In May 2025, mcdonalds.com.au recorded a web traffic of around **** million website visits in Australia, making it the leading fast food website in the country by visits. dominos.com.au recorded around *** million site visits that same month.

Some types of food and drink establishments are better known for off premises services than others. In 2019, coffee and snack establishments held the highest share of off-premise customer traffic with 73 percent of overall traffic being off premises. Meanwhile, quick service restaurants weren't far behind with 72 percent of QSR traffic being off premises. In contrast, fine dining establishments accounted for the lowest off premises customer traffic, with only seven percent.

In December 2023, the food delivery website doordash.com was the most visited in the restaurant and delivery category in the United States, accounting for roughly **** percent of desktop traffic. Following in second place with a visit share of just over three percent was toasttab.com.

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.

Dataset of 700,000 restaurants in the United States complete with detailed contact and geolocation data. The database includes multiple data points such as restaurant name, address, phone number, website, email, opening hours, latitude, longitude, and cuisine.

In December 2023, the online food delivery website ubereats.com was the most visited in the restaurant and delivery category worldwide, accounting for about 4.75 percent of desktop traffic. Doordash.com followed in a close second place with a visit share of 4.32 percent.

Point of Interest (POI) is defined as an entity (such as a business) at a ground location (point) which may be (of interest). We provide high-quality POI data that is fresh, consistent, customizable, easy to use and with high-density coverage for all countries of the world.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

A new POI comes into existence. It could be a bar, a stadium, a museum, a restaurant, a cinema, or store, etc.. In today's interconnected world its information will appear very quickly in social media, pictures, websites, press releases. Soon after that, our systems will pick it up.

POI Data is in constant flux. Every minute worldwide over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist. And over 94% of all businesses have a public online presence of some kind tracking such changes. When a business changes, their website and social media presence will change too. We'll then extract and merge the new information, thus creating the most accurate and up-to-date business information dataset across the globe.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via our data update pipeline.

Customers requiring regularly updated datasets may subscribe to our Annual subscription plans. Our data is continuously being refreshed, therefore subscription plans are recommended for those who need the most up to date data. The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

Data samples may be downloaded at https://store.poidata.xyz/us

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.

Analysis of ‘Top 100 Biggest Restaurant Chains 2021’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/johnharshith/top-100-biggest-restaurant-chains-2021 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

https://i.insider.com/5db704d7045a311ad239369b?width=1300&format=jpeg&auto=webp" alt="Popular Restaurant Chains">

Context

This Dataset contains the data compiled by Technomic and reported by Restaurant Business magazine, the top 100 most popular restaurant chains in the United States in terms of the latest 2020 sales which were responsible for three-fourths of the total industry sales growth last year.

Content

The data was obtained from the Restaurant Business magazine website. The columns contain stats such as position of restaurant chains, 2020 U.S. sales, YOY sales change, 2020 U.S. units, YOY unit change, segment and menu types. This data can be found from the website https://www.restaurantbusinessonline.com/top-500-chains with detailed analysis.

Inspiration

While 2016 was a rough year for chain restaurants, more than half of the industry wealth of $521.9 billion still comes from the Top 500 chains and nearly 94% of those dollars and 93% of those units are represented in the Top 250. These stats have made me curious to find out interesting profit patterns from this dataset.

Dataset Usage

This Dataset can be used to study interesting patterns using various classification techniques and arrive at some exciting conclusions. One can create amazing visualisations using the different columns of the dataset. We can also find out and design an effective business model from the given dataset and take one step closer to your most successful restaurant chain startup ever!

--- Original source retains full ownership of the source dataset ---

In February of 2021, consumers in the United States were asked whether they preferred using a restaurant's own app or website for food delivery. The majority of respondents, 67 percent, stated that they did prefer using a restaurant's own app or website for food delivery. Meanwhile, the remaining 33 percent stated that they preferred using another method, such as a third-party app or website.

Analysis of ‘Restaurant Business Rankings 2020’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/michau96/restaurant-business-rankings-2020 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The year 2020 is behind us, therefore, many summaries of this pandemic year are created, e.g. in the form of rankings. Such a list (or actually 3 lists) was created by the "Restaurant Business" magazine. On the website you can find only basic information about ranked restaurants, full data and analyzes are available in the paid report.

Content

The data was obtained by means of web scraping, i.e. data download with the use of programming code based on the website code. In this case, the "rvest" package from the R programming language was used along with the "SelectorGadet" browser add-on to facilitate work with the website.

