Mapping Resilience: AI-Powered Regions for Smarter Disaster Planning

by

Author: Denis Avetisyan

A new approach leverages artificial intelligence and localized data to create adaptable regional boundaries that better address specific disaster risks and enable more targeted interventions.

The system architecture facilitates agentic AI-enhanced planning support for regionalization, leveraging a framework designed to optimize complex spatial arrangements and resource allocation through autonomous decision-making processes.
The system architecture facilitates agentic AI-enhanced planning support for regionalization, leveraging a framework designed to optimize complex spatial arrangements and resource allocation through autonomous decision-making processes.

This review details a RepSC-SOM framework utilizing agentic AI and heterogeneous geospatial data for data-driven regionalization in disaster risk management and spatial planning.

Traditional planning units often fail to capture the nuanced demands of local communities, hindering effective disaster preparedness. This paper, ‘Enhancing Demand-Oriented Regionalization with Agentic AI and Local Heterogeneous Data for Adaptation Planning’, introduces a novel planning support system leveraging agentic AI and a spatially-constrained self-organizing map (RepSC-SOM) to generate data-driven, adaptable regions for risk management. Through a case study in Jacksonville, Florida, we demonstrate how this platform facilitates interactive exploration and evaluation of regionalization strategies, combining computational rigor with user-driven decision-making. Could this approach unlock more responsive and equitable adaptation planning in increasingly complex urban environments?

Spatial Heterogeneity and the Imperative of Granular Planning

Disaster preparedness isn’t uniform; effective strategies hinge on recognizing that risk isn’t evenly distributed across a landscape. Regions don’t experience hazards identically, due to variations in topography, population density, infrastructure, and socioeconomic factors. Consequently, a one-size-fits-all approach to planning proves inadequate. Instead, preparedness necessitates a granular regionalization – dividing areas into units that accurately reflect these complex, spatially varying risks. This allows resources to be strategically allocated, prioritizing areas with higher vulnerability and tailoring interventions to specific local conditions. Ignoring this spatial heterogeneity can lead to misdirected efforts, leaving some communities disproportionately exposed while others receive unnecessary support, ultimately undermining the overall effectiveness of disaster response.

Current spatial planning methodologies often falter when confronted with the integration of varied data streams crucial for effective disaster preparedness. Socioeconomic indicators, detailing population density and resource availability, are frequently compiled and analyzed separately from environmental conditions – such as floodplains or earthquake fault lines – and assessments of infrastructure vulnerability, including building codes and transportation network resilience. This fragmented approach hinders the creation of truly meaningful spatial units, as it fails to capture the complex interplay between these factors. Consequently, regions may be inadequately defined, masking localized vulnerabilities and preventing the targeted allocation of resources needed to mitigate potential impacts. The inability to synthesize these diverse datasets into a cohesive spatial framework represents a significant obstacle to building robust and equitable disaster resilience strategies.

The challenge of effective disaster preparedness is significantly compounded by the inherent complexity of spatially heterogeneous data. Geographic information isn’t uniformly distributed; instead, critical factors like population density, infrastructure quality, and environmental vulnerability vary dramatically across even short distances. This creates a mosaic of risk that traditional planning methods – often relying on broad, administrative boundaries – struggle to accurately represent. Integrating diverse datasets, each with differing resolutions and formats, into a cohesive spatial framework requires sophisticated analytical techniques. Without these, attempts at regionalization risk obscuring localized vulnerabilities and leading to inefficient allocation of resources, ultimately hindering the development of truly actionable and targeted preparedness strategies. The sheer volume and variety of relevant information necessitates advanced computational approaches to identify meaningful patterns and delineate regions that reflect genuine risk profiles.

Ineffective spatial delineation directly undermines disaster preparedness by creating zones that fail to accurately reflect localized risk. When planning efforts rely on broad or generalized spatial units, critical variations in vulnerability—stemming from differences in socioeconomic status, environmental exposure, and infrastructure resilience—are obscured. Consequently, resources may be misallocated, protective measures may be insufficient in high-risk areas, and certain communities remain disproportionately exposed. This uneven distribution of vulnerability isn’t simply a matter of inequity; it actively compromises the overall effectiveness of disaster response, as failures in one area can cascade and exacerbate impacts elsewhere. Achieving truly resilient regional planning, therefore, necessitates a move beyond conventional spatial boundaries and towards granular, data-driven units that precisely capture the complex interplay of factors influencing risk and vulnerability.

The platform allows users to define a study area and disaster type to tailor regionalization analyses.
The platform allows users to define a study area and disaster type to tailor regionalization analyses.

