Italy: Mobility COVID-19

This dataset is part of  COVID-19 Pandemic 
  • CSV OD_Matrix_daily_flows_norm_full_2020_01_18_2020...
    Updated: July 21, 2020

    The file contains the daily fraction of users' moving between Italian provinces. Each line corresponds to an entry of the origin-destination matrix (i,j). The fields of the table are: - p1: COD PROV of origin, - p2: COD PROV of destination, - days in the format yyyy-mm-dd.

  • CSV median_q1_q3_rog_2020_01_18_2020_06_26.csv
    Updated: July 21, 2020

    Median and IQR of users' radius of gyration in a province by week. The fields of the table are: - COD_PROV of the province; - SIGLA of the province; - DEN_PCM of the province; - days in the format yyyy-mm-dd. - median week, Q1 week and Q3 week with week in the format dd/mm-DD/MM where dd/mm} and DD/MM are the first and the last day of the week, respectively.

  • CSV average_network_degree_2020_01_18_2020_06_26.csv
    Updated: July 21, 2020

    Daily time-series of the average degree of the proximity network. Each entry is the value of on a given day. The fields of the table are: - COD_PROV of the province; - SIGLA of the province; - DEN_PCM of the province; - days in the format yyyy-mm-dd.

  • CSV id_provinces_IT.csv
    Updated: July 21, 2020

    Table of the administrative codes of the 107 Italian provinces. The fields of the table are: - COD_PROV is an integer field that is used to identify a province in all other data records; - SIGLA is a two-letters code that identifies the province according to the ISO_3166-2 standard - DEN_PCM is the full name of the province.

Source ISI Foundation / Cuebiq Inc
Contributor
Date of Dataset February 23, 2020 - March 13, 2020
Updated July 21, 2020
Expected Update Frequency As needed
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Underlying mobility data is provided by Cuebiq, a location intelligence and measurement platform. Through its Data for Good program, Cuebiq provides access to aggregated and privacy-safe mobility data for academic research and humanitarian initiatives. This first-party data is collected from anonymized users who have opted-in to provide access to their location data anonymously, through a GDPR-compliant framework. In order to preserve privacy, residential areas are inferred at an aggregated geohash level, thereby allowing for demographic analysis while obfuscating the true home location of anonymous users and prohibiting misuse of data.

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