Harnessing Rich Multi-Modal Data for Spatial-Temporal Homophily-Embedded Graph Learning Across Domains and Localities

TL;DR

This paper introduces a multi-modal data fusion framework that leverages spatial-temporal homophily in graph learning to improve urban analytics across diverse cities and domains, demonstrating strong predictive performance with minimal reconfiguration.

Contribution

It presents a novel heterogeneous data pipeline that integrates multi-source, multi-modality urban data into a flexible graph learning framework for cross-domain and cross-locality applications.

Findings

Effective data fusion from 50+ sources improves urban prediction tasks.

Framework generalizes well across different cities and domains.

Achieves high predictive accuracy with minimal reconfiguration.

Abstract

Modern cities are increasingly reliant on data-driven insights to support decision making in areas such as transportation, public safety and environmental impact. However, city-level data often exists in heterogeneous formats, collected independently by local agencies with diverse objectives and standards. Despite their numerous, wide-ranging, and uniformly consumable nature, national-level datasets exhibit significant heterogeneity and multi-modality. This research proposes a heterogeneous data pipeline that performs cross-domain data fusion over time-varying, spatial-varying and spatial-varying time-series datasets. We aim to address complex urban problems across multiple domains and localities by harnessing the rich information over 50 data sources. Specifically, our data-learning module integrates homophily from spatial-varying dataset into graph-learning, embedding information of various localities into models. We demonstrate the generalizability and flexibility of the framework through five real-world observations using a variety of publicly accessible datasets (e.g., ride-share, traffic crash, and crime reports) collected from multiple cities. The results show that our proposed framework demonstrates strong predictive performance while requiring minimal reconfiguration when transferred to new localities or domains. This research advances the goal of building data-informed urban systems in a scalable way, addressing one of the most pressing challenges in smart city analytics.