Xin Lu et al. established a methodological framework in studies of human migration and climate change by using MPD. The study showed that analyses based on mobile network data can describe important short-term features (hours-weeks) of human mobility during and after extreme weather events, which are extremely hard to quantify using standard survey-based research. The study concurrently quantified the incidence, direction, duration and seasonality of migration episodes in Bangladesh (Lu, et al., 2016).
In 2012, Blumenstock described how mobile phones can provide a new source of data on internal migration (Blumenstock J. E., 2012). The analysis revealed more subtle patterns that were not detected in a government survey on migration. Blumenstock (2012) also provided a new quantitative perspective on certain patterns of internal migration in Rwanda that are unobservable using standard survey techniques and explored ways on how new forms of information and communication technology can be used to better understand the essence of migration in developing countries.
The studies conducted in Estonia (Ahas R. S., 2016) describe Tracking Transnationalismwith MPD. The study aims to analyse transnationalism originating from Estonia and to discuss the reasons and geography of the phenomenon. In terms of methodology, the stated aim was to develop a method for measuring transnationalism with the help of roaming data from mobile operators (Ahas, Silm, & Tiru, 2016/2017). Another study ("Mapping changes of residence with passive mobile positioning data: the case of Estonia") by Kamenjuk, Aasa, & Sellin (2017) presented a framework for mapping changes of residence using data from passive mobile positioning and an anchor point model to better understand the limits of these methods and their contribution to understanding long-term mobility. The study concludes that the most important considerations in monitoring change of residence using passive MPD include the continuity of the time-series data, the varying structure of the mobile tower network and the diversified nature of human mobility.
By means of a business-to-government data sharing initiative with MNOs in Europe to help fight COVID-19, the European Commission carried out research on highlighting commuting patterns of short-term transregional mobility through the concept of Mobility Functional Areas (MFA, Iacus et al 2020). Silm & Ahas in 2010 studied the seasonal variability of the population in Estonia and develop a methodology for the monitoring of the short-term mobility of the population with mobile-positioning data. The timing and geography of the seasonal migration patterns studied showed the different directions and causes of seasonal moves. The methodology developed for the monitoring of short-term migration is suitable for the monitoring of movements over more extensive territory (Silm & Ahas, 2010).
Practitioners' Guide on Harnessing Data Innovation for Migration Policydiscusses the methodological approaches and considerations for producing migration indicators based on MPD. To assess the migrations, some requirements have to be fulfilled:
- time period: reliability of data directly depends on the length of the time period;
- identification of home and other meaningful locations (work, school, etc.);
- change of important locations;
- quality of the raw data.
The use of MPD for producing migration statistics following the above approaches were explored in quite a number of countries. There have been several successful studies on the identification of important and meaningful locations using MPD (Ahas et al., 2009, Ahas et al., 2010, Bianchi et al., 2016, Mamei et al., 2016, Jiang et al., 2017).
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