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

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...

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.

About this Data

This is a list of 19,439 restaurants and similar businesses with menu items containing "burrito" or "taco" in their names provided by Datafiniti's Business Database.

The dataset includes the category, cuisine, restaurant information, and more for a menu item. Each row corresponds to a single menu item from the restaurant, and the entirety of each restaurant's menu is not listed. Only burrito or taco items are listed.

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 discover which parts of the country offer the most for Mexican food aficionados. E.g.:

What is the ratio of burritos and tacos on restaurant menus from each city? What is the ratio of burritos and tacos on restaurant menus from cities with the most restaurants per capita (10,000 residents)? What is the ratio of cities with the most authentic Mexican restaurants per capita (10,000 residents)? Which cities have the most authentic Mexican restaurants? Which cities have the most Mexican restaurants? Which Mexican restaurants have the most locations nationally?

Data Schema

A full schema for the data is available in support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

Get this data and more by creating a free Datafiniti account or requesting a demo.

This dataset was created by Datafiniti and contains around 77,000 samples along with City, Categories Restaureant, Menus_amount Max, technical information, and other features such as:

• Cuisines
• Menu_description
• priceRangeMin and priceRangeMax
• and more.

How to use this dataset

• Exploratory Data Analysis
• Study the influence of price on menus_name
• Study the words that are most often used to describe burritos and tacos dishes

Acknowledgements

If you use this dataset in your research, please credit Datafiniti

Start A New Notebook!

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.

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

This dataset contains Restaurant Availability and Expenditures metrics displayed in the U.S. Department of Agriculture (USDA) Food Environment Atlas website, including fast-food restaurant availability, full-service restaurant availability, and expenditures at fast-food and full-service restaurants.

Data was last updated on the USDA website in September 2020.

Any data elements with numerical values reflect figures at the locality-level unless otherwise specified with an asterisk (*). See column descriptions for details. For more information on all metrics in this dataset, see the Food Environment Atlas Restaurant Availability and Expenditures documentation.

MenuStat.org is an interactive online database of nutrition and menu information from top national restaurant chains. Each item is coded a mutually exclusive food category and descriptive information is coded into binary variables (e.g. on the kids menu). A new year of data is added annually and individual menu items are linked over time with a unique ID, allowing for trend analyses. On MenuStat.org, menu items that can be ordered together as part of a customizable or combination meal are linked together by a unique ID (Customizable_Build_ID) and all data can be exported for further analyses. NYC DOHMH developed and maintains MenuStat, but it is hosted externally

The Ubereats Restaurant Dataset: Over 100,000 US Restaurants

Delicious And Affordable Eats

By Jeff [source]

About this dataset

The Ubereats Restaurant Dataset contains information on over 100,000 restaurants in the United States, including location, contact information, price range, review rating, and more. This dataset is a great resource for anyone looking for information on the best and most delicious restaurants in the US

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How to use the dataset

The Ubereats Restaurant Dataset contains information on over 100,000 restaurants in the United States, including location, contact information, price range, review rating, and more. This information can be used to find the best and most delicious restaurants in the US.

To find the best restaurants in the US, filter the dataset by is_open = 1 and review_rating >= 4.5. This will give you a list of open restaurants that have a high review rating. You can then use the other columns in the dataset to find more information about each restaurant, such as location, price range, and delivery time

Research Ideas

• Location-based restaurant recommendations - using the latitude and longitude of a user's current location, restaurants in the vicinity can be recommended
• Price range-based restaurant recommendations - using the price_bucket column, restaurants within a certain price range can be recommended
• Review-based restaurant recommendations - using the review_rating and review_count columns, restaurants with high ratings and/or a large number of reviews can be recommended

Acknowledgements

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

Data Source

License

See the dataset description for more information.

