Graph and download economic data for All Employees: Leisure and Hospitality: Limited-Service Restaurants and Other Eating Places in New York City, NY (SMU36935617072259001SA) from Jan 1990 to May 2025 about restaurant, leisure, hospitality, New York, NY, food, services, employment, and USA.

Graph and download economic data for All Employees: Leisure and Hospitality: Limited-Service Restaurants and Other Eating Places in New York City, NY (SMU36935617072259001A) from 1990 to 2024 about restaurant, leisure, hospitality, New York, NY, food, services, employment, 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!

All Employees: Full-Service Restaurants in New York City, NY was 155.30000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, All Employees: Full-Service Restaurants in New York City, NY reached a record high of 172.30000 in January of 2017 and a record low of 61.50000 in January of 1992. Trading Economics provides the current actual value, an historical data chart and related indicators for All Employees: Full-Service Restaurants in New York City, NY - last updated from the United States Federal Reserve on July of 2025.

All Employees: Leisure and Hospitality: Full-Service Restaurants in New York City, NY was 150.10000 Thous. of Persons in March of 2025, according to the United States Federal Reserve. Historically, All Employees: Leisure and Hospitality: Full-Service Restaurants in New York City, NY reached a record high of 176.60000 in June of 2017 and a record low of 25.70000 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for All Employees: Leisure and Hospitality: Full-Service Restaurants in New York City, NY - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for All Employees: Leisure and Hospitality: Full-Service Restaurants in San Francisco-San Mateo-Redwood City, CA (MD) (SMU06418847072251101SA) from Jan 1990 to May 2025 about restaurant, San Francisco, CA, services, employment, and USA.

All Employees: Leisure and Hospitality: Full-Service Restaurants in Oklahoma City, OK (MSA) was 28.20000 Thous. of Persons in March of 2025, according to the United States Federal Reserve. Historically, All Employees: Leisure and Hospitality: Full-Service Restaurants in Oklahoma City, OK (MSA) reached a record high of 28.60000 in June of 2023 and a record low of 9.40000 in January of 1990. Trading Economics provides the current actual value, an historical data chart and related indicators for All Employees: Leisure and Hospitality: Full-Service Restaurants in Oklahoma City, OK (MSA) - last updated from the United States Federal Reserve on July of 2025.

Comprehensive dataset of 1 Hong Kong style fast food restaurants in Madou District, Tainan City, Taiwan as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The Mexico Foodservice Market is segmented by Foodservice Type (Cafes & Bars, Cloud Kitchen, Full Service Restaurants, Quick Service Restaurants), 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.

Restaurants and mobile food services accounted for the largest sector of the food service industry in the United Kingdom, with 101,152 businesses in 2022. The other main sectors include beverage serving industries (pubs, bars and licensed clubs) and event catering. Overall, the total number of food service businesses operating in the UK increased in 2022 compared to a year earlier.

Graph and download economic data for All Employees: Full-Service Restaurants in San Francisco-Redwood City-South San Francisco, CA (MD) (SMU06418847072251101A) from 1990 to 2024 about restaurant, San Francisco, CA, services, employment, and USA.

Comprehensive dataset of 4 Hong Kong style fast food restaurants in East District, Tainan City, Taiwan as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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.

Comprehensive dataset of 7 Hong Kong style fast food restaurants in Tamsui District, New Taipei City, Taiwan as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset provides insights into customer experiences at McDonald's restaurants, focusing on negative Yelp reviews. It encompasses 1525 observations detailing ratings, reviews, and city locations, alongside classified sentiments. The primary purpose is to offer a sentiment analysis of low-rated McDonald's reviews, with contributors having categorised the reasons for dissatisfaction. These reasons include issues such as rude service, slow service, problems with orders, bad food, poor neighbourhood conditions, dirty locations, high cost, or missing items.

Columns

• _unit_id: A unique identifier for each unit.
• _golden: Indicates if the entry is a golden record.
• _unit_state: The state of the unit.
• _trusted_judgments: The number of trusted judgments for an entry.
• _last_judgment_at: The timestamp of the last judgment.
• policies_violated: Specifies if any policies were violated.
• policies_violated:confidence: The confidence score for policies violated.
• city: The city where the review originated.
• policies_violated_gold: Golden standard for policies violated.
• review: The full text of the customer review.

