强制流离失所情境下社会经济估计的迁移学习
Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings
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中文总结 AI 辅助
本研究将预训练的多模态时空视觉变换器迁移至南苏丹、喀麦隆和赞比亚的流离失所情境，利用卫星数据估计社会经济状况，填补调查间隙，支持人道主义决策。
中文摘要 AI 辅助
包容性住户调查的进展加强了针对被迫流离失所人口的社会经济证据，为生活条件和福利提供了不可或缺的基准。然而，这些调查仍然资源密集且具有周期性，而各轮调查之间条件可能发生变化，特别是在受脆弱性、冲突和暴力影响的地区。因此，需要更频繁更新、空间粒度更细的补充证据，以识别各轮调查之间社会经济状况可能发生变化的地区，并为业务优先排序提供信息。地球观测和机器学习提供了一种可扩展的空间明确社会经济信息来源。然而，为一般人群开发的工具尚未在被迫流离失所环境中得到系统性的适应和评估，在这些环境中，生活条件、定居模式和流离失所影响可能大不相同。我们通过将一种多模态时空视觉变换器（该变换器基于来自36个非洲国家约120万户家庭的人口与健康调查数据进行了预训练）适应到南苏丹、喀麦隆和赞比亚的被迫流离失所和收容社区环境中，来填补这一空白。我们使用源自联合国难民署FDS和RMS数据的社会经济指数来开发和评估更新后的适应模型。我们的结果表明，卫星衍生的地理空间协变量解释了营地交叉网格中社会经济结果变异的66%，平均绝对误差（MAE）为4.37个指数点，在非营地交叉区域解释了41%的变异，MAE为5.41。该框架通过定期更新的基于模型的社会经济估计填补了关键的空间和时间数据空白，补充了周期性住户调查并增加了其价值。这些估计在调查轮次之间维持洞察力，并支持及时的人道主义优先排序和实地核查。
英文摘要
Progress in inclusive household surveys has strengthened socioeconomic evidence for forcibly displaced populations, providing indispensable benchmarks on living conditions and welfare. However, these surveys remain resource-intensive and periodic, while conditions can change between rounds, particularly in settings affected by fragility, conflict, and violence. More frequently updated, spatially granular complementary evidence is therefore needed to identify where socioeconomic conditions may be changing between survey rounds and to inform operational prioritization. Earth observation and machine learning offer a scalable source of spatially explicit socioeconomic information. However, tools developed for general populations have not been systematically adapted and evaluated in forced displacement settings, where living conditions, settlement patterns, and displacement impacts may differ substantially. We address this gap by adapting a multimodal spatiotemporal vision transformer, pretrained on Demographic and Health Survey data from approximately 1.2 million households across 36 African countries, to forced displacement and host community settings in South Sudan, Cameroon, and Zambia. We develop and evaluate the updated, adapted model using socioeconomic indices derived from UNHCR FDS and RMS data. Our results show that satellite-derived geospatial covariates explain up to 66% of the variation in socioeconomic outcomes in camp-intersecting grids, with a mean absolute error (MAE) of 4.37 index points, and 41% in non-camp-intersecting areas, with an MAE of 5.41. The framework complements and adds value to periodic household surveys by filling critical spatial and temporal data gaps with regularly updated, model-based socioeconomic estimates. These estimates sustain insight between survey rounds and support timely humanitarian prioritization and field verification.
发表机构
- The United Nations High Commissioner for Refugees（联合国难民署）
- Chalmers University（查尔姆斯理工大学）
- The World Bank（世界银行）
机构由 AI 辅助整理，请以论文原文为准。
