The coronavirus (COVID-19) pandemic caused the United States' restaurant industry to take a huge hit. Due to measures of social distancing and general caution in public places, consumers were forced to dine out less. According to the source, the year-over-year change of seated diners in restaurants in the U.S., compared to 2019, dropped **** percent on August 1, 2022.

The coronavirus pandemic brought major changes to dining behaviors among restaurant-goers in the United States. Restaurant closures and social distancing measures resulted in an increasing demand for online food delivery, both directly through a restaurant's website or using a third-party delivery service. In 2020, the online restaurant delivery sector's share of the restaurant market was predicted to be ** percent, before the pandemic this figure was forecast at **** percent. The post-coronavirus market share was expected to rise as much as ** percent in 2025.

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 global restaurant industry was seriously impacted by the coronavirus (COVID-19) pandemic. Social distancing measures and general caution towards public places caused many consumers to dine out less. According to the source, the year-over-year change of seated diners in restaurants worldwide, compared to 2019, was **** percent on August 1, 2022. Has the global online food delivery sector grown due to COVID-19? The market size of the global online food delivery sector was estimated to total ***** billion U.S. dollars in 2022, a figure that is forecast to grow to over *** billion U.S. dollars by 2027. Due to the coronavirus (COVID-19) pandemic, and a subsequent lack of in-house dining, worldwide digital restaurant food delivery grew across various countries from 2019 to 2020. Digital delivery services are defined as meals or snacks ordered via mobile app, internet, or text message. In total, digital restaurant delivery increased ** percent globally, with the United States increasing the most at *** percent. What is the leading restaurant chain worldwide? When looking at the global restaurant landscape, the majority of the biggest brands are quick service restaurants (QSRs). In a 2021 ranking of the most valuable quick service brands worldwide, McDonald's came out on top, reaching a brand value of ***** billion U.S. dollars. Meanwhile, Starbucks was a not so close second place, at approximately **** billion U.S. dollars.

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. 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.

BackgroundCOVID-19 has harmed restaurants, but customer preferences remain unknown. This study aims to determine the needs, barriers, interests, and food choice changes in restaurants and customers before and during the COVID-19 pandemic in Tarragona Province (Spain).MethodsAn observational cross-sectional study conducted in spring 2021 collected Mediterranean offerings, food safety, and hygiene information about the pandemic through online surveys and focus group interviews with restaurateurs and customers about the changes in their needs and new barriers.ResultsFifty-one restaurateurs (44 survey, 7 focus group) and 138 customers (132 survey, 6 focus group) were included. In relation to the economic, emotional, and uncertainty restaurateurs’ barriers detected, they implemented measures to tackle it: buy less and more often, reduce restaurant staff and reduce the restaurants offer, among others. Some customers reported changes in their restaurant orders, specifically increasing their takeaway orders. The Mediterranean diet offer (AMed criteria) remained without noticeable changes in any of the criteria. After lockdown, compared to before lockdown, restaurateurs increased their takeaway offerings by 34.1% (p 

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) |

...

The coronavirus (COVID-19) pandemic caused the United Kingdom's (UK) restaurant industry to take a huge hit. Due to measures of social distancing and general caution in public places, consumers were forced to dine out less. According to the source, the year-over-year change of seated diners in restaurants in the UK, compared to 2019, was 19.50 percent on August 1, 2022.

This dataset is no longer being updated as of 7/1/2021. It is being retained on the Open Data Portal for its potential historical interest.

A list of retail stores, restaurants, personal services and other businesses open and closed during the COVID-19 pandemic. Also indicates if business is offering delivery, pick up or on-line sales.

Updated at least biweekly during Covid-19 Pandemic.

dataset for restaurants

This dataset contains information on applications

In this article, IBISWorld analyst Jeremy Moses dives into trends within the restaurant sector amid the COVID-19 pandemic.

Provides public health guidance to support operators in reducing the risk of transmission of COVID-19 among guests and workers in restaurants, cafes, pubs and bars. NOTE: Translations are available in the Related tab for previous versions.