Columns

File: Ubereat_US_Merchant.csv | Column name | Description | |:-------------------|:------------------------------------------------------------------------| | city | The city in which the restaurant is located. (String) | | state | The state in which the restaurant is located. (String) | | zipcode | The zip code in which the restaurant is located. (String) | | address | The address of the restaurant. (String) | | loc_name | The name of the location at which the restaurant is located. (String) | | loc_number | The number of the location at which the restaurant is located. (String) | | url | The URL of the restaurant. (String) | | promotion | Any promotions that the restaurant is currently running. (String) | | latitude | The latitude of the restaurant. (Float) | | longitude | The longitude of the restaurant. (Float) | | is_open | Whether or not the restaurant is currently open. (Integer) | | closed_message | The message displayed when the restaurant is closed. (String) | | delivery_fee | The delivery fee charged by the restaurant. (Float) | | delivery_time | The estimated delivery time for the restaurant. (Float) | | review_count | The number of reviews for the restaurant. (Integer) | | review_rating | The average review rating for the restaurant. (Float) | | price_bucket | The price bucket in which the restaurant falls. (String) | | img1 | The URL of the first image for the restaurant. (String) | | img2 | The URL of the second image for the restaurant. (String) | | img3 | The URL of the third image for the restaurant. (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 Jeff.

Eateries in in New York City Department of Parks & Recreation properties including snack bars, food carts, mobile food trucks, and restaurants.

This data includes the name and location of active food service establishments and the violations that were found at the time of the inspection. Active food service establishments include only establishments that are currently operating. This dataset excludes inspections conducted in New York City (https://data.cityofnewyork.us/Health/Restaurant-Inspection-Results/4vkw-7nck), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Update frequencies and availability of historical inspection data may vary from county to county. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health. For more information, visit http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm or go to the “About” tab.

The dataset contains every sustained or not yet adjudicated violation citation from every full or special program inspection conducted up to three years prior to the most recent inspection for restaurants and college cafeterias in an active status on the RECORD DATE (date of the data pull). When an inspection results in more than one violation, values for associated fields are repeated for each additional violation record. Establishments are uniquely identified by their CAMIS (record ID) number. Keep in mind that thousands of restaurants start business and go out of business every year; only restaurants in an active status are included in the dataset. Records are also included for each restaurant that has applied for a permit but has not yet been inspected and for inspections resulting in no violations. Establishments with inspection date of 1/1/1900 are new establishments that have not yet received an inspection. Restaurants that received no violations are represented by a single row and coded as having no violations using the ACTION field. Because this dataset is compiled from several large administrative data systems, it contains some illogical values that could be a result of data entry or transfer errors. Data may also be missing. This dataset and the information on the Health Department’s Restaurant Grading website come from the same data source. The Health Department’s Restaurant Grading website is here: http://www1.nyc.gov/site/doh/services/restaurant-grades.page See the data dictionary file in the Attachments section of the OpenData website for a summary of data fields and allowable values.

This dataset is the NYC restaurant inspections with an INSPECTION DATE taking place in 2019.

Original text: The dataset contains every sustained or not yet adjudicated violation citation from every full or special program inspection conducted up to three years prior to the most recent inspection for restaurants and college cafeterias in an active status on the RECORD DATE (date of the data pull). When an inspection results in more than one violation, values for associated fields are repeated for each additional violation record. Establishments are uniquely identified by their CAMIS (record ID) number. Keep in mind that thousands of restaurants start business and go out of business every year; only restaurants in an active status are included in the dataset. Records are also included for each restaurant that has applied for a permit but has not yet been inspected and for inspections resulting in no violations. Establishments with inspection date of 1/1/1900 are new establishments that have not yet received an inspection. Restaurants that received no violations are represented by a single row and coded as having no violations using the ACTION field. Because this dataset is compiled from several large administrative data systems, it contains some illogical values that could be a result of data entry or transfer errors. Data may also be missing. This dataset and the information on the Health Department’s Restaurant Grading website come from the same data source. The Health Department’s Restaurant Grading website is here: http://www1.nyc.gov/site/doh/services/restaurant-grades.page See the data dictionary file in the Attachments section of the OpenData website for a summary of data fields and allowable values.

COVID-19 Relief Restaurant Revitalization Fund Recipients

The restaurants that received revitalization fundings during the peak of the Covid-19 pandemic

By State of New York [source]

About this dataset

The American Rescue Plan Act established the Restaurant Revitalization Fund (RRF) to provide funding to help restaurants and other eligible businesses keep their doors open. This program provided restaurants with funding equal to their pandemic-related revenue loss up to $10 million per business and no more than $5 million per physical location. Recipients are not required to repay the funding as long as funds are used for eligible uses no later than March 11, 2023.

