Enhancing Demand-Oriented Regionalization with Agentic AI and Local Heterogeneous Data for Adaptation Planning

2025-11-13

AI Summary

Traditional static planning units—such as census tracts or ZIP code areas—fail to capture fine-grained community heterogeneity, undermining the adaptability and equity of disaster prevention and response. To address this, we propose a human-in-the-loop dynamic zoning framework integrating geographic adaptive filtering, spatially constrained self-organizing maps (RepSC-SOM), and AI agent–driven reasoning for feature recommendation, constraint-aware optimization, and iterative region growth refinement. Grounded in localized heterogeneous data and guided by interactive user input, the framework enables interpretable, controllable, and computationally rigorous dynamic unit generation. Evaluated on flood risk assessment in Jacksonville, Florida, our system significantly improves real-world alignment, decision transparency, and operational flexibility of zoning outcomes. It establishes a novel paradigm for resilient urban planning—one that is explainable, participatory, and scalable.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Conventional planning units or urban regions, such as census tracts, zip codes, or neighborhoods, often do not capture the specific demands of local communities and lack the flexibility to implement effective strategies for hazard prevention or response. To support the creation of dynamic planning units, we introduce a planning support system with agentic AI that enables users to generate demand-oriented regions for disaster planning, integrating the human-in-the-loop principle for transparency and adaptability. The platform is built on a representative initialized spatially constrained self-organizing map (RepSC-SOM), extending traditional SOM with adaptive geographic filtering and region-growing refinement, while AI agents can reason, plan, and act to guide the process by suggesting input features, guiding spatial constraints, and supporting interactive exploration. We demonstrate the capabilities of the platform through a case study on the flooding-related risk in Jacksonville, Florida, showing how it allows users to explore, generate, and evaluate regionalization interactively, combining computational rigor with user-driven decision making.

Problem

• Creating dynamic planning units for disaster response
• Integrating agentic AI with human oversight in planning
• Addressing inflexibility of conventional geographic boundaries

Innovation

• Agentic AI guides dynamic region generation process
• RepSC-SOM extends self-organizing maps with geographic filtering
• Human-in-the-loop platform integrates computational and interactive exploration