贫困制图对于监测联合国2030年可持续发展议程中的可持续发展目标1（SDG 1）——即在全球各地消除一切形式的贫困——日益重要。然而，及时且高空间分辨率的贫困估计仍面临困难，因为传统的基于人口普查和调查的方法成本高昂、实施频次低，且在贫困最严重的地区往往数据最为稀疏。由于贫困源于由人类流动性、社会互动、基础设施和经济活动共同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计与制图带来了新机遇。这些方法处于统计物理、复杂系统科学与数据科学的交叉领域，能够实现更高时空分辨率的贫困估计。本文综述了贫困的基本概念及主要测度框架，并考察了利用卫星影像、手机数据、社交媒体数据以及多源数据融合进行贫困估计与制图的最新进展。本文还探讨了代表性、跨区域可迁移性、可解释性及不确定性量化等长期存在的挑战。最后，本文厘清了当代贫困制图在分析潜力与实际应用限制两方面的边界。

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.