# 1. Introduction

An estimated 5.5 million people were internally displaced in Afghanistan in the summer of 2021, according to the International Organization for Migration (IOM, 2021). The 2020 Global Report ranks Afghanistan among the top five countries with high levels of internal displacement due to conflict and violence. It ranks first with the highest number of Internally Displaced Persons (IDPs) due to natural disasters (IDMC, 2020a, 11-12). Important geographic disparities can be observed, with regions such as Herat, Helmand, Badghis, or Kandahar hosting many IDPs (Figure 1). Since 2017, 350,000 – 480,000 people have been displaced annually due to ongoing conflict and violence (UNOCHA, 2020; UNOCHA, 2021). A developing drought that threatens millions with food insecurity worsens a precarious political context. At the same time, fragile contexts like Afghanistan have relatively scarce on-the-ground data due to constraints on data collection.

This paper explores how remote sensing data, particularly geospatial data, can be leveraged to monitor displacement flows in Afghanistan. It seeks to draw lessons from the 2018 drought, which up to that point was considered one of the worst in decades. Between mid-2017 and mid-2018, Afghanistan recorded a deficit of 70 percent precipitation and insufficient snowfall, which constitutes an important water source for irrigation (Oxfam, 2018). The 2018 drought was dubbed the worst drought in two decades and affected 22 of the 34 provinces in the country. About 13.5 million Afghan people faced heightened food insecurity in 2018, with lost livelihoods, livestock deaths, and forced displacement into urban areas (FAO, 2019). An estimated 371,000 people left their homes, including 120,000 in Badghis province or about a quarter of the population. They congregated in the <mark>provincial capital Qala-e-Naw to receive emergency humanitarian assistance.</mark> <mark>To identify patterns of displacement, the team combined displacement data from the IOM Displacement Tracking Matrix (DTM) with Nighttime Lights (NTL) data obtained from NOAA’s VIIRS satellite.</mark><sup>3</sup> <mark>This exploratory study was regionally focused on capturing displacement in and around Badghis. The results suggest</mark> that cumulated displacement movement over 2018-2020 can be proxied by trends in NTL imagery. Settlements with higher net inflows of displaced persons between 2018 and 2020 have larger NTL growth than others, and the results hold with additional robustness checks. Allowing for non-linearity suggests diminishing returns to displacement flows; settlements with the largest level of NTL expansion are those with the lowest level of displacement inflows.

The model uses NTL data to predict whether a settlement was a net receiver of displacement flows in 20182020 and finds promising results. The paper uses a fixed effect model to predict the total inflow of displaced persons and estimate whether a settlement has a positive or negative net inflow, i.e., whether it was an inbound area or served as an outbound area that people fled. We find a significant and concave relationship between the average NTL and the cumulated net inflow of displaced population over 2018-2020. The reverse model correctly classifies 63.2 percent of these settlements, a promising baseline of accuracy that could be improved upon with more training data.

This study provides a proof of concept to test whether population displacements can be proxied by variations in geospatial data. It contributes to the literature on geospatial data and displacement by combining remote sensing data and IOM administrative data in Afghanistan. This country has experienced large-scale displacement and where security and accessibility constraints make ground-truth data sparse. Geospatial data offers a unique opportunity to track the evolution of indicators through time-series satellite images in hard-to-reach areas. With this work, we aim to complement the literature on displacement by providing an example of how to proxy for displacement flows in situations where data are inconsistent and hard to collect. The methodology can be scaled and tailored to the country level to monitor the effects of drought on displacement.

The rest of the paper is organized as follows: section 2 summarizes the literature on geospatial analysis to track displacement. Section 3 outlines the datasets leveraged for the analysis. Section 4 presents the analytical results and robustness checks. Section 5 illustrates how these results can be used to predict displacement levels based on geospatial data alone. Section 6 concludes.

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