Expanding the Economic Value of Night-Time Light Data: Applications and Improvements in the Poverty Measurement

本文围绕贫困测量的新数据源——夜间灯光数据展开深入分析，详细阐述了其在贫困测度中的独特优势、实际应用和局限，并提出针对性优化路径。夜间灯光数据凭借其独特的物理客观性和社会特征性，在贫困地图绘制和政策评估等方面发挥着重要作用，但因技术限制、提取方法缺陷、社会文化干扰等因素存在改进空间。通过采取多源数据融合提升数据质量、本土化适配、机器学习驱动优化和耦合多维贫困指标构建综合体系等优化措施，可提升灯光数据的应用价值。This paper presents a deep analysis of night-time light data, a new data source for poverty measurement, detailing its unique advantages, practical applications and limitations in poverty measurement, and suggesting targeted ways to improve it. With its unique physical objectivity and social characteristics, night-time light data plays an important role in poverty mapping and policy assessment, but there is room for improvement due to technical limitations, flawed extraction methods and socio-cultural interference. The application value of lighting data can be enhanced by applying optimisation measures such as merging data from multiple sources to improve data quality, adapting to local conditions, using machine learning methods to optimise models, and linking multidimensional poverty indicators to build a comprehensive system.