Last year, I was in Dili, Timor Leste, listening to an event on big data that was partially sponsored by SciDev, a respected science communication organization. My recollection is that the speakers were talking about work done by others based on reports. So we were happy to have our research featured in an article in SciDev. The author, Nalaka Gunawardene, attended our presentation at the Sri Lanka Institute of Engineers in January and made further efforts to understand what we were doing.
MNBD allows tracking and mapping of daily changes in population densities relative to midnight (‘home location’). Daytime or ‘work’ locations may be identified, along with where people came from.
One insight: the northern part of Colombo, where the poor are concentrated, shows a lower density at mid-day on weekdays relative to midnight. This is due to large numbers crossing to the southern part to provide labour. Similarly, various types of land use can be discerned by analysing the daily loading patterns of base stations. This, in turn, enables a closer alignment of urban plans and actual land use. 
City planners like to know how people actually use specific urban environments. Samarajiva says mobile use patterns — interpreted with other local knowledge — can be leveraged to determine land use patterns. When people are found staying at a location for a significant time, adequate resources and services can be assigned to that area.
For now, MNBD is the only dataset that can provide comprehensive insights for urban planning and other public purposes that do not marginalise the poor. However, other datasets — such as those from Twitter — may also be used for specific purposes, with care.