Effects of COVID-19 State Restaurant Policies on Immigrant Restaurant Worker Outcomes

The coronavirus (COVID-19) pandemic severely impacted the restaurant industry in the United States in 2020. Due to social distancing measures and general caution in public places, consumers were dining out less. According to the source, visits to full service restaurants in the U.S. went down by ** percent in the second quarter of 2020 compared to the previous year. Meanwhile, quick service restaurants were not quite as badly affected, showing a YoY decline of ** percent in Q2 2020.

COVID-19 Outcomes for Immigrants in the United States Restaurant Industry

Public US Government data about financial assistance to restaurants during the pandemic. According to the Small Business Administration website

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 will provide 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.

The original data and data dictionary are published here. I downloaded and minimally processed the data using this notebook.

Sweden Business Survey: COVID-19 Effect: Turnover: Services: HR: Restaurants: Decreased: 26 to 50 Percent data was reported at 2.000 % in 11 Aug 2021. This records a decrease from the previous number of 9.000 % for 09 Jun 2021. Sweden Business Survey: COVID-19 Effect: Turnover: Services: HR: Restaurants: Decreased: 26 to 50 Percent data is updated daily, averaging 16.000 % from May 2020 (Median) to 11 Aug 2021, with 19 observations. The data reached an all-time high of 41.000 % in 23 Sep 2020 and a record low of 2.000 % in 11 Aug 2021. Sweden Business Survey: COVID-19 Effect: Turnover: Services: HR: Restaurants: Decreased: 26 to 50 Percent data remains active status in CEIC and is reported by National Institute of Economic Research. The data is categorized under Global Database’s Sweden – Table SE.S008: Business Survey: COVID-19 Effect: Turnover (Discontinued).

This indicator shows how the number of visitors to retail and recreation places has changed relative to the period before the pandemic.

Motivation

Why did I create this dataset? This is my first time creating a notebook in Kaggle and I am interested in learning more about COVID-19 and how different countries are affected by it and why. It might be useful to compare different metrics between different countries. And I also wanted to participate in a challenge, and I've decided to join the COVID-19 datasets challenge. While looking through the projects, I noticed https://www.kaggle.com/koryto/countryinfo and it inspired me to start this project.

Method

My approach is to scour the Internet and Kaggle looking for country data that can potentially have an impact on how the COVID-19 pandemic spreads. In the end, I ended up with the following for each country:

• Monthly temperature and precipitation from Worldbank
• Latitude and longitude
• Population, density, gender and age
• Airport traffic from Worldbank
• COVID-19 date of first case and number of cases and deaths as of March 26, 2020
• 2009 H1N1 flu pandemic cases and deaths obtained from Wikipedia
• Property affordability index and Health care index from Numbeo
• Number of hospital beds and ICU beds from Wikipedia
• Flu and pneumonia death rate from Worldlifeexpectancy.com (Age Adjusted Death Rate Estimates: 2017)
• School closures due to COVID-19
• Number of COVID-19 tests done
• Number of COVID-19 genetic strains
• US Social Distancing Policies from COVID19StatePolicy’s SocialDistancing repository on GitHub
• DHL Global Connectedness Index 2018 (People Breadth scores)
• Datasets have been merged by country name whenever possible. I needed to rename some countries by hand, e.g. US to United Sates, etc. but it's possible that I might have missed some. See the output file covid19_merged.csv for the merged result.

See covid19_data - data_sources.csv for data source details.

Notebook: https://www.kaggle.com/bitsnpieces/covid19-data

Caveats

Since I did not personally collect each datapoint, and because each datasource is different with different objectives, collected at different times, measured in different ways, any inferences from this dataset will need further investigation.

Other interesting sources of information

• IMF Policy Tracker
• nCov strain analysis across the globe!
• Google Mobility Report
• Oxford government response tracker
• Impact on aviation
• Impact on restaurants
• Harvard
• Tableau datahub
• Unemployment outlook from ILO

Acknowledgements

I want to acknowledge the authors of the datasets that made their data publicly available which has made this project possible. Banner image is by Brian.

I hope that the community finds this dataset useful. Feel free to recommend other datasets that you think will be useful / relevant! Thanks for looking.