wealth_index_of_towns_and_counties

High-precision and wide coverage data on rural household wealth is essential for bridging national-level rural revitalization policies with micro-level rural entities, enabling the precise allocation of public resources. However, the vast number and dispersed distribution of rural communities in China make wealth data difficult to collect and scarce in availability. To address this challenge, this study proposes an integrated technical framework that combines "sky" remote sensing imagery with "ground" village street view imagery to construct a fine-grained, computable representation of rural household wealth. Centered on the intelligent interpretation of rural housing features, we extract wealth-related visual elements from imagery and regress them against benchmark survey-based household wealth indicators to develop a high-accuracy township-level wealth prediction model (R² = 71%). This model is used to generate a nationwide, township-scale rural household wealth map. Our findings reveal a distinct “bimodal” spatial distribution of rural wealth in China, characterized by a polarization pattern: higher in the south and east, and lower in the north and west. This approach offers a scalable and cost-effective alternative to traditional household surveys, supporting the identification of rural development gaps and facilitating more targeted and effective rural policy implementation.

高精度、广覆盖的农村家庭财富数据，是衔接国家级乡村振兴政策与微观农村主体、实现公共资源精准配置的关键支撑。然而，中国农村社区数量庞大且分布分散，导致财富数据采集难度极高、可用资源极为匮乏。针对这一难题，本研究提出了一套融合“天基”遥感影像与“地基”村落街景影像的集成技术框架，以构建细粒度、可计算的农村家庭财富表征体系。本研究以农村住房特征的智能解译为核心，从影像中提取与财富相关的视觉元素，并将其与基于基准调查的家庭财富指标进行回归分析，从而构建出高精度的乡镇级财富预测模型（R²=71%）。依托该模型，本研究生成了覆盖全国的乡镇尺度农村家庭财富分布图。研究结果显示，中国农村财富呈现显著的“双峰”空间分布特征，且存在明显的极化格局：南部与东部地区财富水平较高，北部与西部地区则相对较低。相较于传统家庭调查，该方法具备可规模化、低成本的优势，可为识别农村发展差距、推动更具针对性与实效性的乡村政策落地提供有力支撑。