Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement

TL;DR

This paper introduces ME-POIs, a novel framework that combines language-based POI embeddings with human mobility data to learn more accurate, usage-based representations of locations, improving map enrichment tasks.

Contribution

The paper presents a new method for learning POI representations by integrating mobility data with language models, capturing POI function and usage patterns beyond static metadata.

Findings

ME-POIs outperform text-only models on map enrichment tasks.

Mobility-only trained ME-POIs can surpass text-only models in certain scenarios.

The approach effectively captures POI usage patterns across users and time.

Abstract

Recent progress in geospatial foundation models highlights the importance of learning general-purpose representations for real-world locations, particularly points-of-interest (POIs) where human activity concentrates. Existing approaches, however, focus primarily on place identity derived from static textual metadata, or learn representations tied to trajectory context, which capture movement regularities rather than how places are actually used (i.e., POI's function). We argue that POI function is a missing but essential signal for general POI representations. We introduce Mobility-Embedded POIs (ME-POIs), a framework that augments POI embeddings derived, from language models with large-scale human mobility data to learn POI-centric, context-independent representations grounded in real-world usage. ME-POIs encodes individual visits as temporally contextualized embeddings and aligns them with learnable POI representations via contrastive learning to capture usage patterns across users and time. To address long-tail sparsity, we propose a novel mechanism that propagates temporal visit patterns from nearby, frequently visited POIs across multiple spatial scales. We evaluate ME-POIs on five newly proposed map enrichment tasks, testing its ability to capture both the identity and function of POIs. Across all tasks, augmenting text-based embeddings with ME-POIs consistently outperforms both text-only and mobility-only baselines. Notably, ME-POIs trained on mobility data alone can surpass text-only models on certain tasks, highlighting that POI function is a critical component of accurate and generalizable POI representations.