We stopped reporting new data on Oct 15, 2022. All published data will remain publicly available.


This documentation explains how public health officials can best use the Community Mobility Reports in their work

The Community Mobility Reports show movement trends by region, across different categories of places. For each category in a region, reports show the changes in 2 different ways:

  • Headline number: Compares mobility for the report date to the baseline day. Calculated for the report date (unless there are gaps) and reported as a positive or negative percentage.
  • Trend graph: The percent changes in the 6 weeks before the report date. Shown as a graph.

No personally identifiable information, such as an individual’s location, contacts or movement, will be made available at any point. These reports are created with aggregated, anonymized sets of data from users who have turned on the Location History setting, which is off by default.

How the headline number date relates to the report date


Graph with animated headline number that decreases and increases

This diagram shows a report dated Mar 27 2020. The headline number (-15% to -63% in this example) is the percent change for the latest day—also called the report date

The headline number isn't an average or the trend of the previous changes. Notice how only the last day of the graph changes when the headline number changes.


If we didn't have enough data to confidently and anonymously estimate the change from the baseline, you’ll see gaps and the headline number is the most-recent calculated change.

Avoid comparing places across regions. Regions can have local differences in the data which might mislead.

Report date

We create a PDF report for a specific calendar date. This is the date we compare in the headline numbers. You can find the report date at the top of the PDF or in the filename (if no one has renamed the file).


The data shows how visitors to (or time spent in) categorized places change compared to our baseline days. A baseline day represents a normal value for that day of the week. The baseline day is the median value from the 5‑week period Jan 3 – Feb 6, 2020.

For each region-category, the baseline isn’t a single value—it’s 7 individual values. The same number of visitors on 2 different days of the week, result in different percentage changes. So, we recommend the following:

  • Don’t infer that larger changes mean more visitors or smaller changes mean less visitors.
  • Avoid comparing day-to-day changes. Especially weekends with weekdays.

How did we pick perfectly normal baseline days? We probably haven’t—a short period of the year can't represent normal for every region on our planet. We picked a recent period, before widespread disruption as communities responded to COVID-19. Even so, for some regions, the baseline falls during a time when COVID-19 was established. To interpret the data for your region, follow the local checklist.

To help you track week-to-week changes, the baseline days never change. These baseline days also don't account for seasonality. For example, visitors to parks typically increase as the weather improves.

Place categories

To make the reports useful, we use categories to group some of the places with similar characteristics for purposes of social distancing guidance. For example, we combine grocery and pharmacy as these tend to be considered essential trips.

Each high-level category contains many types of places—some might not be obvious. The following table shows just some of the wide range of places included in 2 example categories:

Parks Transit stations
Public garden Subway station
Castle Sea port
National forest Taxi stand
Camp ground Highway rest stop
Observation deck Car rental agency

Note, Parks typically means official national parks and not the general outdoors found in rural areas.

Long-term analysis

As time passes and we move further away from the baseline period, populations might vary due to relocation or new regional and remote working options. Google’s understanding of categorized places might also change. For example, the same value today and in April 2020 might not indicate the same behavior or adherence—it might be that Google has updated information about shops and restaurants in the region or that fewer people live there now. These differences could shift the values up or down over long time periods, so we recommend using some caution when analyzing data from longer time intervals (6+ months).

Next steps

To know how you should interpret common patterns in the data, read Understand the data.

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