利用时空加性高斯过程模型填补粮食安全监测中的调查缺口
Filling survey gaps in food security monitoring with spatio-temporal additive Gaussian process models
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中文总结 AI 辅助
本文提出时空加性高斯过程模型，利用其克罗内克结构实现可扩展推理，在尼日利亚、乍得数据上验证精度更优，可填补粮食安全监测的地理与时空缺口。
中文摘要 AI 辅助
保障一个国家所有地区的粮食安全需要持续监测，但受资源限制和运营优先级影响，家庭调查往往会留下显著的时空缺口。本文提出一种时空加性高斯过程模型，用于分地区估算国家级以下层面的粮食安全时间序列。为解决高斯过程模型的计算成本问题，我们利用时空协方差矩阵的克罗内克结构实现可扩展推理。我们在尼日利亚和乍得的粮食安全调查数据上评估该方法，将其与其他统计和机器学习模型对比，结果显示所提方案在保留可靠不确定性的同时实现了更高精度，尤其是在协变量具有信息价值时。我们进一步应用该模型生成调查未覆盖的尼日利亚各州的估算值，证明其在填补粮食安全监测地理缺口方面的应用价值。
英文摘要
Ensuring food security across all regions of a country requires continuous monitoring, yet household surveys often leave significant spatio-temporal gaps due to resource constraints and operational priorities. In this paper, we propose a spatio-temporal additive Gaussian process model to estimate sub-national food security time series by regions. To address the computational cost of Gaussian process models, we exploit Kronecker structure of the spatio-temporal covariance matrix for scalable inference. We evaluate the proposed approach on food security survey data from Nigeria and Chad comparing it against other statistical and machine learning models and show how our proposal achieves better accuracy while retaining reliable uncertainty, especially when covariates are informative. We further apply the model to generate estimates for Nigerian states not covered by the survey, demonstrating its operational value for filling geographic gaps in food security monitoring.
