用深度学习融合人口普查与地理数据，提升印度贫困指标的精细预测精度。
Joint Utilization of Geospatial and census proxies for Autoencoder-Assisted Downscaling (JUGAAD) of socioeconomic indicators in India
- 通过聚类和自编码器压缩多源数据，降低噪声并提取关键特征
- 在村级尺度上实现对2011年贫困指标的高精度重建，验证准确率高
- 适合关注发展中国家社会经济监测的政策研究者和数据科学家
监测贫困与粮食安全指标对解决发展中国家的社会经济挑战至关重要。然而，数据源存在尺度不匹配问题：人口普查提供地理覆盖，而社会经济指标来自周期性开展的粗粒度调查，带来方法论挑战。本研究以2001年和2011年印度数据为例，提出深度学习框架JuGAAD。采用三步流程：将人口普查与地理数据平均至中间村组级网格以降噪并规范行政边界变动；利用自编码器将高维国家抽样调查办公室（NSSO）数据压缩为低维潜在表示；再通过回归模型将放大后的普查与地理数据映射至该表示。该函数应用于细粒度人口普查数据，生成高分辨率预测，并与真实县级别NSSO指标进行验证。结果表明，该方法可在细尺度上精准预测社会经济指标。
原文摘要 · Abstract (English)
Monitoring poverty and food security indicators is imperative for addressing socioeconomic challenges in developing nations. A limitation is mismatches in scale between data sources: census data provide geographic coverage, while socioeconomic indicators are derived from infrequently conducted surveys at coarse resolutions, posing a methodological challenge. This study introduces a deep learning framework, JuGAAD, using Indian census and survey data from 2001 and 2011 as a case study. We employ a three-step process: census and geospatial data are averaged into intermediate village-cluster-scale tessellations to reduce noise and regularize administrative boundary changes; an autoencoder compresses high-dimensional National Sample Survey Office (NSSO) data into a low-dimensional latent representation; and a regression model maps upscaled census and geospatial data to this representation. This function is applied to fine-grained census data to generate high-resolution predictions, validated against ground-truth district-level NSSO indicators. Results confirm the methodology predicts socioeconomic indicators at fine scales with strong accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。