  Nighttime lights satellite imagery has been used for decades as a uniform,
global source of data for studying a wide range of socioeconomic factors.
Recently, another more terrestrial source is producing data with similarly
uniform global coverage: anonymous and aggregated smart phone location. This
data, which measures the movement patterns of people and populations rather
than the light they produce, could prove just as valuable in decades to come.
In fact, since human mobility is far more directly related to the socioeconomic
variables being predicted, it has an even greater potential. Additionally,
since cell phone locations can be aggregated in real time while preserving
individual user privacy, it will be possible to conduct studies that would
previously have been impossible because they require data from the present. Of
course, it will take quite some time to establish the new techniques necessary
to apply human mobility data to problems traditionally studied with satellite
imagery and to conceptualize and develop new real time applications. In this
study we demonstrate that it is possible to accelerate this process by
inferring artificial nighttime satellite imagery from human mobility data,
while maintaining a strong differential privacy guarantee. We also show that
these artificial maps can be used to infer socioeconomic variables, often with
greater accuracy than using actual satellite imagery. Along the way, we find
that the relationship between mobility and light emissions is both nonlinear
and varies considerably around the globe. Finally, we show that models based on
human mobility can significantly improve our understanding of society at a
global scale.
