Fine-grained migration data illuminate demographic, environmental, and health phenomena. However, United States migration data have serious drawbacks: public data lack spatial granularity, and higher-resolution proprietary data suffer from multiple biases. To address this, we develop a method that fuses high-resolution proprietary data with coarse Census data to create MIGRATE: annual migration matrices capturing flows between 47.4 billion US Census Block Group pairs -- approximately four thousand times the spatial resolution of current public data. Our estimates are highly correlated with external ground-truth datasets and improve accuracy relative to raw proprietary data. We use MIGRATE to analyze national and local migration patterns. Nationally, we document demographic and temporal variation in homophily, upward mobility, and moving distance -- for example, rising moves into top-income-quartile block groups and racial disparities in upward mobility. Locally, MIGRATE reveals patterns such as wildfire-driven out-migration that are invisible in coarser previous data. We release MIGRATE as a resource for migration researchers.

翻译：精细尺度的人口迁移数据能够揭示人口、环境与健康现象。然而，美国现有迁移数据存在严重缺陷：公开数据缺乏空间粒度，而更高分辨率的专有数据则存在多种偏差。为解决这一问题，我们开发了一种方法，将高分辨率专有数据与粗略的人口普查数据相融合，构建出MIGRATE：该数据集包含年度迁移矩阵，捕捉了474亿对美国人口普查区块组之间的流动——其空间分辨率约为当前公开数据的四千倍。我们的估计结果与外部真实数据集高度相关，并相较于原始专有数据提高了准确性。我们利用MIGRATE分析了全国及地方层面的迁移模式。在全国层面，我们记录了同质性、向上流动性和迁移距离在人口特征与时间维度上的变化——例如，迁入收入最高四分位区块组的流动增加，以及向上流动性中存在的种族差异。在地方层面，MIGRATE揭示了诸如野火驱动的人口外流等以往粗粒度数据无法观测到的模式。我们将MIGRATE作为一项资源公开发布，以供迁移研究学者使用。