arXiv AI By Zhiyong Zhou, Song Gao, Qianheng Zhang, Feng Zhang, Zhenhong Du

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

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arXiv Machine Learning
Oct 3

Relative Transitions, Not Absolute Destinations: A Transfer-and-Ground Framework for Target-Trajectory-Free Human Mobility Generation

The paper introduces Nomad, a transfer-and-ground framework for generating human mobility trajectories without target-city trajectory data. It learns relative transitions from source cities using POI attributes and then grounds these transitions onto a target city’s POI map via a behavior graph and exploration–return walk. Experiments across ten cities show Nomad improves trajectory fidelity and downstream utility by roughly 15% and 3% respectively over adaptation baselines.

By Yidi Wang, Yunhe Zhang, Bangchao Deng, Dingqi Yang, Pengyang Wang
arXiv Machine Learning
Sep 2

Do LLMs Know Your Neighborhood? Auditing LLM Priors for Neighborhood-Level Mobility Prediction and Structural Alignment

The study investigates whether large language models (LLMs) can predict neighborhood-level human mobility without training data. Using anonymized Cuebiq data across four U.S. metropolitan areas, the authors compare zero‑shot LLM predictions to supervised baselines for various mobility outcomes and assess structural alignment with empirical trends. Results show supervised models outperform LLMs (average accuracy 0.580 vs. 0.435), with LLMs relying on coarse, stable priors that may exhibit biased treatment of protected-group predictors.

By Saad Mohammad Abrar, Eesha Kurella, Arnav Dadarya, Naman Awasthi, Kazi Tasnim Zinat, Vanessa Frias-Martinez
arXiv Machine Learning
3d ago

PlacePulse: Learning Transferable Place Representations from Structural Geographic Data

PlacePulse is a structural geospatial foundation model that transforms 572 attributes—expanded to 2,288 through H3 neighborhood aggregates—into 256‑dimensional unit‑norm embeddings for over a million H3 resolution‑7 cells across the United States. The model achieves high intrinsic reconstruction quality (mean $R^2 = 0.853$) and strong spatial organization (Moran's $I = 0.93$), while outperforming a dimensionality‑matched baseline on 74 held‑out targets from 12 external sources and competing with Google’s Population Dynamics Foundation Model on various health, economic, election, mobility, and social‑vulnerability outcomes. These results demonstrate that static structural composition can serve as a distinct and complementary modality for geospatial foundation models.

By Luc\'ia Garc\'ia-Duarte, Miguel \'Alvarez, Diego Vicente, Javier P\'erez Trufero, Javier de la Torre
arXiv Machine Learning
Sep 22

LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling

LE4Mob is a new location embedding framework that learns inductive, distance‑aware representations from geographic context, enabling it to encode unseen locations and preserve spatial relationships. It builds on contrastive language‑location pre‑training and adds a regularisation objective that encourages the embedding space to reflect geographic distance. Experiments on next‑location prediction and commuter flow generation across multiple datasets show that LE4Mob outperforms strong baselines, especially in inductive settings and when downstream models use direct interactions between location embeddings.

By Xinglei Wang, Stephen Law, Zichao Zeng, Junyuan Liu, Guangsheng Dong, Tao Cheng