AI 中文总结
本综述梳理贫困概念与衡量框架，探讨卫星、手机等数据及多源融合在贫困制图的进展，分析其挑战，阐明当代贫困制图的潜力与局限。
AI 中文摘要
贫困制图对监测联合国2030议程的可持续发展目标1（SDG 1，旨在消除各地一切形式的贫困）愈发重要。然而，及时且高分辨率的贫困估算仍具难度，因为传统基于人口普查和调查的方法成本高、频率低，且在贫困最严重的地区数据往往最稀疏。由于贫困源于由人类流动、社会互动、基础设施和经济活动塑造的复杂社会经济系统，新兴计算方法与非传统数据源为贫困估算和制图创造了新机遇。这些方法处于统计物理学、复杂系统科学与数据科学的交叉领域，支持更精细时空分辨率的贫困估算。本综述总结了贫困的核心概念及衡量贫困的主要框架，探讨了利用卫星影像、手机数据、社交媒体数据及多源数据融合开展贫困估算与制图的最新进展，还讨论了代表性、跨区域可迁移性、可解释性及不确定性量化等持续存在的挑战，最后阐明了当代贫困制图的分析潜力与实际局限。
英文摘要
Poverty mapping is increasingly important for monitoring Sustainable Development Goal 1 (SDG 1) of the United Nations 2030 Agenda, which aims to end poverty in all its forms everywhere. Yet timely and fine-resolution poverty estimation remains difficult because conventional census- and survey-based approaches are costly, infrequent, and often sparse precisely where deprivation is most severe. As poverty emerges from complex socioeconomic systems shaped by human mobility, social interactions, infrastructure, and economic activities, emerging computational methods and nontraditional data sources have created new opportunities for poverty estimation and mapping. At the intersection of statistical physics, complex systems science, and data science, these approaches enable poverty estimation at finer spatial and temporal resolutions. This review summarizes the main concepts of poverty and the principal frameworks used to measure it, and examines recent advances on poverty estimation and mapping using satellite imagery, mobile phone data, social media data, and multisource data fusion. The review also discusses persistent challenges related to representativeness, transferability across regions, interpretability, and uncertainty quantification. Finally, the review clarifies both the analytical promise and the practical limits of contemporary poverty mapping.