Distribution

The dataset comprises 1525 individual observations or records. While a specific file format is not detailed for this dataset, data files on the platform are typically provided in CSV format. The exact number of rows and columns is clear from the observation count and listed columns.

Usage

This dataset is ideal for: * Sentiment analysis of customer feedback in the fast-food industry. * Natural Language Processing (NLP) research and model training, particularly for text classification and sentiment prediction. * Identifying common customer pain points and areas for operational improvement within restaurant chains. * Marketing analysis to understand brand perception and customer satisfaction drivers. * Academic studies on consumer reviews and public opinion.

Coverage

The dataset covers customer reviews from various random metro areas, including notable contributions from Las Vegas (27%) and Chicago (14%). The data appears to primarily originate from a single day, 21st February 2015, based on the review timestamps. It focuses on the experiences of customers who dined at McDonald's establishments.

License

CCO

Who Can Use It

• Data scientists and machine learning engineers for building and testing sentiment analysis models.
• Market researchers and brand managers seeking to analyse customer satisfaction and identify areas for brand improvement.
• Restaurant operators and business analysts interested in understanding operational shortcomings and customer grievances.
• Students and academics conducting research on consumer behaviour, text analytics, or the fast-food industry.

Dataset Name Suggestions

• McDonald's Yelp Review Sentiment Analysis
• Fast Food Negative Customer Reviews
• McDonald's Customer Experience Data
• Yelp Reviews: McDonald's Feedback Insights

Attributes

Original Data Source: McDonalds Yelp! Reviews

Comprehensive dataset of 2 Hong Kong style fast food restaurants in East District, Chiayi City, Taiwan as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Context

I was interested in behaviour of people ordering food online. For sample dataset Thane City was selected for analysis of the data. In Thane city, number of restaurants serving variety of cuisines are present. Currently there are approximately 1200 restaurants present in Thane City. I was interested in finding a) Density of restaurant b) Correlation between restaurant type and rating of restaurant c) Cuisines offered by the restaurant

Content Using the Zomato dataset, I could extract and analyse the following: a) Detailed information about restaurant including establishment type, type of cusines, pricing, no of reviews, average rating b) Number of restaurants per sq. km This will help in identifying the areas with high density of restaurants. c) Breakup of rating reviews assigned to restaurant by the users d) Types of cusine offered by restaurants and percentage distribution of the same. e) Frequency distribution of number of reviews by users.

f) Top dishes of each restaurants as per users reviews. Phase I, In Phase I, using Zomato API, top 100 restaurants of each establishment types were extracted. The detailed information as per point a) above was available. Phase II, In Phase II , using the restaurant url, top dishes of the restaurant was scraped from the website. Acknowledgements All copyrights for the data is owned by Zomato Media Pvt. Ltd. This data was extracted for educational purpose only.

This dataset offers a detailed overview of restaurant information, including their location, cuisines, average cost, and user ratings. It is designed to facilitate the analysis of various factors influencing restaurant popularity, such as cuisine type, pricing, and the availability of booking and delivery services. The dataset can be instrumental in developing personalised restaurant recommendation systems and gaining insights into the broader food service industry.

Columns

• Restaurant ID: A unique identifier assigned to each restaurant.
• Restaurant Name: The official name of the dining establishment.
• City: The city where the restaurant is located.
• Address: The specific street address of the restaurant.
• Locality: The neighbourhood or area within the city where the restaurant is situated.
• Longitude: The geographical longitude coordinate of the restaurant's location.
• Latitude: The geographical latitude coordinate of the restaurant's location.
• Cuisines: Describes the types of food offered, such as Japanese, Thai, Chinese, or Mughlai.
• Average Cost for two: The estimated cost for a meal for two people at the restaurant.
• Currency: The currency used for the average cost (e.g., Indian Rupees (Rs.), Dollar ($)).
• Has Table booking: Indicates whether the restaurant accepts table reservations (Yes/No).
• Has Online delivery: Indicates if the restaurant provides online food delivery services (Yes/No).
• Is delivering now: Shows if the restaurant is currently offering delivery (Yes/No).
• Price range: A categorisation of the restaurant's price level, ranging from 1 (least expensive) to 4 (most expensive).
• Aggregate rating: The overall rating of the restaurant based on customer reviews.
• Rating colour: A colour code representing the rating (e.g., Dark green, Green, Yellow, Orange, Red, White).
• Rating text: A text description of the rating (e.g., Excellent, Very good, Good, Average, Poor, Not rated).
• Votes: The total number of user votes or reviews received by the restaurant.

