README.md

---
pretty_name: Google Community Mobility Reports — global daily
license: cc-by-4.0
size_categories:
- 10M<n<100M
tags:
- cadence-daily
- geo-global
- surveillance-mobility
- tier-2
- availability-inactive
schema_version: '0.1'
source_id: global-mobility
source_url: https://www.google.com/covid19/mobility/
manifest_section: §15.7
surveillance_category: mobility
pathogens: []
availability: inactive
availability_notes: Google ended data collection 2022-10-15. Historical CSV remains available; not updated.
access_type: csv
tier: 2
cadence: daily
geography_levels:
- national
- subnational-state
- subnational-county
- subnational-city
geography_countries:
- multiple
gold_standard_for: []
vintaged_version_of: null
succeeds: null
derived_from: []
value_columns:
- name: retail_and_recreation
  unit: percent change vs. baseline
  value_type: index
  description: 'Daily % change in visits + length of stay at retail / recreation places

    (restaurants, cafes, shopping centers, theme parks, museums, libraries,

    cinemas) compared to the baseline (median value for the 5-week period

    Jan 3 – Feb 6, 2020).

    '
  aggregation: mean
- name: grocery_and_pharmacy
  unit: percent change vs. baseline
  value_type: index
  description: Daily % change in visits to grocery markets, food warehouses, farmers' markets, specialty food shops, drug
    stores, pharmacies.
  aggregation: mean
- name: parks
  unit: percent change vs. baseline
  value_type: index
  description: Daily % change in visits to local parks, national parks, public beaches, marinas, dog parks, plazas, public
    gardens.
  aggregation: mean
- name: transit_stations
  unit: percent change vs. baseline
  value_type: index
  description: Daily % change in visits to public transport hubs (subway, bus, train stations).
  aggregation: mean
- name: workplaces
  unit: percent change vs. baseline
  value_type: index
  description: Daily % change in visits to places of work.
  aggregation: mean
- name: residential
  unit: percent change vs. baseline
  value_type: index
  description: 'Daily % change in time spent in places of residence. (Note: this is

    length-of-stay, not visit count — different metric than the other five.)

    '
  aggregation: mean
notes:
  extra_columns:
  - column: location_name
    description: 'Most-specific populated source field — county name for county rows,

      metro name for metro rows, ISO sub-region name for state rows, country

      name for national rows. `location_id` is canonical.

      '
  interpretation_caveats:
  - column: residential
    caveat: 'Residential is reported as % change in *length of stay*, not visit

      count. The other five categories are visit-based. Don''t sum or compare

      directly across these two flavours.

      '
  - column: parks
    caveat: 'Parks shows extreme seasonal cyclicity (winter vs. summer is a 200%+

      swing in many countries) that reflects normal seasonality, not

      pandemic-period behaviour change. Detrend before any pandemic analysis.

      '
  - column: workplaces
    caveat: 'Holidays / weekends are baked into the baseline since the baseline is

      a median across weekdays + weekends. Day-of-week cycles in the output

      are real but reflect *deviation* from the comparable weekday''s baseline.

      '
  general: 'Aggregated, anonymised mobility signals derived from Google Maps Location

    History data. Coverage is global at the national level; subnational depth

    varies by country (US has county granularity, most others have ISO 3166-2

    state-level only). Privacy threshold means small geographies / low-traffic

    days are NaN.

    '
extra:
  baseline_period: median value across Jan 3 – Feb 6, 2020 (5 weeks, per weekday)
  collection_end_date: '2022-10-15'
  source_columns_omitted: '`place_id` (Google internal place identifier — opaque), `sub_region_1`,

    `sub_region_2`, `country_region`, `metro_area` (collapsed into the derived

    `location_id` + `location_name`), `country_region_code`, `iso_3166_2_code`,

    `census_fips_code` (likewise collapsed).

    '
computed:
  last_ingested: '2026-04-26T12:14:44Z'
  row_count: 11728497
  time_coverage:
  - start: '2020-02-15'
    end: '2022-10-15'
  geography_unit_count: 5306
  observed_cadence_days: 1
  missing_gaps: []
  data_hash: 63d11fb60ba0adbb
---

# Google Community Mobility Reports — global daily

Aggregated, anonymised mobility signals derived from Google Maps Location
History data. Coverage is global at the national level; subnational depth
varies by country (US has county granularity, most others have ISO 3166-2
state-level only). Privacy threshold means small geographies / low-traffic
days are NaN.

**Source:** <https://www.google.com/covid19/mobility/>

## Coverage

- **Time:** 2020-02-15 → 2022-10-15
- **Cadence:** `daily` (observed median spacing: 1 days)
- **Geography levels:** `national`, `subnational-state`, `subnational-county`, `subnational-city` — 5306 unique location IDs
- **Countries:** multiple
- **Pathogens:** —
- **Surveillance category:** `mobility`
- **Rows:** 11,728,497

## Columns

| Column | Unit | value_type | Aggregation | Description |
|--------|------|------------|-------------|-------------|
| `retail_and_recreation` | percent change vs. baseline | `index` | `mean` | Daily % change in visits + length of stay at retail / recreation places
(restaurants, cafes, shopping centers, theme parks, museums, libraries,
cinemas) compared to the baseline (median value for the 5-week period
Jan 3 – Feb 6, 2020).
 |
| `grocery_and_pharmacy` | percent change vs. baseline | `index` | `mean` | Daily % change in visits to grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores, pharmacies. |
| `parks` | percent change vs. baseline | `index` | `mean` | Daily % change in visits to local parks, national parks, public beaches, marinas, dog parks, plazas, public gardens. |
| `transit_stations` | percent change vs. baseline | `index` | `mean` | Daily % change in visits to public transport hubs (subway, bus, train stations). |
| `workplaces` | percent change vs. baseline | `index` | `mean` | Daily % change in visits to places of work. |
| `residential` | percent change vs. baseline | `index` | `mean` | Daily % change in time spent in places of residence. (Note: this is
length-of-stay, not visit count — different metric than the other five.)
 |

### Additional data columns

- **`location_name`** — Most-specific populated source field — county name for county rows,
metro name for metro rows, ISO sub-region name for state rows, country
name for national rows. `location_id` is canonical.


## Interpretation caveats

Things that may differ from how other sources define a similar measure. If you're combining this dataset with another, read these first.

- **`residential`** — Residential is reported as % change in *length of stay*, not visit
count. The other five categories are visit-based. Don't sum or compare
directly across these two flavours.

- **`parks`** — Parks shows extreme seasonal cyclicity (winter vs. summer is a 200%+
swing in many countries) that reflects normal seasonality, not
pandemic-period behaviour change. Detrend before any pandemic analysis.

- **`workplaces`** — Holidays / weekends are baked into the baseline since the baseline is
a median across weekdays + weekends. Day-of-week cycles in the output
are real but reflect *deviation* from the comparable weekday's baseline.


## Access

- **Availability:** `inactive`
- **Access type:** `csv`
- **License:** cc-by-4.0
- **Tier:** 2

---

*Schema version `0.1` · Last ingested 2026-04-26T12:14:44Z · `source_id: global-mobility` · Manifest section §15.7*