FAP-CD: Fairness-Driven Age-Friendly Community Planning via Conditional Diffusion Generation

TL;DR

FAP-CD introduces a diffusion-based framework for equitable urban planning that optimizes age-friendly facility distribution by learning spatial relationships and demand fairness, significantly improving service equity.

Contribution

The paper presents a novel diffusion model for age-friendly community planning that incorporates demand fairness and spatial accessibility, advancing urban renewal strategies.

Findings

Achieves 41% average improvement over baselines.

Effectively balances age-friendly needs with regional equity.

Utilizes a demand-fairness pre-training and graph denoising techniques.

Abstract

As global populations age rapidly, incorporating age-specific considerations into urban planning has become essential to addressing the urgent demand for age-friendly built environments and ensuring sustainable urban development. However, current practices often overlook these considerations, resulting in inadequate and unevenly distributed elderly services in cities. There is a pressing need for equitable and optimized urban renewal strategies to support effective age-friendly planning. To address this challenge, we propose a novel framework, Fairness-driven Age-friendly community Planning via Conditional Diffusion generation (FAP-CD). FAP-CD leverages a conditioned graph denoising diffusion probabilistic model to learn the joint probability distribution of aging facilities and their spatial relationships at a fine-grained regional level. Our framework generates optimized facility distributions by iteratively refining noisy graphs, conditioned on the needs of the elderly during the diffusion process. Key innovations include a demand-fairness pre-training module that integrates community demand features and facility characteristics using an attention mechanism and min-max optimization, ensuring equitable service distribution across regions. Additionally, a discrete graph structure captures walkable accessibility within regional road networks, guiding model sampling. To enhance information integration, we design a graph denoising network with an attribute augmentation module and a hybrid graph message aggregation module, combining local and global node and edge information. Empirical results across multiple metrics demonstrate the effectiveness of FAP-CD in balancing age-friendly needs with regional equity, achieving an average improvement of 41% over competitive baseline models.