Machine-Learning Geographic Sampling Frames Underrepresent People on the Move in Fragile Settings

Andrea Carlomaria Caflisch, Daniel Masterson, Stephen Daniel O'Connell, Ettan Patel and Julia Smith Omomo
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Daniel Masterson: University of California, Santa Barbara

No zy7ua_v1, SocArXiv from Center for Open Science

Abstract: Political science research often studies difficult-to-locate populations: internally displaced persons (IDPs), returnees, migrants, and others who move during war, disaster, or crisis. We compare machine-learning (ML)-generated building footprints against high-resolution satellite imagery for constructing sampling frames. In displacement- affected communities in Iraq, 15.5% of ML points were invalid or duplicated, and manual additions constituted 19% of the final hybrid frame. Manual additions reached 10.2 percentage points more IDPs in rural communities and 10.1 more returnees in urban areas. We attribute differences to two mechanisms: temporary and informal shelters are harder for ML algorithms to detect, while post-bombardment reconstruction can create structures absent from the imagery underlying ML frames. ML-only frames produce systematic coverage error, and targeted satellite validation can reduce it.

Date: 2026-10-03
New Economics Papers: this item is included in nep-iue and nep-mig
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:zy7ua_v1

DOI: 10.31235/osf.io/zy7ua_v1

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