This dataset details New York State recipients of RRF funds, including the loan number, approval date, business name, address, city, state, zip code, grant amount, franchise name (if applicable), rural/urban indicator, HUBZone indicator, Congressional District (CD), and indicators of whether the grant was used for outdoor seating, a covered supplier expense, debt relief or refinancing, food expenses related to on-site consumption or delivery/catering services ,indoor maintenance expenses such as rent or mortgage payments ,or operations expenditures such as employee salaries

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How to use the dataset

Research Ideas

• Identify restaurant trends during the COVID-19 pandemic.
• Identify areas of the country that have been most affected by the pandemic.
• Which businesses are most likely to receive funding from the government

Acknowledgements

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

Data Source

License

See the dataset description for more information.

Columns

File: us-sba-covid-19-relief-to-nys-business-restaurant-revitalization-fund-1.csv | Column name | Description | |:-------------------------------------|:-----------------------------------------------------------------------------------| | LoanNumber | The loan number for the recipient. (Integer) | | ApprovalDate_Year | The year the loan was approved. (Integer) | | ApprovalDate_Month | The month the loan was approved. (Integer) | | ApprovalDate_Day | The day the loan was approved. (Integer) | | BusinessName | The name of the business that received the loan. (String) | | BusinessAddress | The address of the business that received the loan. (String) | | BusinessCity | The city of the business that received the loan. (String) | | BusinessState | The state of the business that received the loan. (String) | | BusinessZip | The zip code of the business that received the loan. (String) | | GrantAmount | The amount of the grant received by the business. (Float) | | FranchiseName | The name of the franchise, if applicable. (String) | | RuralUrbanIndicator | An indicator of whether the business is located in a rural or urban area. (String) | | HubzoneIndicator | An indicator of whether the business is located in a HUBZone. (String) | | CD | The congressional district in which the business is located. (String) | | grant_purp_cons_outdoor_seating | An indicator of whether the grant was used for outdoor seating. (String) | | grant_purpose_covered_supplier | An indicator of whether the grant was used for a covered supplier. (String) | | grant_purpose_debt | An indicator of whether the grant was used for debt relief. (String) | | grant_purpose_food | An indicator of whether the grant was used for food purposes. (String) | | grant_purpose_maintenance_indoor | An indicator of whether the grant was used for indoor maintenance. (String) | | grant_purpose_operations | An indicator of whether the grant was used for operations. (String) |

...

This data includes the name and location of active food service establishments and the violations that were found at the time of the inspection. Although violation details are collected on inspection reports (i.e., the actual food item, quantity and temperature of food found out of temperature control) as well as corrective actions for critical violations, this data set is limited to the violation number and the corresponding general violation description. Requests for more detailed information or actual copies of inspection reports should be directed to the local health department or State District Office which conducted the inspections in question. Active food service establishments include only establishments that are currently operating. This dataset excludes inspections conducted in New York City (https://data.cityofnewyork.us/Health/Restaurant-Inspection-Results/4vkw-7nck), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Update frequencies and availability of historical inspection data may vary from county to county. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health. Historical inspection data through 2005 is also available. Inactive (closed) establishments can be found at: https://health.data.ny.gov/Health/Food-Service-Establishment-Inspections-Beginning-2/aaxz-j6pj. For more information, visit http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm. The "About" tab contains additional details concerning this dataset.

SMS Marketing for Restaurants Market Outlook

According to our latest research, the global SMS Marketing for Restaurants market size reached USD 1.42 billion in 2024, with a robust year-on-year growth supported by increasing digital adoption in the food service industry. The market is expected to expand at a CAGR of 14.7% from 2025 to 2033, reaching a projected value of USD 4.57 billion by 2033. This impressive growth is driven by the rising need for direct, personalized communication between restaurants and their customers, as well as the growing emphasis on customer engagement and retention strategies. As per our latest research, technological advancements and evolving consumer preferences are further fueling the demand for SMS marketing solutions in the restaurant sector worldwide.

The proliferation of smartphone usage and the widespread adoption of mobile internet have significantly transformed the way restaurants interact with their customers. Today’s consumers expect real-time communication, personalized offers, and instant updates, making SMS marketing an indispensable tool for restaurants aiming to enhance customer engagement. The high open rate of SMS messages, often exceeding 90%, ensures that promotional campaigns, reservation reminders, and loyalty program notifications are promptly seen and acted upon. Furthermore, the simplicity and efficiency of SMS as a communication channel allow restaurants to reach a broad demographic, including customers who may not actively use social media or email. This broad accessibility, coupled with the low cost of SMS campaigns compared to traditional advertising, is a key factor contributing to the market’s rapid expansion.

