Name: GOOGLE MOBILITY DATA
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

GOOGLE MOBILITY DATA

Context

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

These Community Mobility Reports aim to provide insights into what has changed in response to policies aimed at combating COVID-19. The reports chart 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. (https://www.google.com/covid19/mobility/)

Content

The data contains aggregated and anonymised aggregated data per day for each country. For say accessing data for India - the files 2020_IN_Region_Mobility_Report.csv for 2020 data and 2021_IN_Region_Mobility_Report.csv. The aggregated data is not only present at country level, but also at States and district level - as given in sub_region_1 and sub_region_2.

Acknowledgements

This data from report published by Google. https://www.google.com/covid19/mobility/

Inspiration

Some Questions to answer

India is having its Second Wave and one of the major causes is considered to the election rallies held in different parts of the country. How does Mobility Impact the COVID Cases?
Comparing Mobility across different Countries

Tables

N 2022 Vn Region Mobility Report

@kaggle.aiswaryaramachandran_google_mobility_data.n_2022_vn_region_mobility_report

110.37 kB
18,432 rows
15 columns

CREATE TABLE n_2022_vn_region_mobility_report (
  "country_region_code" VARCHAR,
  "country_region" VARCHAR,
  "sub_region_1" VARCHAR,
  "sub_region_2" VARCHAR,
  "metro_area" VARCHAR,
  "iso_3166_2_code" VARCHAR,
  "census_fips_code" VARCHAR,
  "place_id" VARCHAR,
  "date" TIMESTAMP,
  "retail_and_recreation_percent_change_from_baseline" DOUBLE,
  "grocery_and_pharmacy_percent_change_from_baseline" DOUBLE,
  "parks_percent_change_from_baseline" DOUBLE,
  "transit_stations_percent_change_from_baseline" DOUBLE,
  "workplaces_percent_change_from_baseline" VARCHAR,
  "residential_percent_change_from_baseline" DOUBLE
);

N 2022 Ye Region Mobility Report

@kaggle.aiswaryaramachandran_google_mobility_data.n_2022_ye_region_mobility_report

18.16 kB
288 rows
15 columns

CREATE TABLE n_2022_ye_region_mobility_report (
  "country_region_code" VARCHAR,
  "country_region" VARCHAR,
  "sub_region_1" VARCHAR,
  "sub_region_2" VARCHAR,
  "metro_area" VARCHAR,
  "iso_3166_2_code" VARCHAR,
  "census_fips_code" VARCHAR,
  "place_id" VARCHAR,
  "date" TIMESTAMP,
  "retail_and_recreation_percent_change_from_baseline" BIGINT,
  "grocery_and_pharmacy_percent_change_from_baseline" BIGINT,
  "parks_percent_change_from_baseline" BIGINT,
  "transit_stations_percent_change_from_baseline" BIGINT,
  "workplaces_percent_change_from_baseline" BIGINT,
  "residential_percent_change_from_baseline" BIGINT
);

N 2022 Za Region Mobility Report

@kaggle.aiswaryaramachandran_google_mobility_data.n_2022_za_region_mobility_report

35.35 kB
2,880 rows
15 columns

CREATE TABLE n_2022_za_region_mobility_report (
  "country_region_code" VARCHAR,
  "country_region" VARCHAR,
  "sub_region_1" VARCHAR,
  "sub_region_2" VARCHAR,
  "metro_area" VARCHAR,
  "iso_3166_2_code" VARCHAR,
  "census_fips_code" VARCHAR,
  "place_id" VARCHAR,
  "date" TIMESTAMP,
  "retail_and_recreation_percent_change_from_baseline" BIGINT,
  "grocery_and_pharmacy_percent_change_from_baseline" BIGINT,
  "parks_percent_change_from_baseline" BIGINT,
  "transit_stations_percent_change_from_baseline" DOUBLE,
  "workplaces_percent_change_from_baseline" BIGINT,
  "residential_percent_change_from_baseline" BIGINT
);

N 2022 Zm Region Mobility Report

@kaggle.aiswaryaramachandran_google_mobility_data.n_2022_zm_region_mobility_report

20.16 kB
576 rows
15 columns

CREATE TABLE n_2022_zm_region_mobility_report (
  "country_region_code" VARCHAR,
  "country_region" VARCHAR,
  "sub_region_1" VARCHAR,
  "sub_region_2" VARCHAR,
  "metro_area" VARCHAR,
  "iso_3166_2_code" VARCHAR,
  "census_fips_code" VARCHAR,
  "place_id" VARCHAR,
  "date" TIMESTAMP,
  "retail_and_recreation_percent_change_from_baseline" BIGINT,
  "grocery_and_pharmacy_percent_change_from_baseline" BIGINT,
  "parks_percent_change_from_baseline" DOUBLE,
  "transit_stations_percent_change_from_baseline" BIGINT,
  "workplaces_percent_change_from_baseline" BIGINT,
  "residential_percent_change_from_baseline" BIGINT
);

N 2022 Zw Region Mobility Report

@kaggle.aiswaryaramachandran_google_mobility_data.n_2022_zw_region_mobility_report

34.04 kB
4,546 rows
15 columns

CREATE TABLE n_2022_zw_region_mobility_report (
  "country_region_code" VARCHAR,
  "country_region" VARCHAR,
  "sub_region_1" VARCHAR,
  "sub_region_2" VARCHAR,
  "metro_area" VARCHAR,
  "iso_3166_2_code" VARCHAR,
  "census_fips_code" VARCHAR,
  "place_id" VARCHAR,
  "date" TIMESTAMP,
  "retail_and_recreation_percent_change_from_baseline" DOUBLE,
  "grocery_and_pharmacy_percent_change_from_baseline" DOUBLE,
  "parks_percent_change_from_baseline" DOUBLE,
  "transit_stations_percent_change_from_baseline" DOUBLE,
  "workplaces_percent_change_from_baseline" BIGINT,
  "residential_percent_change_from_baseline" DOUBLE
);