Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security

Weiliang Chen, Qianqian Ren, Yong Liu, Jianguo Sun

TL;DR

This work proposes a novel framework, Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning (EUPAS), to address key challenges in secure, data-driven modeling, providing a stronger foundation for future urban analytics and decision-making applications.

Abstract

Urban region profiling plays a crucial role in forecasting and decision-making in the context of dynamic and noisy urban environments. Existing methods often struggle with issues such as noise, data incompleteness, and security vulnerabilities. This paper proposes a novel framework, Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning (EUPAS), to address these challenges. By combining adversarial contrastive learning with both supervised and self-supervised objectives, EUPAS ensures robust performance across various forecasting tasks such as crime prediction, check-in prediction, and land use classification. To enhance model resilience against adversarial attacks and noisy data, we incorporate several key components, including perturbation augmentation, trickster generator, and deviation copy generator. These innovations effectively improve the robustness of the embeddings, making EUPAS capable of handling the complexities and noise inherent in urban data. Experimental results show that EUPAS significantly outperforms state-of-the-art methods across multiple tasks, achieving improvements in prediction accuracy of up to 10.8%. Notably, our model excels in adversarial attack tests, demonstrating its resilience in real-world, security-sensitive applications. This work makes a substantial contribution to the field of urban analytics by offering a more robust and secure approach to forecasting and profiling urban regions. It addresses key challenges in secure, data-driven modeling, providing a stronger foundation for future urban analytics and decision-making applications.

Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security

TL;DR

This work proposes a novel framework, Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning (EUPAS), to address key challenges in secure, data-driven modeling, providing a stronger foundation for future urban analytics and decision-making applications.

Abstract

Urban region profiling plays a crucial role in forecasting and decision-making in the context of dynamic and noisy urban environments. Existing methods often struggle with issues such as noise, data incompleteness, and security vulnerabilities. This paper proposes a novel framework, Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning (EUPAS), to address these challenges. By combining adversarial contrastive learning with both supervised and self-supervised objectives, EUPAS ensures robust performance across various forecasting tasks such as crime prediction, check-in prediction, and land use classification. To enhance model resilience against adversarial attacks and noisy data, we incorporate several key components, including perturbation augmentation, trickster generator, and deviation copy generator. These innovations effectively improve the robustness of the embeddings, making EUPAS capable of handling the complexities and noise inherent in urban data. Experimental results show that EUPAS significantly outperforms state-of-the-art methods across multiple tasks, achieving improvements in prediction accuracy of up to 10.8%. Notably, our model excels in adversarial attack tests, demonstrating its resilience in real-world, security-sensitive applications. This work makes a substantial contribution to the field of urban analytics by offering a more robust and secure approach to forecasting and profiling urban regions. It addresses key challenges in secure, data-driven modeling, providing a stronger foundation for future urban analytics and decision-making applications.
Paper Structure (30 sections, 31 equations, 8 figures, 5 tables, 3 algorithms)

This paper contains 30 sections, 31 equations, 8 figures, 5 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Region Representation Learning
  4. Graph Neural Network for Region Representation
  5. Graph Contrastive Learning for Region Representation
  6. Preliminaries
  7. Methodology
  8. Framework Overview
  9. Region Representation Learning
  10. Perturbation Augmentation for Robustness and Security
  11. Attentive Supervised Module
  12. Adversarial Contrastive Module
  13. Adversarial Perturbation Layer
  14. Contrastive Optimization for Robust Augmentation
  15. Joint Optimization
  16. ...and 15 more sections

Figures (8)

  • Figure 1: Difficulties in Data Augmentation for Urban Data: (a) Distribution of a POI and its neighboring locations; (b) Replacing a POI (e.g., a café) with a nearby location (e.g., a restaurant or shopping center) fundamentally changes the semantics and purpose of the trip, distorting the model’s learning process.
  • Figure 2: The architecture of EUPAS integrates four key components: Region Representation Learning, Perturbation Augmentation, Attentive Supervised Module, and Adversarial Contrastive Learning.
  • Figure 3: Workflow diagram for the deviation copy and trickster generators.
  • Figure 4: Comparison of Manhattan districts and region clusters obtained under different baseline methods.
  • Figure 5: Land usage classification performance.
  • ...and 3 more figures

Theorems & Definitions (3)

  • Definition 1: Human Mobility
  • Definition 2: POI Information
  • Definition 3: Geographic Neighbors