Fusing Remote Sensing with Sparse Demographic Data for Synthetic Population Generation: An Algorithm and Application to Rural Afghanistan

International Journal of Geographical Information ScienceInternational Journal of Geographical Information Science, published online 19 November 2012

Seyed M. Mussavi Rizi, Maciej M. Łatek, and Armando Geller

“We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.”