New Paper: Modeling Wildfire Evacuation with Embedded Fuzzy Cognitive Maps

While we have explored disasters in the past through agent-based models and other computational social science approaches, one area we have not explored is how one can use agent-based models to explore evacuations durring a wild fire event.  This has now changed with a new paper with  Zhongyu Zhou and myself entitled  "Modeling Wildfire Evacuation with Embedded Fuzzy Cognitive Maps: An Agent-Based Simulation of Emotion and Social Contagion" which was recently presented at the  2025 International Conference of the Computational Social Science Society of the Americas (CSSSA). 

In the paper we present an agent-based model combined with an embedded fuzzy cognitive map (FCM) to simulate residents’ evacuation behavior during a wildfire event. If this sounds of interest, below we provide the abstract to the paper along with some of the figures that showcase the model logic and some of its results. A detailed ODD, the model and the data needed to run the model can be found at: https://github.com/ozzyzhou99/LA-Wildfire-Model/. Finally, at the bottom of the post you can find the full referece to the paper and a link to it.  

Abstract: 

Wildfires are becoming increasingly dangerous, especially in densely populated fire-prone areas like Los Angeles. People’s evacuation decisions during wildfire events are influenced by many factors, including emotions such as fear or panic, which often affect people’s choices to evacuate. Traditional evacuation models often assume that individuals behave rationally. As a result, these models tend to overlook the influence of emotional factors on evacuation behavior. To address this issue, this study develops an agent-based model (ABM) combined with an embedded fuzzy cognitive map (FCM) to simulate residents’ evacuation behavior during a wildfire event. The model covers two types of agents: evacuees and rescuers. It focuses on how emotions change over time and how they spread among people. While we also expect to observe how these emotional changes will affect evacuation decisions. This research also considers differences between different income groups to explore whether low-income residents are more likely to panic. Results from the model show that agents with different emotions behave differently during the evacuation process. Emotional changes clearly affect how agents choose routes and whether they can respond quickly. In addition, the results suggest that income level affects emotional responses, and low-income groups are more likely to feel fear. This study highlights the value of using ABM and FCM together to better understand evacuation behavior and provides a new idea for developing fairer and more effective disaster response plans.

Keywords: Agent-Based Modeling, Emotional decision-making, GIS, Fuzzy Cognitive Map, Wildfire Evacuation.

Data used in the setting up the model experiment. (A) is household income data, (B) is location of previously affected houses, and (C) is evacuation road data.

Agent-level embedded FCM loop with social contagion.

Evacuees’ Workflow (A), Rescuers” Workflow (B).

Box plots of average emotions for three groups of experiments (50 repetitions each). From left to right, the number of people in each income group increases progres- sively. Low income (LI), middle income (MI), and high income (HI).

Full Referece 

Zhou, Z. and Crooks, A.T. (2025), Modeling Wildfire Evacuation with Embedded Fuzzy Cognitive Maps:An Agent-Based Simulation of Emotion and Social Contagion, Proceedings of the 2025 International Conference of the Computational Social Science Society of the Americas, Santa Fe, NM. (pdf)

HD-GEN: A Software System for Large-Scale Human Mobility Data Generation Based on Patterns of Life

 

Human mobility datasets are essential for investigating human behavior, mobility patterns, and traffic dynamics.  In the past we have written about how one can use agent-based models to generate patterns of life trajectories datasets. Building on this work at the ACM SIGSPATIAL 2025 conference, we (Hossein Amiri, Richard Yang, Shiyang Ruan, Joon-Seok Kim, Hamdi Kavak, Andrew Crooks,  Dieter Pfoser,  Carola Wenk and Andreas Züfle) had a paper entitled "HD-GEN: A Software System for Large-Scale Human Mobility Data Generation Based on Patterns of Life"

In this paper, we extend our previous work by introducing a software system that provides a new suite of tools built on top of the Patterns of Life simulation framework. Specifically this work consolidates our contributions into a unified data generation pipeline that includes:

1. additional discussion of the motivation and applications of large-scale simulated trajectory data, 
2. detailed instructions on running the simulation and generating datasets, 
3. extended analysis of the shared dataset, and 
4. an integrated GitHub repository

The proposed system enables large-scale synthetic dataset generation, either by statistically replicating real-world data or by creating datasets with user-defined properties. If this sounds of interest, below you can read the abstract to the paper, the poster that accompanies it and we have also provided detailed instructions on how to reproduce the generated datasets, and made the code and data available at https://github.com/onspatial/large-scale-dataset-generator.

