近期，基础模型已被应用于城市社会经济预测任务，所用数据包括兴趣点（POI）文本、卫星影像和地理空间描述。然而，这些模型主要依赖于单个地点的静态属性，而忽略了揭示地点间功能关联性的移动模式。为弥补这一空白，我们探索了移动网络是否可通过显式编码城市实体间的连通性，从而激发基础模型的地理空间能力。为此，我们提出 \textit{MobFusion}——一种模块化的、以移动性增强的基础模型融合范式，并通过三种互补设计予以实例化：（i）将移动网络作为零样本大语言模型（LLM）提示的上下文；（ii）将移动网络作为图连接器，融合地理空间视觉嵌入与文本嵌入；（iii）将移动网络作为结构化标记，支持多模态大语言模型推理。基于来自美国三个大都市区的匿名大规模移动数据集，我们在三项实例化方案中均观察到 \textit{MobFusion} 在多项城市预测任务（如家庭中位收入、人口密度及犯罪率预测）上的性能提升，表明融入人类移动性可有效增强基础模型对社会经济现象的理解能力。

Foundation models have recently been applied to urban socioeconomic prediction using POI text, satellite imagery, and geospatial descriptions. However, these models mostly rely on static attributes of individual places, while ignoring the mobility patterns that reveal how places are functionally connected. To address this gap, we explore whether mobility networks can elicit the geospatial capabilities of foundation models by explicitly encoding connectivity among urban entities. We propose \textit{MobFusion}, a modular mobility-enhanced foundation model fusion paradigm, and instantiate it through three complementary designs: (i) mobility networks as contexts for zero-shot LLM prompting, (ii) as graph connectors for fusing geospatial visual embeddings with textual embeddings, and (iii) as structured tokens for multimodal LLM reasoning. Using anonymized large-scale mobility datasets from three U.S. metropolitan areas, we find that \textit{MobFusion} improves urban prediction tasks (e.g., median household income, population density, and crime prediction) across three instantiations, demonstrating that incorporating human mobility can effectively improve the socioeconomic understanding of foundation models.