Integration of Community Level Data into Mathematical Models

In the past we have posted about how we can utilize data and models to explore pandemics and peoples reactions to them. And while interest in the COVID might of waned, there will be future pandemics. 

To this end, at the 53rd Annual Meeting of NAPCRG we (Laurene Tumiel Berhalter, Sanchit Goel, Dawn Vanderkooi, Bruce Pitman, Yinyin Ye,  Jennifer Surtees and myself) had a poster entitled "Integration of Community Level Data into Mathematical Models to Predict Future Public Health Emergencies." The objective of the poster is to showcase how one can integrate 211 data into models to predict future public health emergencies. If this sounds of interest, below you can see the poster and at the bottom of the post you can access the abstract. 

Full Reference:

Tumiel, L.M., Goel, S., Vanderkooi, D., Pitman E.B., Crooks A.T., Ye, Y. and Surtees, J. (2025), Integration of Community Level Data into Mathematical Models to Predict Future Public Health Emergencies, North American Primary Care Research Group (NAPCRG) 53rd Annual Meeting, 21st-25th November, Atlanta, GA (pdf).

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. pp. 407-410. (pdf) (poster)