RepSC-SOM: A Framework for Data-Driven Regionalization – A Rigorous Approach

The RepSC-SOM framework incorporates an auto-encoder embedding step to address the challenges of high-dimensional and complex datasets. This process reduces the dimensionality of input data while preserving crucial relationships between variables. Auto-encoders, a type of artificial neural network, learn efficient data codings through unsupervised learning, identifying underlying patterns and creating a lower-dimensional representation. This embedded representation then serves as input for the Self-Organizing Map (SOM), improving the SOM’s ability to accurately cluster data and define meaningful regions, particularly when dealing with datasets containing numerous features or non-linear correlations. The resulting embedding facilitates more effective regionalization by capturing the inherent structure within the data before spatial clustering is performed.

Self-Organizing Maps (SOM) are a type of unsupervised learning technique utilized for dimensionality reduction and data visualization, effectively clustering high-dimensional data into a lower-dimensional, typically two-dimensional, representation. In the RepSC-SOM framework, SOMs facilitate the creation of spatially coherent regions by mapping similar data points to nearby nodes on the map. This process preserves the topological relationships within the data, ensuring that geographically proximate areas with similar characteristics are grouped together. The resulting map provides a visual and quantifiable representation of regionalization, where each node represents a distinct region and the distance between nodes reflects the degree of similarity between those regions. The framework utilizes the SOM’s ability to identify patterns and relationships within complex datasets, thereby generating regions that are both statistically meaningful and geographically logical.

The RepSC-SOM framework explicitly supports spatial contiguity through algorithmic constraints during the Self-Organizing Map (SOM) clustering process. This is achieved by prioritizing the grouping of geographically adjacent data points, ensuring resultant regions exhibit spatial connectedness. Specifically, the framework avoids the creation of fragmented or disjointed regions by penalizing the assignment of spatially isolated data points to the same cluster. This approach guarantees the logical soundness of the generated regionalization, reflecting real-world geographic relationships and facilitating meaningful spatial analysis and interpretation.

Haversine distance is employed within the RepSC-SOM framework to accurately calculate the great-circle distance between two points on a sphere, given their longitudes and latitudes. This is critical for spatial filtering because Euclidean distance becomes increasingly inaccurate as points approach the poles or are widely separated. The Haversine formula, $d = 2r \arcsin(\sqrt{\sin^2(\frac{\Delta lat}{2}) + \cos(lat_1) \cos(lat_2) \sin^2(\frac{\Delta lon}{2})})$, where $r$ is the radius of the Earth, $\Delta lat$ is the difference in latitude, and $\Delta lon$ is the difference in longitude, provides a more precise measurement of spatial separation, ensuring that geographically proximate regions are correctly identified during the Self-Organizing Map (SOM) clustering process and preventing distortion in regionalization results.

The platform successfully regionalizes data, as demonstrated in this screenshot of its output.
The platform successfully regionalizes data, as demonstrated in this screenshot of its output.

Agentic AI and Refinement: Achieving Precision Through Iteration

The Agentic AI Planning Support System employs RepSC-SOM as its primary regionalization engine. RepSC-SOM is a machine learning technique utilized to partition a geographic area into spatially coherent and functionally similar regions. It operates by iteratively clustering data points based on their feature similarities, minimizing within-region variance while maximizing between-region differences. This process results in a tessellation of the study area, where each region represents a distinct combination of characteristics relevant to disaster planning, such as population density, infrastructure vulnerability, and hazard exposure. The RepSC-SOM implementation within the system allows for dynamic regionalization based on varying data inputs and disaster scenarios.

The system’s adaptability is significantly enhanced through the use of a Large Language Model (LLM) that dynamically selects input features for regionalization. Rather than relying on a fixed feature set, the LLM analyzes both the geographic characteristics of the study area and the specific parameters of the disaster event. This analysis informs the selection of the most pertinent input features – such as population density, infrastructure networks, elevation, and historical disaster impact data – to optimize the regionalization process. This feature selection process allows the system to tailor its analysis to the unique context of each scenario, improving the relevance and accuracy of generated regions for disaster response planning.

The region-growing process functions as an iterative refinement stage within the RepSC-SOM engine, systematically improving the spatial characteristics of initially generated regions. This process operates by merging adjacent seed regions based on pre-defined similarity criteria, calculated using relevant input features. Each iteration assesses the compactness and coherence of the evolving regions; specifically, it minimizes fragmentation and maximizes the spatial contiguity of areas exhibiting similar characteristics. The iterative nature of the process allows for the gradual optimization of region boundaries, ensuring the final output consists of spatially consistent and logically defined areas suitable for disaster response planning and resource allocation. This continued refinement enhances the overall quality and usability of the regionalization results.

Human-in-the-Loop Refinement is a critical component of the Agentic AI Planning Support System, enabling disaster response planners to directly modify and validate the automatically generated regionalizations. This process allows planners to leverage their contextual knowledge of the affected area – including infrastructure details, population density, and local vulnerabilities – to adjust region boundaries and ensure the resulting areas are both logically coherent and practically useful for resource allocation and response planning. Specifically, planners can merge, split, or reassign spatial units, correcting any inaccuracies or omissions present in the initial automated output and guaranteeing the final regions align with real-world conditions and operational requirements. This iterative feedback loop improves the overall accuracy and actionability of the system’s output, moving beyond purely algorithmic solutions to incorporate vital human expertise.

