Geospatial Analysis of Displacement in Afghanistan
Given increasing levels of displacement due to conflict and climate change, it is important to establish robust monitoring systems. This paper explores how remote sensing data, particularly geospatial data, can be leveraged to monitor displacement flows. It draws lessons from northeastern Afghanistan, namely the 2018 drought, which is considered one of the worst in decades. The analysis identifies displacement patterns by combining displacement data from the International Organization for Migration Displacement Tracking Matrix with nighttime lights. The results suggest that the cumulated displacement movements from 2018 to 2020 can be proxied by trends in nighttime light imagery. Settlements with higher net inflows of displaced persons between 2018 and 2020 have comparatively larger nighttime light growth. Allowing for nonlinearity suggests decreasing marginal returns of displacement on nighttime lights, as settlements showing the largest expansion of nighttime lights are those with the lowest displacement inflows. The model uses data on nighttime lights to predict whether a settlement was a net receiver of displacement flows during 2018–20 and correctly classifies 63.2 percent of the settlements as net inflow or net outflow. This study provides a proof of concept to test whether population displacements can be proxied using geospatial data trained on administrative records in a data-scarce environment, where real-time insights can inform humanitarian assistance. This work was done before the political crisis of August 2021.
| Main Authors: | , , , |
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| Format: | Working Paper biblioteca |
| Language: | English English |
| Published: |
World Bank, Washington, DC
2023-10-09
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| Subjects: | MIGRATION, DISPLACEMENT, GEOSPATIAL ANALYSIS, ECONOMETRIC REGRESSIONS, NIGHTTIME LIGHT, |
| Online Access: | http://documents.worldbank.org/curated/en/099807010042337170/IDU03c1ca6ba06ffa0476108e1f0709f7beb61e2 https://openknowledge.worldbank.org/handle/10986/40439 |
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