Another major growth driver for the SMS Marketing for Restaurants market is the increasing competition within the food service industry. With new restaurants and food delivery services entering the market at an unprecedented pace, establishments are under constant pressure to differentiate themselves and foster customer loyalty. SMS marketing provides a direct, measurable, and highly effective means of engaging customers, soliciting feedback, and driving repeat business. Restaurants are leveraging SMS for a variety of applications, from sending promotional offers and last-minute deals to conducting quick surveys and gathering valuable customer insights. The ability to segment audiences and personalize messages further enhances the effectiveness of SMS campaigns, leading to higher conversion rates and improved customer satisfaction.

Regulatory developments and data privacy concerns are also shaping the growth trajectory of the SMS Marketing for Restaurants market. Regulations such as the General Data Protection Regulation (GDPR) in Europe and the Telephone Consumer Protection Act (TCPA) in the United States necessitate that restaurants obtain explicit consent from customers before sending marketing messages. While these regulations pose challenges, they also ensure that SMS marketing remains a trusted and reliable communication channel. Restaurants that prioritize compliance and transparency are better positioned to build lasting relationships with their customers, thereby maximizing the return on their SMS marketing investments. As the regulatory landscape continues to evolve, market players are increasingly focusing on integrating advanced consent management and data protection features into their SMS marketing platforms.

From a regional perspective, North America currently dominates the SMS Marketing for Restaurants market, accounting for the largest share in 2024. This can be attributed to the high penetration of smartphones, advanced digital infrastructure, and the early adoption of marketing automation solutions by restaurants in the United States and Canada. Europe follows closely, driven by stringent data privacy regulations and a strong focus on customer experience. The Asia Pacific region, however, is expected to witness the fastest growth during the forecast period, supported by a burgeoning restaurant industry, rapid urbanization, and increasing mobile connectivity. Latin America and the Middle East & Africa are also emerging as promising markets, as restaurants in these regions increasingly recognize the value of SMS marketing in reaching a diverse and growing customer base.

Component Analysis

The SMS Marketing for Restaurants market is segmented by component into Software and Services. The software segm

Lawry's The Prime Rib Locations

All locations of Lawry's The Prime Rib Restaurants

By Tony Paul [source]

About this dataset

This is a complete list of all Lawry's restaurant locations, along with their geographic coordinates, Street addresses, City, State, ZIP code and more. Whether you're looking for your next prime rib dinner destination or just want to know where the nearest Lawry's is, this dataset has you covered

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Research Ideas

• Create a heatmap of Lawry's restaurant locations in the US
• Determin the best location for a new Lawry's restaurant
• Analyze customer reviews to determine which locations are most popular

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: lawrysonline_2018_11_06.csv | Column name | Description | |:-------------------|:---------------------------------------------------------| | name | The name of the restaurant. (String) | | url | The URL of the restaurant's website. (String) | | street_address | The street address of the restaurant. (String) | | city | The city in which the restaurant is located. (String) | | state | The state in which the restaurant is located. (String) | | zip_code | The ZIP code of the restaurant. (String) | | country | The country in which the restaurant is located. (String) | | phone_number_1 | The first phone number for the restaurant. (String) | | phone_number_2 | The second phone number for the restaurant. (String) | | fax_1 | The first fax number for the restaurant. (String) | | fax_2 | The second fax number for the restaurant. (String) | | email_1 | The first email address for the restaurant. (String) | | email_2 | The second email address for the restaurant. (String) | | open_hours | The hours during which the restaurant is open. (String) | | latitude | The latitude coordinate for the restaurant. (Float) | | longitude | The longitude coordinate for the restaurant. (Float) | | facebook | The URL of the restaurant's Facebook page. (String) | | twitter | The URL of the restaurant's Twitter page. (String) | | instagram | The URL of the restaurant's Instagram page. (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 Tony Paul.

Whataburger Restaurant Locations in the US

All of whataburger restaurant locations in the US

By Tony Paul [source]

About this dataset

This is a complete list of all Whataburger restaurant locations, along with their geographic coordinates, street addresses, city, state and ZIP code. This dataset also includes phone numbers, fax numbers and email addresses for each restaurant

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How to use the dataset

This dataset can be used to find the location, phone number, and email address of Whataburger restaurants in the US.

