Space-aware Socioeconomic Indicator Inference with Heterogeneous Graphs

TL;DR

This paper introduces GeoHG, a novel space-aware method using heterogeneous graphs to infer socioeconomic indicators from limited regional data, outperforming traditional spatial interpolation especially under extreme data scarcity.

Contribution

The paper presents GeoHG, the first heterogeneous graph-based approach for non-continuous socioeconomic inference leveraging geospatial data.

Findings

Achieves an R^2 score over 0.8 with 95% data masking.

Outperforms existing spatial interpolation methods.

Effective in scenarios with severe data scarcity.

Abstract

Regional socioeconomic indicators are critical across various domains, yet their acquisition can be costly. Inferring global socioeconomic indicators from a limited number of regional samples is essential for enhancing management and sustainability in urban areas and human settlements. Current inference methods typically rely on spatial interpolation based on the assumption of spatial continuity, which does not adequately address the complex variations present within regional spaces. In this paper, we present GeoHG, the first space-aware socioeconomic indicator inference method that utilizes a heterogeneous graph-based structure to represent geospace for non-continuous inference. Extensive experiments demonstrate the effectiveness of GeoHG in comparison to existing methods, achieving an $R^2$ score exceeding 0.8 under extreme data scarcity with a masked ratio of 95\%.