Distribution

The dataset is typically provided in CSV format and comprises approximately 9,531 records. * Average Cost for two: Costs predominantly fall within the 0.00 - 80,000.00 range. * Currency: Indian Rupees (Rs.) accounts for 91% of the entries, while Dollar ($) accounts for 5%. * City: New Delhi represents 57% of the restaurants, Gurgaon 12%, and other cities account for 31%. There are 8,918 unique city values. * Locality: Connaught Place and Rajouri Garden each represent 1% of localities, with 98% falling into other categories. There are 9,330 unique locality values. * Longitude: Values range from -158 to 175, with a significant concentration between 75.00 and 108.28 (8,064 entries). * Latitude: Values range from -41.3 to 56, with a large number of entries between 26.78 and 36.52 (7,911 entries). * Cuisines: North Indian cuisine accounts for 10%, North Indian, Chinese for 5%, and other cuisine combinations for 85%.

Usage

This dataset is ideal for: * Developing restaurant recommendation systems to suggest personalised dining options based on user preferences, location, and restaurant attributes. * Analysing factors affecting restaurant popularity, such as cuisine type, pricing, table booking availability, and online delivery services. * Gaining insights into the food delivery industry dynamics. * Solving problem statements related to the influence of location on cost, the relationship between cuisine type and ratings, the correlation between cost and ratings, and the impact of booking/delivery options on ratings.

Coverage

The dataset's geographic scope is global, with a strong focus on cities like New Delhi (57%) and Gurgaon (12%) in India, and other cities making up the remaining 31%. The time range and specific demographic scope of the data are not specified in the available information.

License

CC0

Who Can Use It

• Data Scientists and Analysts: For conducting deep dives into restaurant trends and consumer behaviour.
• Developers: To build and improve food delivery applications and recommendation engines.
• Researchers: For academic studies on the hospitality and food service industries.
• Businesses: Seeking to understand market dynamics and competitor landscapes.

Dataset Name Suggestions

• Global Restaurant Insights Dataset
• Zomato Restaurant Data
• Foody Restaurant Popularity Data
• Restaurant Dining Trends Dataset
• Worldwide Restaurant Attributes

Attributes

Original Data Source: Global Zomato Dataset

Comprehensive dataset of 3 Self service restaurants in Metropolitan City of Palermo, Italy as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

In the fast-paced world of hospitality, data is essential for success. Our Global Bar & Restaurant POI database offers in-depth information on the locations of the world's top bars and restaurants, providing businesses with a powerful tool for strategic decision-making. Whether you're a restaurant chain, a marketing agency, or a hospitality researcher, our Global Bar & Restaurant database is a valuable resource for making informed decisions.

What You'll Find in the Database:

• Precise Location Data: Full address, geographic coordinates, and neighborhood details for each establishment.

-Visitation Metrics: GDPR-compliant, non-PII foot traffic insights to help you identify the best locations for your next opening.

• Establishment Information: Official name, unique identifier, and type of establishment (bar, restaurant, café, fast-food chain, etc.).

• Operational Status: Whether the establishment is currently open or closed.

• Date Established: Historical context for trend analysis.

• Data Confidence Level: A rating indicating the accuracy of the information.

How You Can Use This Database:

• Market Analysis: Assess the distribution and density of bars and restaurants globally.

• Site Selection: Identify promising locations for new establishments based on demographics, competition, and visitation metrics of nearby establishments.

• Targeted Marketing: Reach customers near specific establishments with personalized offers.

• Competitive Intelligence: Understand the landscape and identify rivals' strategies.

• Supply Chain Optimization: Streamline logistics based on the distribution of your target establishments.