Abstract

Understanding individual human mobility is critical for a wide range of applications. Real-world trajectory datasets provide valuable insights into actual movement behaviors but are often constrained by data sparsity and participant bias. Synthetic data, by contrast, offer scalability and flexibility but frequently lack realism. To address this gap, we introduce a comprehensive software pipeline for generating, calibrating, and processing large-scale human mobility datasets that integrate the realism of empirical data with the control and extensibility of Patterns-of-Life simulations. Our system consists of three integrated components. First, a genetic algorithm–based calibration module fine-tunes simulation parameters to align with real-world mobility characteristics, such as daily trip counts and radius of gyration, enabling realistic behavioral modeling. Second, a data generation engine constructs geographically grounded simulations using OpenStreetMap data to produce diverse mobility logs. Third, a data processing suite transforms raw simulation logs into structured formats suitable for downstream applications, including model training and benchmarking. 

Keywords: GeoLife, Patterns of Life, Simulation, Realistic Trajectory Datasets

Dataset creation phases with HD-GEN software.

Full Reference: 

Hossein, A., Yang, R.,  Ruan, S., Kim, J-S., Kavak, H., Crooks, A.T., Pfoser, D., Wenk, C. and Züfle, A., (2025). HDGEN: A Software System for Large-Scale Human Mobility Data Generation Based on Patterns of Life. In The 33rd ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’25), November 3–6, 2025, Minneapolis, MN. (pdf) (poster)

Call for Papers: Geosimulation and Its Emerging Directions with AI

As part of the GeoAI and Deep Learning Symposium at the 2026 AAG Annual Meeting in San Francisco, California we have a call for papers for sessions entitled "Geosimulation and Its Emerging Directions with AI"

Call for Papers:

Simulating past, present, and future events can empower humans to understand the composition and interactions in complex systems and explain their emergence and evolution from bottom up. In practice, geosimulations constitute a powerful tool in engaging different stakeholders, exploring what-if scenarios, and evaluating alternative policy outcomes.

We invite interdisciplinary works for the exploration and understanding of complex social and environmental processes by means of computer simulation. We focus on all aspects of simulation and agent societies, including multi-agent systems, agent-based modeling, microsimulation, artificial intelligence (AI) agents, and the integration of Generative AI with simulation.

As GenAI is impacting all aspects of our lives, we are wondering how it will impact geospatial simulations. How do multimodal large language models (MLLMs) help with agent-decision making in the form of generating agent-personas or scheduling agent activities? Can MLLMs reduce coding barriers for beginners? Will GenAI lead to a new generation of modeling toolkits? What are the challenges brought by MLLMs in model design, validation, and computing costs?

We welcome a wide range of studies exploring simulation theories, data, methodologies, and frameworks. We are also interested in case studies applying geosimulations to address real-world challenges. Potential topic areas include, but are not limited to:

• Geosimulation Models and Applications
• Conceptual Geosimulation Models
• General-Purpose Geosimulation Framework
• AI and Geosimulation
• Agents’ Behaviors, Decision-making and AI Agents
• Data Generation Framework
• Validation and Verification for Geosimulation
• Digital Twins
• Microsimulation
• Multi-agent Systems

If you are interested, please email your title and 250-word abstract to Fuzhen Yin (fyin@uccs.edu) and Jeon-Young Kang (geokang@khu.ac.kr) by October 30th.

Chairs:

• Fuzhen Yin, University of Colorado Colorado Springs
• Jeon-Young Kang, Kyung Hee University

Organizers:

• Alison Heppenstall, University of Glasgow, Scotland.
• Andrew Crooks, University at Buffalo, USA.
• Na (Richard) Jiang, Hong Kong University of Science and Technology (Guangzhou), China
• Fuzhen Yin, University of Colorado, Colorado Springs, USA.
• Raja Sengupta, McGill University, Canada.
• Suzana Dragicevic, Simon Fraser University, Canada.
• Boyu Wang, University at Buffalo, USA.
• Sarah Wise, University College London, England
• Jeon-Young Kang, Kyung Hee University, South Korea
• Yahya Gamal, University of Glasgow, Scotland.
• Alexander Michels, University of Texas at Dallas, USA
• Joon-Seok Kim, Emory University, USA

Sponsor Groups:

• Cyberinfrastructure Specialty Group, 
• Geographic Information Science and Systems Specialty Group, 
• Spatial Analysis and Modeling Specialty Group

LLMs and ABMs

In a previous post we talked about the potential of Generative AI for urban modeling, keeping with this theme at the 11th International Conference on Computational Social Science (IC2S2), Na Jiang, Boyu Wang and myself had a poster entitled  Agent-based Models with Large Language Models: Two Modeling Examples. 

