量化地理域偏移以解耦人类流动生成模型的地理空间可迁移性
Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models
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中文总结 AI 辅助
该研究利用美国2265个县的通勤流动数据集，提出地理域偏移概念及两个量化指标，结合回归分析揭示人类流动模型可迁移性受地理差异与模型设计共同影响，为评估改进模型可迁移性提供新框架。
中文摘要 AI 辅助
人类流动是理解城市系统中社会、经济和环境动态的重要代理指标。地理空间可迁移性用于衡量模型在新位置或未见过区域的性能，是比较不同人类流动生成模型的关键维度。然而，很少有研究探讨地理空间可迁移性的内在特征。为此，本研究利用美国2265个县的普查 tract 级别通勤流动大规模基准数据集，系统研究了四种代表性人类流动生成模型的地理空间可迁移性。受机器学习中域适应理论的启发，我们引入地理域偏移来描述源区域与目标区域之间地理特征分布和空间结构的内在差异，这些差异可能共同影响模型可迁移性。此外，我们提出两个指标——互信息（mutual information）和空间偏移（spatial shift）来量化地理域偏移。为检验这些指标与模型可迁移性的关联，我们采用线性混合效应回归分析地理域偏移与可迁移性之间的关联。结果显示，不同区域的迁移性能存在显著的空间异质性和不对称性；信息偏移和空间偏移均具有统计显著且互补的解释力，表明地理空间可迁移性不仅取决于模型设计，还取决于内在地理差异。这些发现为评估和改进人类流动生成模型的地理空间可迁移性提供了新的方法框架，支持在不同区域生成更稳健、公平的人类流动数据，也为地理人工智能（GeoAI）模型开发的空间可迁移性提供了见解。
英文摘要
Human mobility serves as an essential proxy for understanding social, economic, and environmental dynamics in urban systems. Geospatial transferability, which measures a model's capability in a new location or unseen region, is a critical dimension for comparing different human mobility generation models. However, few studies have studied the intrinsic characteristics of geospatial transferability. To this end, this study systematically investigates the geospatial transferability of four representative human mobility generation models using a large-scale benchmark dataset of census tract level commuting flows across 2265 counties in the United States. Inspired by the domain adaptation theory in machine learning, we introduce geographic domain shift to describe the intrinsic differences in geographic feature distributions and spatial structures between source and target regions, which may jointly affect model transferability. Moreover, we propose two metrics, mutual information and spatial shift, to quantify the geographic domain shift. To examine their associations with model transferability, we employ linear mixed-effects regression to analyze the associations between geographic domain shifts and transferability. Our results reveal substantial spatial heterogeneity and asymmetry in transfer performance across regions. Both information shift and spatial shift exhibit statistically significant and complementary explanatory power. This indicates that geospatial transferability depends not only on model design but also on intrinsic geographic differences. These findings provide a novel methodological framework for evaluating and improving the geospatial transferability of human mobility generation models and support more robust and fair human mobility data synthesis across diverse regions. It also offers insights on spatial transferability for GeoAI model development.
发表机构
- University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
