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

Xtract.io's comprehensive location data for restaurants and food stores offers a detailed view of the retail food landscape. Retail strategists, market researchers, and business developers can utilize this dataset to analyze market distribution, identify emerging trends, and develop targeted expansion strategies across the food retail sector.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geospatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

Content

In this dataset, you will find over 10,000 different fast-food restaurants located all over the United States of America.

Acknowledgements

This dataset comes from https://data.world/datafiniti/fast-food-restaurants-across-america.

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

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

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

About this Dataset

Contact information for over 20,000 restaurants across the US. All restaurants from the NAICS code 72251: Restaurants and Other Eating Places. This includes all set down, fast casual, fast food, and ethnic restaurants. List includes name, address, phone number, website, contact email address, and a brief description. Data was collected from a combination of web scrapping and manual data entry. Similar lists cost over $1500 from lead generation and business data companies.

Category

Lead Generation

Keywords

restaurants,contact,mailing

Row Count

21210

Price

$499.00

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

Context

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.

Inspiration

You can use this data to rank cities with the most and least fast-food restaurants across the U.S. E.g.:

1. Cities with the most and least McDonald's per capita
2. Fast food restaurants per capita for all states
3. Fast food restaurants with the most locations nationally
4. Major cities with the most and least fast food restaurants per capita
5. Small cities with the most fast-food restaurants per capita
6. States with the most and least fast food restaurants per capita
7. The number of fast-food restaurants per capita

If you like the dataset, do upvote!

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

{"definition": "The number of full-service restaurants in the county per 1,000 residents. Full-service restaurants (defined by North American Industry Classification System (NAICS) code 722110) include establishments primarily engaged in providing food services to patrons who order and are served while seated (i.e., waiter/waitress service) and pay after eating. These establishments may provide this type of food service to patrons in combination with selling alcoholic beverages, providing takeout services, or presenting live nontheatrical entertainment.", "availableYears": "2012", "name": "Full-service restaurants/1,000 pop, 2012", "units": "Count", "shortName": "FSRPTH12", "geographicLevel": "County", "dataSources": "Restaurant data are from the U.S. Census Bureau, County Business Patterns. Population data are from the U.S. Census Bureau, Population Estimates."}

© FSRPTH12

This layer is sourced from gis.ers.usda.gov.

Analysis of ‘🍕 Pizza restaurants and Pizzas on their Menus’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/pizza-restaurants-and-pizzas-on-their-menuse on 13 February 2022.

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

About this dataset

About this Data

This is a list of over 3,500 pizzas from multiple restaurants provided by Datafiniti's Business Database. The dataset includes the category, name, address, city, state, menu information, price range, and more for each pizza restaurant.

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 how much you can expect to pay for pizza across the country. E.g.:

• What are the least and most expensive cities for pizza?
• What is the number of restaurants serving pizza per capita (100,000 residents) across the U.S.?
• What is the median price of a large plain pizza across the U.S.?
• Which cities have the most restaurants serving pizza per capita (100,000 residents)?

Data Schema

A full schema for the data is available in our 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 10000 samples along with Longitude, Price Range Max, technical information and other features such as: - Date Updated - Categories - and more.

How to use this dataset

• Analyze Date Added in relation to Province
• Study the influence of Price Range Min on Address
• More datasets

Acknowledgements

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

Start A New Notebook!

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

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

The United States Full Service Restaurants Market is segmented by Cuisine (Asian, European, Latin American, Middle Eastern, North American), by Outlet (Chained Outlets, Independent Outlets) and by Location (Leisure, Lodging, Retail, Standalone, Travel). Market Value in USD is presented. Key data points observed include the number of outlets for each foodservice channel; and, average order value in USD by foodservice channel.

Xtract.io's comprehensive McDonald's location data provides a detailed view of the global fast-food chain's network. Restaurant investors, market researchers, and business analysts can utilize this dataset to analyze market penetration, identify expansion opportunities, and develop a sophisticated understanding of McDonald's geographical strategy.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geospatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

Snapshot img

US Fast Casual Restaurants Market Size 2025-2029

US fast casual restaurants market size is forecast to increase by USD 84.5 billion at a CAGR of 13.7% between 2024 and 2029.