In this poster and extended abstract we detail how LLMs can help with many aspects of agent-based modeling development. If this sounds of interest, below you can see the abstract, the poster and the full referece and link to the extended abstract .

Abstract:

Large language models (LLMs) play an important role in AI-powered code assistants such as code completion, debugging, and documentation. Such models can be further fine-tuned on smaller amount of data for specific tasks, often with the improvement of performance compared to generic LLMs. However, such fine-tuning techniques are seldomly used in generating sophisticated agent-based models (ABMs), because they are often implemented as software that demands extra standards such as the Overview, Design concepts, and Details (ODD) protocol. This research examines how we can bridge this gap by utilizing LLMs in designing or conceptualizing, building, and running agent-based models in the form of user prompts. In this work, two models are created to demonstrate the proposed method. Specifically, Sakoda's checkerboard model of social interaction is created by LLM from explicit design and description through prompts. The other model stimulates consumer preferences and restaurant visits as designed and implemented by a LLM. These models are evaluated by human experts on their code correctness and quality for both verification and validation purposes. This work serves as a first step towards fine-tuned LLMs on existing models and documentations to create high-quality and functional ABMs based on either user prompts or standard protocols, contributing to further exploration on the future of AI-assisted geospatial simulation development.

Keywords: agent-based modeling, geospatial simulations, large language models, generative AI, coding 

Full reference: 

Jiang, N., Wang, B. and Crooks, A.T. (2025), Agent-based Models with Large Language Models: Two Modeling Examples, 11th International Conference on Computational Social Science (IC2S2), 21-24th July, Norrkoping, Sweden. (extended abstract pdf) (poster pdf)

Examining spatial expansion and stemming strategies of urban shrinkage

In the past we have written about how one can study urban shrinkage with a specific emphasis on Detroit from both an agent-based modeling perspective and also from analyzing newspapers through natural language processing  Keeping with the theme of Detroit and urban shrinkage we (Xiaoliang Meng, Yichun Xie, Junyi Wu, Heather Khan Welsh,  Shi Zeng and myself) have a new paper entitled "Examining spatial expansion and stemming strategies of urban shrinkage: evidence from Detroit, USA" which was recently published in npj Urban Sustainability. 

In this paper we introduce a method for studying urban shrinkage by constructing multi-scale spatial structures based on urban network connectivity which we call gravity-networked spatial interaction zones-based spatial panel modeling or GSIZs-Spanel for short. We demonstrate this method by exploring the spatial processes and scopes of past urban shrinkage in Detroit between 2000 and 2020.  If this sounds of interest, below you can read the abstract to the paper, along with the conceptual design of GSIZs-Spanel modeling framework and some of our results. While at the bottom of the post you can find the full referece and link to the paper. 

Abstract:

This study introduces a new modeling paradigm called gravity-networked spatial interaction zones-based spatial panel modeling (GSIZs-Spanel). Using Detroit as a case study, this paper investigates urban shrinkage by integrating shrinkage driving factors, their regional interactions, networks of cities, spatial processes, and longitudinal dynamics. Results suggest that high minority population concentration and persistent poverty are the primary factors impacting Detroit’s inner-city shrinkage. Demographics, economics, and development practices affect shrinkage in suburbs and surrounding cities. Shrinkage spreads outwards like waves; different juxtapositions of driving factors affect shrinkage resilience; spillover effects are particularly vibrant at 25–50 GSIZs; rightsizing is a rational strategy, but it failed to work alone. Integrating spatial planning of driving factors, land uses, spillover effects, rightsizing strategy, and regional collaboration among federal, regional, and local organizations could moderate urban decline. GSIZs-Spanel, which was developed here, could be applied in any U.S. city or other global city.

The conceptual design of GSIZs-Spanel modeling framework.

Patterns of spillover effects of the Spanel models at the 5-incremental spatial clusters. (a: Spatial processes of urban shrinkage. b: Spatial patterns of vacancy severity.)

Spillover effects of the Spanel models at the 5-incremental spatial clusters

Full Reference: 

Meng X., Xie, Y., Crooks, A.T., Wu J., Khan-Welsh, H. and Zen, S. (2025), Examining spatial expansion and stemming strategies of urban shrinkage: evidence from Detroit, USA, npj Urban Sustainability, 5: 52. Available at https://doi.org/10.1038/s42949-025-00245-5 (pdf)