Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study

2025-01-20

AI Summary

Urban predictive models often suffer from spatially uneven residual distributions, leading to imbalanced resource allocation and exacerbating inequities across regions and demographic groups. To address this, we conduct an empirical study on spatiotemporal forecasting—specifically traffic flow—in Chicago. We propose a Residual-Aware Attention (RAA) module and a fairness-enhancing loss function: the RAA dynamically refines the adjacency matrix and explicitly captures spatial heterogeneity; the loss function is the first to incorporate residual spatial clustering—quantified via Moran’s I—into the training objective of spatiotemporal graph neural networks (ST-GNNs). By integrating adaptive graph learning with spatial fairness metrics, our approach maintains high predictive accuracy (only a 9% increase in error) while significantly improving prediction equity across diverse communities. Fairness metrics improve by 48%, effectively mitigating residual concentration in central urban areas.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Urban prediction tasks, such as forecasting traffic flow, temperature, and crime rates, are crucial for efficient urban planning and management. However, existing Spatiotemporal Graph Neural Networks (ST-GNNs) often rely solely on accuracy, overlooking spatial and demographic disparities in their predictions. This oversight can lead to imbalanced resource allocation and exacerbate existing inequities in urban areas. This study introduces a Residual-Aware Attention (RAA) Block and an equality-enhancing loss function to address these disparities. By adapting the adjacency matrix during training and incorporating spatial disparity metrics, our approach aims to reduce local segregation of residuals and errors. We applied our methodology to urban prediction tasks in Chicago, utilizing a travel demand dataset as an example. Our model achieved a 48% significant improvement in fairness metrics with only a 9% increase in error metrics. Spatial analysis of residual distributions revealed that models with RAA Blocks produced more equitable prediction results, particularly by reducing errors clustered in central regions. Attention maps demonstrated the model's ability to dynamically adjust focus, leading to more balanced predictions. Case studies of various community areas in Chicago further illustrated the effectiveness of our approach in addressing spatial and demographic disparities, supporting more balanced and equitable urban planning and policy-making.

Problem

• Urban Prediction Disparities
• Population Variability
• Inequality Amplification

Innovation

• Residual-aware Attention Module
• Fairness-enhanced Loss Function
• Spatial Equity in Urban Prediction