The data was downloaded from www.restaurantbusinessonline.com on January 30, 2021 with three plants describing 3 rankings: top 250, top 100 indenents and future 50 thus creating 3 tables, where the restaurant is described by several variables in each row.

Inspiration

The data can be used to tell the story of what 2020 was like for restaurants, what was hot, what could be more popular soon, or what the difference is between large companies and smaller businesses. I am curious what useful information can be obtained from this data!

Photo by Luis Hansel on Unsplash.

--- Original source retains full ownership of the source dataset ---

This dataset contains Wake County Restaurant information and location data pulled from Wake County's GIS REST endpoint. For more information see Wake County's webpage.This dataset is updated daily.

Data you can expect: - Metadata (country, region, city, coordinates, address, categories, description, operating hours, and more) - Contacts (phone contacts, email, website) - Social profiles (LinkedIn, Twitter, Instagram, Facebook) - Other info (awards, Michelin stars, executive chef, popular dishes, average meal price, and more)

If you are looking for other data such as photos or menus, let us know since we have them as well.

How we deliver data: - We transform it to fit your system's data schema, (ease the pain and cost of having data engineers from your side) - We are completely flexible on the delivery format and method.

Analysis of ‘Open Restaurant Applications’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/adf35eb6-ac87-4f63-9aed-893bf628ccbc on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Open Restaurant Applications is a dataset of applications from food service establishments seeking authorization to re-open under Phase Two of the State’s New York Forward Plan, and place outdoor seating in front of their business on the sidewalk and/or roadway.

For more information please visit NYC DOT Open Restaurants website: nyc.gov/openrestaurants

For the most up to date Open Restaurant updates, please visit: https://bit.ly/2Z00kn8

--- Original source retains full ownership of the source dataset ---

In December 2023, deliveryhero.com was the most visited website in the restaurant and delivery category in Sweden, accounting for just over 15 percent of desktop traffic. Uber Eats' web page came in second place, with a visit share of 7.45 percent.

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

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.

Open Restaurant Applications is a dataset of applications from food service establishments seeking authorization to re-open under Phase Two of the State’s New York Forward Plan, and place outdoor seating in front of their business on the sidewalk and/or roadway.

For more information please visit NYC DOT Open Restaurants website: nyc.gov/openrestaurants

For the most up to date Open Restaurant updates, please visit: https://bit.ly/2Z00kn8

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

The Wake County health department inspects food service facilities throughout Wake County. The department permits and inspects these facilities, and responds to citizen complaints. In the event of disease outbreak, the department investigates to determine the source of the infection, and prevent further illness.

This dataset captures the restaurants that are inspected. The data set is geocoded based on address with approximately 85% of the locations having a valid geo-location.

You can find out additional information about our restaurant inspections on our website: Food Safety and Sanitation

This table captures all Wake County sanitation inspections from September 20, 2012 to Present.

This table is part of a set of data that combined will give you a picture of all restaurant inspections. Those three tables are:

1. Restaurants: This table captures all active facilities where Wake County performs sanitations inspections. Facilities that are closed are removed from all three files in this dataset. Per NC State regulations, facilities that have a change in ownership are considered closed and the restaurant re-opens under a new permit, even if there is not a change in the name of the restaurant.

2. Food Inspections: This table captures all Wake County performs sanitations inspections at active restaurants since September 20, 2012

3. Food Inspection Violations: This table captures all violations identified during specific Wake County sanitations inspections at active restaurants since September 20, 2012. It reports the results in code violations and according to CDC Risk Factors. You can find additional information about the CDC Risk Factors on the FDA website: "http://www.fda.gov/Food/GuidanceRegulation/RetailFoodProtection/FoodborneIllnessRiskFactorReduction/ucm224321.htm">Retail Risk Factor Study

The tables can be connected through the HSISID field and the Permit ID field.

The frequency of facility inspections fall under the following rules:

Inspected once per year:

Risk Category I applies to food service establishments that prepare only non-potentially hazardous foods.

Inspected twice per year:

Risk Category II applies to food service establishments that cook and cool no more than two potentially hazardous foods. Potentially hazardous raw ingredients shall be received in a ready-to-cook form.

Inspected three times per year

Risk Category III applies to food service establishments that cook and cool no more than three potentially hazardous foods.

Inspected four times per year

Risk Category IV applies to food service establishments that cook and cool an unlimited number of potentially hazardous foods. This category also includes those facilities using specialized processes or serving a highly susceptible population.