MNBD also affords the possibility for understanding the socio-economic status of regions across the country in near real-time.
(2012) showed correlations between human mobility variables derived from MNBD, and Socio Economic Levels (SELs) from data from the National Statistical Agency in an unnamed country.
Another study by GUTIERREZ, KRINGS & BLONDEL (2013) used MNBD from Cote d'Ivoire (both airtime purchase records as well as communication patterns) to estimate SELs and the diversity and variation in income levels.
A CGAP study (KUMAR & MUHOTA, 2012) hypothesized that MNBD (and specifically the consumption variables) could be leveraged to facilitate financial inclusion by providing new measures of creditworthiness for the unbanked.
Mobility data from MNBD can show population displacements after a disaster.
MNBD can provide new proxy measures of such ties, both at a micro as well as a macro level.
Researchers (mainly from known institutions in developed countries, with some exceptions such as LIRNEas/a) (3) have recently succeeded in obtaining MNBD but at considerable expenditure of time (in building and leveraging the relationships with operators).
In addition to MNBD from Milan and the Autonomous Province of Trento, they also curated and provided additional datasets (weather, electricity, public and private transport, social network data, etc.) for the same regions (5).
MNBD is not amenable to come under the gambit of open data initiatives that have been gaining popularity.
For example researchers in Sri Lanka working with MNBD, found strange mobility patterns in some of the data.
Even when leveraging MNBD for development, surveys and supplemental datasets will remain important to sharpen the analyses and especially to verify the underlying assumptions.
Spurred by the exponential growth of mobile connectivity, the attendant large volumes of MNBD, offer the possibility to obtain rich behavioral insights at a scale that was never possible before.