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EPI-Eval/global-mobility

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… See the full description on the dataset page: https://huggingface.co/datasets/EPI-Eval/global-mobility.

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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

ColumnUnitvalue_typeAggregationDescription
retail_and_recreationpercent change vs. baselineindexmeanDaily % 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