Towards Proactive Resilience and Demand-Oriented Planning: A Paradigm Shift

Hazard-aware regionalization represents a pivotal shift in disaster preparedness, moving beyond uniform strategies to embrace geographically nuanced planning. This approach allows regional planners to delineate areas based on specific hazard profiles – be it floodplains, seismic zones, or wildfire-prone landscapes – and subsequently tailor mitigation and response efforts accordingly. Instead of applying a one-size-fits-all solution, resources and interventions can be precisely targeted to address the unique vulnerabilities of each region, maximizing their effectiveness and minimizing potential damage. By integrating detailed hazard mapping with regional boundaries, planners gain a granular understanding of risk distribution, facilitating the development of targeted building codes, evacuation plans, and infrastructure investments. This localized focus not only enhances resilience but also promotes more efficient resource allocation and fosters greater community ownership of disaster preparedness initiatives.

The system fosters a shift towards planning frameworks that are directly responsive to the needs of those they serve through Demand-Oriented Regionalization. Rather than imposing pre-defined administrative boundaries, the approach facilitates the creation of planning units that coalesce around shared demands – such as access to critical infrastructure, emergency services, or economic opportunities – as identified by local communities. This bottom-up methodology ensures that resources are allocated efficiently and equitably, addressing the most pressing concerns of residents and stakeholders. By prioritizing user needs, the system enhances the relevance and effectiveness of spatial planning, moving beyond a one-size-fits-all approach to one that is tailored to the unique characteristics and priorities of each region.

A core benefit of this integrated framework lies in its capacity to shift disaster management from reactive response to proactive resilience building. Rather than solely addressing consequences after an event, the system identifies potential vulnerabilities within urban landscapes before disasters strike. This is achieved through detailed spatial analysis, incorporating hazard data with socio-economic factors to pinpoint areas and populations most at risk. By anticipating these weaknesses – whether related to infrastructure, demographics, or environmental conditions – planners can implement targeted interventions. These interventions range from reinforcing critical infrastructure and diversifying supply chains to enhancing early warning systems and developing comprehensive evacuation plans, ultimately minimizing potential damage and accelerating recovery efforts. This preventative approach represents a fundamental shift towards more sustainable and effective urban governance, fostering communities better prepared to withstand future challenges.

The research details a system poised to redefine urban governance through a more nuanced understanding of localized disaster risks. Traditional spatial planning often relies on broad-stroke assessments, failing to account for the unique vulnerabilities present in specific communities. This framework, however, integrates detailed risk data with regional characteristics, allowing for targeted interventions. The paper demonstrates that this improved risk capture translates to more effective planning units, better aligned with community needs, and ultimately, a significant advancement in building proactive resilience. By shifting from reactive disaster management to preventative strategies, cities can enhance their capacity to withstand and recover from future events, fostering more sustainable and equitable urban development.

The pursuit of adaptable planning regions, as detailed in this study, mirrors a fundamental principle of algorithmic construction: the search for invariant truths amidst complex data. This work’s RepSC-SOM framework, prioritizing data-driven regionalization over conventional methods, echoes Ada Lovelace’s sentiment: “The Analytical Engine has no pretensions whatever to originate anything.” The system doesn’t invent solutions, but rather meticulously organizes and reveals inherent patterns within local heterogeneous data – specifically, disaster risks. Just as the Engine required precise instructions, this agentic AI relies on rigorously defined parameters to produce regions demonstrably more attuned to the nuances of spatial planning and effective intervention.

Beyond the Regions

The presented RepSC-SOM framework, while demonstrating a capacity for nuanced regionalization, merely scratches the surface of a far more fundamental challenge. The true difficulty does not reside in generating regions, but in rigorously proving their inherent stability and resilience under conditions of genuine, unpredictable stress. Current validation relies heavily on historical data—a comforting, but ultimately circular, logic. A region defined by past disasters is, tautologically, predisposed to similar events. The elegance of a solution is not measured by its ability to mirror the known, but by its predictive power regarding the unknown.

Future work must prioritize the formal verification of these agentic systems. The notion of “adaptation” itself requires mathematical definition. What constitutes a ‘successful’ adaptation? Under what conditions does a region’s self-organization converge towards a stable, optimal configuration? The current emphasis on human-in-the-loop interaction, while pragmatically sensible, risks obscuring the core algorithmic problem: can we build a system that guarantees robustness, or are we simply refining a sophisticated form of trial and error?

Ultimately, the field should shift from merely modeling complexity to reducing it. Simplicity, it must be remembered, does not equate to brevity—it demands non-contradiction and logical completeness. The pursuit of ever-more-detailed simulations, without a corresponding effort to establish formal guarantees, is a beautiful distraction from the essential task: to build systems that are, demonstrably, correct.

Original article: https://arxiv.org/pdf/2511.10857.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-11-18 00:10