Fusing remote sensing with sparse demographic data for synthetic population generation: an algorithm and application to rural Afghanistan |
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Authors: | Seyed M. Mussavi Rizi Maciej M. Łatek Armando Geller |
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Affiliation: | 1. Krasnow Institute for Advanced Study , George Mason University , Fairfax , VA , USA smussavi@gmu.edu;3. Krasnow Institute for Advanced Study , George Mason University , Fairfax , VA , USA;4. Scensei LLC , Alexandria , VA , USA |
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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. |
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Keywords: | population synthesis data fusion data uncertainty remote sensing multiagent modeling Afghanistan |
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