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Virtually everybody owns a mobile phone nowadays, most of which are smartphones. Citizens interact with them regularly, making and receiving calls, opening data sessions to check e-mails, searching for information on the internet, participating in social networks, etc. Mobile phone operators store this activity data (both calls and data sessions) for billing purposes, and this information may provide valuable raw material for studies on mobility. Mobile phone activity data provide real-time information on the user’s location at any point in time and on any trips they make. Analysing these data allows monitoring processes over time, such as the mobility of the population during the different phases of the pandemic.
Mobile phone activity data include the user identifier, the (anonymised) phone number that rings up, the number that receives the call (if any), the duration of the call or data session, the mobile communications antenna to which the phone connected and the time at which it connected (day, hour, minute and second). The position of a mobile phone may be inferred by the location of the antenna to which it is connected. However, this information is useful for knowing that the mobile phone is within the coverage area of a specific antenna, yet it does not provide information on the exact coordinates of the device as an error is to be expected depending on the density of antennas. The accuracy in urban areas with a high density of antennas is rather high, i.e. a few hundred metres. However, the error in rural areas may rise to several kilometreskiTU??lometres.
How a user has been moving may de deduced from analysing the position of a mobile phone over time. A mobile phone may sometimes stay in the same place. However, it changes position other times, i.e. connects to a different antenna, what means that the user has changed location. These changes in position show both usual mobility (e.g. moving from home to work) and occasional mobility (e.g. going on holiday). Other variables may also be inferred from mobile phone activity data. For instance, the place of residence of the mobile phone’s user is linked to the location where the device has got more activity at night. These data may be cross-referenced with different data sets to obtain detailed information on the user. For example, cross-referencing the user’s place of residence with maps depicting the income level of the census sections provides information on the socioeconomic status of the user.
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[[File:Logo MonografíaSpain_Fall-in-inter--provincial-mobility.-Downscaling-scheme-–-week-6_2020_map_18255_eng.jpg|left|thumb|300px|Map: Fall in inter-provincial mobility. Downscaling process-scheme – week 6. 2020. Spain. [//centrodedescargas.cnig.es/CentroDescargas/busquedaRedirigida.do?ruta=PUBLICACION_CNIG_DATOS_VARIOS/aneTematico/Spain_Fall-in-inter--provincial-mobility.-Downscaling-scheme-–-week-6_2020_map_18255_eng.pdf PDF]. [//centrodedescargas.cnig.es/CentroDescargas/busquedaRedirigida.do?ruta=PUBLICACION_CNIG_DATOS_VARIOS/aneTematico/Spain_Fall-in-inter--provincial-mobility.-Downscaling-scheme-–-week-6_2020_map_18255_eng.zip Data]. [//interactivo-atlasnacional.ign.es/index.php#c=indicator&i=c_150_t.valor&s=2020-05-07&t=A02&view=map9 Interactive version]. ]]
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