用半监督方法将难民数据细化到0.5度网格，提升定位精度。
Gridding Forced Displacement using Semi-Supervised Learning
- 融合联合国与卫星数据，通过标签传播算法实现高精度定位
- 在25国范围内对超1000万难民样本定位，平均准确率达92.9%
- 适合研究区域冲突、人口流动的学者及政策制定者使用
我们提出一种半监督方法，将25个撒哈拉以南非洲国家的难民统计数据从行政边界细化至0.5度网格单元。通过整合联合国难民署（UNHCR）的ProGres登记数据、谷歌开放建筑轮廓（Google Open Buildings）的卫星建筑信息以及开放街图人口地点（OpenStreetMap Populated Places）的位置坐标，采用标签传播算法，在高空间粒度上生成具空间明确性的难民分布数据。该方法在超过1000万难民观测值的定位中实现了92.9%的平均准确率，使此前在区域和国家层面被掩盖的局部位移模式得以识别。生成的高分辨率数据集为深入理解位移驱动因素提供了基础支持。
原文摘要 · Abstract (English)
We present a semi-supervised approach that disaggregates refugee statistics from administrative boundaries to 0.5-degree grid cells across 25 Sub-Saharan African countries. By integrating UNHCR's ProGres registration data with satellite-derived building footprints from Google Open Buildings and location coordinates from OpenStreetMap Populated Places, our label spreading algorithm creates spatially explicit refugee statistics at high granularity.This methodology achieves 92.9% average accuracy in placing over 10 million refugee observations into appropriate grid cells, enabling the identification of localized displacement patterns previously obscured in broader regional and national statistics. The resulting high-resolution dataset provides a foundation for a deeper understanding of displacement drivers.
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