household food security scale. This file provides three food security variables (categorical, raw score, and scale score) for each of these scales along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS April 1999 data file. 4) CPS 2000 30-day Food Security data--Subsequent to the release of the September 2000 CPS-FSS public-use data file, USDA developed a revised 30-day CPS Food Security Scale. This file provides three food security variables (categorical, raw score, and scale score) for the 30-day scale along with household identification variables to allow the user to match this supplementary data file to the CPS-FSS September 2000 data file. Food security is measured at the household level in three categories: food secure, low food security and very low food security. Each category is measured by a total count and as a percent of the total population. Categories and measurements are broken down further based on the following demographic characteristics: household composition, race/ethnicity, metro/nonmetro area of residence, and geographic region. The food security scale includes questions about households and their ability to purchase enough food and balanced meals, questions about adult meals and their size, frequency skipped, weight lost, days gone without eating, questions about children meals, including diversity, balanced meals, size of meals, skipped meals and hunger. Questions are also asked about the use of public assistance and supplemental food assistance. The food security scale is 18 items that measure insecurity. A score of 0-2 means a house is food secure, from 3-7 indicates low food security, and 8-18 means very low food security. The scale and the data also report the frequency with which each item is experienced. Data are available as .dat files which may be processed in statistical software or through the United State Census Bureau's DataFerret http://dataferrett.census.gov/. Data from 2010 onwards is available below and online. Data from 1995-2009 must be accessed through DataFerrett. DataFerrett is a data analysis and extraction tool to customize federal, state, and local data to suit your requirements. Through DataFerrett, the user can develop an unlimited array of customized spreadsheets that are as versatile and complex as your usage demands then turn those spreadsheets into graphs and maps without any additional software. Resources in this dataset:Resource Title: December 2014 Food Security CPS Supplement. File Name: dec14pub.zipResource Title: December 2013 Food Security CPS Supplement. File Name: dec13pub.zipResource Title: December 2012 Food Security CPS Supplement. File Name: dec12pub.zipResource Title: December 2011 Food Security CPS Supplement. File Name: dec11pub.zipResource Title: December 2010 Food Security CPS Supplement. File Name: dec10pub.zip

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.

Comprehensive dataset containing 5,314 verified Soul food restaurant businesses in United States with complete contact information, ratings, reviews, and location data.

Dutch Bros Locations (Geolocations)

All of Dutch Bros restaurant locations (Latitude and Longitude)

By M Longoria [source]

About this dataset

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

If you're looking for a quick and easy way to find Dutch Bros locations in the United States, this dataset is exactly what you need. Just enter in a location (city, state, or zip code) and the results will show you all of the Dutch Bros locations in that area. You can also use the longitude and latitude coordinates to plot the locations on a map

Research Ideas

• Addresses of Dutch Bros coffee locations in the United States You can use this dataset to find the addresses of Dutch Bros coffee locations in the United States. This can be useful if you are looking for a specific location or want to find out how many locations there are in a particular area.

• Longitude and latitude coordinates of Dutch Bros coffee locations in the United States You can use this dataset to find the longitude and latitude coordinates of Dutch Bros coffee locations in the United States. This can be useful if you want to plot the locations on a map or calculate distances between them.

• City names of Dutch Bros coffee locations in the United States You can use this dataset to find the city names of Dutch Bros coffee locations in the United States. This can be useful if you are looking for a specific location or want to find out how many locations there are in a particular city

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: DutchBrosLocations20211025.csv | Column name | Description | |:----------------|:----------------------------------------------------------------------------| | Address | The address of the Dutch Bros location. (String) | | State | The state in which the Dutch Bros location is located. (String) | | City | The city in which the Dutch Bros location is located. (String) | | Postal Code | The postal code of the Dutch Bros location. (String) | | Location | The longitude and latitude coordinates of the Dutch Bros location. (String) | | Latitude | The latitude coordinate of the Dutch Bros location. (Float) | | Longitude | The longitude coordinate of the Dutch Bros location. (Float) |

File: CityLatLong.csv | Column name | Description | |:--------------|:---------------------------------------------------------------------------| | State | The state in which the Dutch Bros location is located. (String) | | Latitude | The latitude coordinate of the Dutch Bros location. (Float) | | Longitude | The longitude coordinate of the Dutch Bros location. (Float) | | City Name | The name of the city in which the Dutch Bros location is located. (String) | | Country | The country in which the Dutch Bros location is located. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit M Longoria.

Food Recalls in the United States

Recalls of Hazardous Foods in the United States

About this dataset

To help ensure the safety of the food supply, the United States Department of Agriculture Food Safety and Inspection Service (FSIS) and the Food and Drug Administration (FDA) issue recalls when they become aware of products that may be contaminated.

This dataset catalogs all food recalls issued by the FSIS and FDA. It includes information on what product was recalled, what specific contamination was found, where in the United States the product was sold, when the recall was announced, and how many people may have been affected.

If you are concerned about the safety of a particular food product, this dataset is a valuable resource for investigating whether it has been recalled in the past

How to use the dataset

This dataset catalogs food recalls in the United States from the USDA Food Safety and Inspection Service (FSIS) and the FDA. It includes information on product identity, manufacturer, country of origin, sellers/distributors, recall type, date of recall announcement, and foodborne illness if reported.

This dataset can be used to track trends in food recalls over time or to examine specific cases in detail. It may also be useful for research into the causes of foodborne illness and how to prevent it

Research Ideas

• Predicting the occurrence of food recalls
• Assessing the effectiveness of food safety regulation
• Identifying at-risk foods or production processes

Columns:

• Recalls_ID: A unique ID number assigned to each recall
• Date_Published: The date when the recall was published
• Recalls_Product: The product that is being recalled
• Description_of_Product: A description of the product being recalled/investigated

• Synopsis: A brief summary of why the product is being recalled etc.

  Inspiration

  What are some factors that lead to food products getting recalled?

  How has the number of recalls changed over time?

License

See the dataset description for more information.

Columns

Comprehensive dataset containing 9,045 verified Vietnamese restaurant businesses in United States with complete contact information, ratings, reviews, and location data.