Fusing remote sensing with sparse demographic data for synthetic population generation: an algorithm and application to rural Afghanistan

Abstract

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.

Keywords:

• population synthesis
• data fusion
• data uncertainty
• remote sensing
• multiagent modeling
• Afghanistan

Acknowledgments

We thank three anonymous reviewers for constructive comments and suggestions, and Hector Maletta for valuable survey data on Afghan agriculture. The Office of Naval Research (ONR) grant N00014-08-1-0378 partially supported this research. Views expressed herein are ours, not of George Mason University or the ONR.