Urban Region Profiling via A Multi-Graph Representation Learning Framework

TL;DR

This paper introduces Region2Vec, a multi-graph learning framework that integrates various regional data sources to improve urban region profiling, especially in socioeconomically diverse areas.

Contribution

The study develops an innovative multi-graph fusion module and incorporates diverse data sources to enhance urban region representations.

Findings

Region2Vec outperforms state-of-the-art methods in real-world datasets.

It improves profiling accuracy in regions with high socioeconomic variance.

The framework effectively integrates mobility, geographic, and POI data.

Abstract

Urban region profiling can benefit urban analytics. Although existing studies have made great efforts to learn urban region representation from multi-source urban data, there are still three limitations: (1) Most related methods focused merely on global-level inter-region relations while overlooking local-level geographical contextual signals and intra-region information; (2) Most previous works failed to develop an effective yet integrated fusion module which can deeply fuse multi-graph correlations; (3) State-of-the-art methods do not perform well in regions with high variance socioeconomic attributes. To address these challenges, we propose a multi-graph representative learning framework, called Region2Vec, for urban region profiling. Specifically, except that human mobility is encoded for inter-region relations, geographic neighborhood is introduced for capturing geographical contextual information while POI side information is adopted for representing intra-region information by knowledge graph. Then, graphs are used to capture accessibility, vicinity, and functionality correlations among regions. To consider the discriminative properties of multiple graphs, an encoder-decoder multi-graph fusion module is further proposed to jointly learn comprehensive representations. Experiments on real-world datasets show that Region2Vec can be employed in three applications and outperforms all state-of-the-art baselines. Particularly, Region2Vec has better performance than previous studies in regions with high variance socioeconomic attributes.