The 'name' column indicates the name of the Whataburger restaurant. The 'url' column indicates the URL of the Whataburger restaurant's website. The 'street_address' column indicates the street address of the Whataburger restaurant. The 'city' column indicates the city of the Whataburger restaurant. The 'state' column indicates the state of the Whataburger restaurant. The 'zip_code' column indicates the ZIP code of the Whataburger restaurant . The 'country' column indicates country of Whataburger restaurant . The 'phone_number_1' colum n indicate s th e first phone number o f th e W hata burger restau rant

Research Ideas

• Finde the nearest Whataburger location
• Provide customer support for Whataburger
• Analyze trends in Whataburger restaurant locations

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: whataburger_2018_11_06.csv | Column name | Description | |:-------------------|:-----------------------------------------------------------------| | name | The name of the Whataburger restaurant. (String) | | url | The URL of the Whataburger restaurant. (String) | | street_address | The street address of the Whataburger restaurant. (String) | | city | The city of the Whataburger restaurant. (String) | | state | The state of the Whataburger restaurant. (String) | | zip_code | The ZIP code of the Whataburger restaurant. (String) | | country | The country of the Whataburger restaurant. (String) | | phone_number_1 | The first phone number of the Whataburger restaurant. (String) | | phone_number_2 | The second phone number of the Whataburger restaurant. (String) | | fax_1 | The first fax number of the Whataburger restaurant. (String) | | fax_2 | The second fax number of the Whataburger restaurant. (String) | | email_1 | The first email address of the Whataburger restaurant. (String) | | email_2 | The second email address of the Whataburger restaurant. (String) | | website | The website of the Whataburger restaurant. (String) | | open_hours | The open hours of the Whataburger restaurant. (String) | | latitude | The latitude of the Whataburger restaurant. (Float) | | longitude | The longitude of the Whataburger restaurant. (Float) | | facebook | The Facebook page of the Whataburger restaurant. (String) | | twitter | The Twitter handle of the Whataburger restaurant. (String) | | instagram | The Instagram handle of the Whataburger restaurant. (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 Tony Paul.

The dataset contains every sustained or not yet adjudicated violation citation from every full or special program inspection conducted up to three years prior to the most recent inspection for restaurants and college cafeterias in an active status on the RECORD DATE (date of the data pull). When an inspection results in more than one violation, values for associated fields are repeated for each additional violation record. Establishments are uniquely identified by their CAMIS (record ID) number. Keep in mind that thousands of restaurants start business and go out of business every year; only restaurants in an active status are included in the dataset. Records are also included for each restaurant that has applied for a permit but has not yet been inspected and for inspections resulting in no violations. Establishments with inspection date of 1/1/1900 are new establishments that have not yet received an inspection. Restaurants that received no violations are represented by a single row and coded as having no violations using the ACTION field. Because this dataset is compiled from several large administrative data systems, it contains some illogical values that could be a result of data entry or transfer errors. Data may also be missing. This dataset and the information on the Health Department’s Restaurant Grading website come from the same data source. The Health Department’s Restaurant Grading website is here: http://www1.nyc.gov/site/doh/services/restaurant-grades.page See the data dictionary file in the Attachments section of the OpenData website for a summary of data fields and allowable values.

This data includes the name and location of inactive food service establishments and the violations that were found at the time of the inspection. Inactive food service establishments include only establishments that are no longer in business or have not operated for an extended period of time. This dataset excludes inspections conducted in New York City (https://data.cityofnewyork.us/Health/Restaurant-Inspection-Results/4vkw-7nck), Suffolk County (http://apps.suffolkcountyny.gov/health/Restaurant/intro.html) and Erie County (http://www.healthspace.com/erieny). Inspections are a “snapshot” in time and are not always reflective of the day-to-day operations and overall condition of an establishment. Occasionally, remediation may not appear until the following month due to the timing of the updates. Update frequencies and availability of historical inspection data may vary from county to county. Some counties provide this information on their own websites and information found there may be updated more frequently. This dataset is refreshed on a monthly basis. The inspection data contained in this dataset was not collected in a manner intended for use as a restaurant grading system, and should not be construed or interpreted as such. Any use of this data to develop a restaurant grading system is not supported or endorsed by the New York State Department of Health. For more information, visit http://www.health.ny.gov/regulations/nycrr/title_10/part_14/subpart_14-1.htm or go to the “About” tab.