Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models

Zhiyong Zhou; Song Gao; Qianheng Zhang; Feng Zhang; Zhenhong Du

cs.AIAug 21, 2026

Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models

Authors: Zhiyong Zhou, Song Gao, Qianheng Zhang, Feng Zhang, Zhenhong Du

Organizations: GeoDS Lab, Department of Geography, University of Wisconsin-Madison, Madison, WI, USA · School of Earth Sciences & Zhejiang Key Laboratory of Geographic Information Science, Zhejiang University, Hangzhou, China

Abstract

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.

Figures & tables

Figure 1: a. The county occupation of the used census tract-level commuting OD flow dataset in the United States. The color saturation denotes the log-transformed census tract number. b. The mutual information (MI) shift between input features of the source domain and input features of the target domain. c. The proposed Moran spatial shift between Moran’s I index of the target domain ( $I_{tar}$ ) and that of the source domain $I_{src}$ , i.e., $I_{tar}-I_{src}$ .

Figure 2: The hexagon cube maps of OD flow generation transfer performance, a. CPC and b. RMSE, which were aggregated with hexagon cells across the 2265 counties from 48 states, where the counties of a state construct the source domain for training, and the remaining counties are transferred to. The color saturation of a hexagon cube represents the mean values of CPC or RMSE achieved in target counties within the hexagon area, while the height of the hexagon cube denotes the standard deviation of CPC or RMSE achieved by DeepGravity models trained with different source states in the same hexagon area. Notably, the height in a. is scaled up to 5000 times the CPC std., and the height in b. is scaled up to 10 times the RMSE std. The bidirectional evaluation of state pairs, one of which acts as a source state domain for training and the other one is a target domain for transfer, shows the asymmetry of geospatial transferability regarding c. CPC and d. RMSE. Each row represents the geospatial transferability from a source state to the other states.

Figure 3: a. The joint distribution of mutual information (MI) shift (x-axis) and Moran’s I spatial shift (y-axis). b-d. The MI-Moran’s I shift distribution of different reference states in three counties, where the MI shifts remain at the 25%, 50%, and 75% quantiles in ascending order, respectively, and e-g. The distribution in three counties whose Moran’s I index shifts remain at the 25%, 50%, and 75% quantiles, respectively. The saturation of color denotes the CPC transferability, with darker indicating a higher CPC. The size of dots represents the RMSE transferability, with the larger size meaning a larger RMSE.

Figure 4: The joint associations of MI shift and Moran shift on CPC in (a) and on RMSE in (c) of fitted linear mixed-effects models based on DeepGravity. The derived fixed effects of source (training) states are mapped in (b) and (d) , respectively.

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Mitigating Domain Shift

Open paper AskPDF arXiv

In computer vision and machine learning for geographic data, out-of-domain generalization is a pervasive challenge, arising from uneven global data coverage and distribution shifts across geographic regions. Though models are frequently trained in one region and deployed in another, there is no principled method for determining when this cross-region adaptation will be successful. A well-defined notion of distance between distributions can effectively quantify how different a new target domain is compared to the domains used for model training, which in turn could support model training and deployment decisions. In this paper, we propose a strategy for computing distances between geospatial domains that leverages geographic information with Optimal Transport methods (GeoSpOT). In our experiments, GeoSpOT distances emerge as effective predictors of cross-domain transfer difficulty. We further demonstrate that embeddings from pretrained location encoders provide information comparable to image/text embeddings, despite relying solely on longitude-latitude pairs as input. This allows users to get an approximation of out-of-domain performance for geospatial models, even when the exact downstream task is unknown, or no task-specific data is available. Building on these findings, we show that GeoSpOT distances can preemptively guide data selection and enable predictive tools to analyze regions where a model is likely to underperform.

Abstract

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