Xiong Meicheng, Zhu Di, Van Riper David
Department of Geography, Environment and Society, University of Minnesota, Twin Cities, Minneapolis, USA.
Minnesota Population Center, University of Minnesota, Twin Cities, Minneapolis, USA.
Sci Data. 2025 Jul 1;12(1):1106. doi: 10.1038/s41597-025-05410-0.
Census data, as a traditional data source of resident socio-demographics, provides valuable information for decision-makers, researchers, and the public. While numerous efforts have been made to develop more comprehensive data products based on census datasets, most approaches treat census units as static and independent entities, overlooking their interactions. In this paper, we introduce the "visitor census" dataset, a semantically enriched census that incorporates human visitations extracted from large-scale mobile positioning data. We identified and validated the potential home locations of 3.58 million anonymous mobile phone users across seven U.S. metropolitan statistical areas in July 2021 and utilized home detection results to enrich the socio-demographic profile of the places users visited. The proposed data generation framework is adaptive, allowing future integration of diverse socio-demographic features at varying spatial and temporal scales. Overall, this visitor-based census represents an effort to enrich resident-based census knowledge by incorporating mobilities and spatial interactions in human digital traces, bridging the gap between aggregated and individual analysis, as well as between conventional census and mobile phone data.
人口普查数据作为居民社会人口统计学的传统数据来源，为决策者、研究人员和公众提供了有价值的信息。尽管人们已经做出了许多努力来基于人口普查数据集开发更全面的数据产品，但大多数方法都将人口普查单位视为静态和独立的实体，而忽略了它们之间的相互作用。在本文中，我们介绍了“访客普查”数据集，这是一种语义丰富的人口普查，它纳入了从大规模移动定位数据中提取的人类访问信息。我们识别并验证了2021年7月美国七个大都市统计区中358万匿名手机用户的潜在家庭住址，并利用家庭检测结果丰富用户访问地点的社会人口概况。所提出的数据生成框架具有适应性，允许在不同的空间和时间尺度上未来整合各种社会人口特征。总体而言，这种基于访客的人口普查旨在通过将人类数字轨迹中的移动性和空间相互作用纳入其中，丰富基于居民的人口普查知识，弥合汇总分析与个体分析之间以及传统人口普查与手机数据之间的差距。