US Fast Casual Restaurants Market is experiencing significant growth, driven by the increasing demand for innovation and customization in food menus. Consumers are seeking more personalized dining experiences, leading to the popularity of fast casual concepts that offer a unique blend of affordability, quality, and convenience. Additionally, the market is witnessing an increasing focus on digitalization of services, with contactless ordering and payment options gaining traction in response to the ongoing pandemic. However, the market faces challenges as well. Intense competition from quick-service restaurants and the need to maintain consistent supply chains pose significant hurdles for market growth.
Regulatory compliance and labor costs also add complexity to the operational landscape. To capitalize on opportunities and navigate challenges effectively, companies must stay abreast of consumer trends and invest in digital technologies to enhance the customer experience. Innovation in menu offerings, sustainable sourcing, and strategic partnerships will be key differentiators in this dynamic market.

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

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 US Fast Casual Restaurants market is experiencing significant growth, driven by various factors. Menu innovation, with an emphasis on vegetarian, vegan, and gluten-free options, caters to diverse dietary preferences and allergies. Customer experience is prioritized through personalized dining, contactless ordering, and mobile payments. Data-driven decision-making and restaurant automation, including artificial intelligence and predictive analytics, optimize operations and reduce labor costs. Franchise models and in-house delivery services expand reach and cater to Generation Z's preference for convenience. Supply chain management and food traceability ensure transparency and sustainability, while omni-channel strategies engage customers through loyalty programs and subscription services.Restaurant analytics provide valuable customer feedback, enabling continuous improvement and operational optimization.

How is this market segmented?

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

Channel

  Dine-in
  Takeaway


Application

  Franchised
  Standalone


Food Type

  Burger/Sandwich
  Pizza/Pasta
  Asian
  Latin American
  Chicken
  Others


Target Audience

  Millennials
  Working Professionals
  Families


Distribution Channel Specificity

  Specialty Chains
  Online Platforms
  Retail Foodservice


Geography

  North America

    US

By Channel Insights

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

Fast casual restaurants in the US and Canada offer a unique dining experience, blending the speed and convenience of fast food with the quality and ambiance of casual dining. These establishments, which have gained popularity in recent years, use fresh ingredients, digital ordering systems, and self-service kiosks to streamline the dining process while maintaining food quality. Employee training is a key focus to ensure consistent customer service and food safety. Menu innovation and healthy options cater to various consumer preferences, including Generation Z and those seeking sustainable practices. In addition, many fast casual restaurants offer catering services, in-house delivery, and third-party delivery through food delivery platforms.

The franchise model is also common in the fast casual industry, allowing for efficient expansion and operational efficiency. Restaurant technology plays a significant role in fast casual operations, with kitchen display systems, labor scheduling, and point-of-sale systems helping to manage inventory and improve efficiency. Digital marketing and social media marketing are essential for customer engagement and loyalty programs. Food trucks and ghost kitchens are emerging trends in the fast casual industry, offering alternative business models and lower overhead costs. Food safety and food quality are top priorities, with strict adherence to industry standards and regulations. The fast casual industry continues to evolve, with dining trends such as virtual brands, subscription services, and data analytics shaping the future of this dynamic market.

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

Market Dynamics

Our researchers analyzed the data with 2024 as the base year,

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.

Whatscooking.restaurants

  Overview

This dataset provides detailed information about various restaurants, including their location, cuisine, ratings, and other attributes. It is particularly useful for applications in food and beverage industry analysis, recommendation systems, and geographical studies.

  Dataset Structure

Each record in the dataset represents a single restaurant and contains the following fields:

_id: A unique identifier for the restaurant… See the full description on the dataset page: https://huggingface.co/datasets/MongoDB/whatscooking.restaurants.

Extensive US food and dining POI database with 943K locations. Covers restaurants, dessert parlors, bakeries, cafes, and more. Ideal for market analysis, trend spotting, and strategic planning in the food service industry. High-quality data enables confident business decisions.

This table contains 375 series, with data for years 1998 - 2001 (not all combinations necessarily have data for all years), and was last released on 2008-09-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia ...), North American Industry Classification System (NAICS) (5 items: Food services and drinking places; Special food services; Full-service restaurants; Limited-service eating places ...), Summary statistics (5 items: Operating revenue; Operating profit margin; Salaries; wages and benefits; Operating expenses ...).