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

Jinlin Li, Xintong Li, Xiao Zhou

TL;DR

The paper tackles the challenge of aging populations by developing FAP-CD, a fairness-driven, diffusion-based framework for age-friendly community planning. It models AFC generation as conditioned graph diffusion on walkable facility graphs, incorporating a fair-demand pre-training module and a discrete graph structure to capture accessibility. Key contributions include the first conditional discrete graph diffusion approach for AFC planning, the integration of a graph denoising network with an augmentation strategy, and extensive Beijing-based experiments showing substantial improvements in efficiency and equity (average improvement around 41% reported in the abstract is superseded here by specific metrics, but the claimed gains are substantial). The work advances practical, scalable urban renewal planning by aligning elderly needs with regional fairness, demonstrated through multi-metric evaluation and visual case studies.

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.

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

TL;DR

The paper tackles the challenge of aging populations by developing FAP-CD, a fairness-driven, diffusion-based framework for age-friendly community planning. It models AFC generation as conditioned graph diffusion on walkable facility graphs, incorporating a fair-demand pre-training module and a discrete graph structure to capture accessibility. Key contributions include the first conditional discrete graph diffusion approach for AFC planning, the integration of a graph denoising network with an augmentation strategy, and extensive Beijing-based experiments showing substantial improvements in efficiency and equity (average improvement around 41% reported in the abstract is superseded here by specific metrics, but the claimed gains are substantial). The work advances practical, scalable urban renewal planning by aligning elderly needs with regional fairness, demonstrated through multi-metric evaluation and visual case studies.

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.

Paper Structure

This paper contains 18 sections, 13 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Problem Statement
  4. Preliminaries on Diffusion Process
  5. Methodology
  6. Fair-demand Module
  7. Discrete Graph Diffusion Model
  8. Conditional Graph Denoising Network
  9. Experiments
  10. Experimental Setup
  11. Experimental Results
  12. Overall Comparison.
  13. Ablation Study.
  14. Hyperparameters.
  15. Visualization of Generated AFC Configurations.
  16. ...and 3 more sections

Figures (8)

  • Figure 1: Local Moran's Index analysis of the distribution of age-related facilities in Beijing, illustrating spatial disparities in resource allocation. Two regions with contrasting resource levels are magnified, with blue-shaded zones indicating 15-minute walkable areas around facilities.
  • Figure 2: Framework overview of the proposed FAP-CD.
  • Figure 3: The architecture of (a) graph denoising network, (b) graph transfomer block, and (c) fair-demand module.
  • Figure 4: Performance Comparison in the Ablation Study.
  • Figure 5: Analysis of Hyperparameters.
  • ...and 3 more figures