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Data

Grocery and pharmacy stores: How did the number of visitors change relative to before the pandemic?

See all data and research on:
COVID-19

What you should know about this indicator

  • These datasets show how visits and length of stay at different places change compared to a baseline. Google calculates these changes using the same kind of aggregated and anonymized data used to show popular times for places in Google Maps.
  • Changes for each day are compared to a baseline value for that day of the week:
    • The baseline is the median value, for the corresponding day of the week, during the 5-week period Jan 3–Feb 6, 2020.
    • The datasets show trends over several months with the most recent data representing approximately 2-3 days ago—this is how long it takes to produce the datasets.
  • What data is included in the calculation depends on user settings, connectivity, and whether it meets our privacy threshold. When the data doesn't meet quality and privacy thresholds, you might see empty fields for certain places and dates.
  • Google includes categories that are useful to social distancing efforts as well as access to essential services.
  • Google calculates these insights based on data from users who have opted-in to Location History for their Google Account, so the data represents a sample of our users. As with all samples, this may or may not represent the exact behavior of a wider population.
Grocery and pharmacy stores: How did the number of visitors change relative to before the pandemic?
Mobility trends for places like grocery markets, food warehouses, farmers markets, specialty food shops, drug stores, and pharmacies.
Source
Google (2022) – with minor processing by Our World in Data
Last updated
July 31, 2024
Unit
%

Related research and writing

Google Mobility Trends: How has the pandemic changed the movement of people around the world?

Hannah Ritchie

Explore charts that include this data

Sources and processing

This data is based on the following sources

Google – COVID-19, Community Mobility Reports

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19.

These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Retrieved on
July 31, 2024
Retrieved from
https://www.google.com/covid19/mobility/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Google LLC "Google COVID-19 Community Mobility Reports".
https://www.google.com/covid19/mobility/ Accessed: 2024-07-31.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19.

These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Retrieved on
July 31, 2024
Retrieved from
https://www.google.com/covid19/mobility/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Google LLC "Google COVID-19 Community Mobility Reports".
https://www.google.com/covid19/mobility/ Accessed: 2024-07-31.

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

This indicator has been smoothed by averaging its values with a centered 7-day rolling window.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Grocery and pharmacy stores: How did the number of visitors change relative to before the pandemic?”, part of the following publication: Edouard Mathieu, Hannah Ritchie, Lucas Rodés-Guirao, Cameron Appel, Daniel Gavrilov, Charlie Giattino, Joe Hasell, Bobbie Macdonald, Saloni Dattani, Diana Beltekian, Esteban Ortiz-Ospina, and Max Roser (2020) - “COVID-19 Pandemic”. Data adapted from Google. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/change-visitors-grocery-stores.html [online resource] (archived on March 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

Google (2022) – with minor processing by Our World in Data

Full citation

Google (2022) – with minor processing by Our World in Data. “Grocery and pharmacy stores: How did the number of visitors change relative to before the pandemic?” [dataset]. Google, “COVID-19, Community Mobility Reports” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/change-visitors-grocery-stores.html (archived on March 4, 2026).

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/change-visitors-grocery-stores.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/change-visitors-grocery-stores.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/change-visitors-grocery-stores.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/change-visitors-grocery-stores.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/change-visitors-grocery-stores.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/change-visitors-grocery-stores.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/change-visitors-grocery-stores.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/change-visitors-grocery-stores.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear