Geosimulations for Addressing Societal Challenges Talks @ AAG

Last year we put out a call for abstracts for presentations for our sessions Geosimulations for Addressing Societal Challenges. The session description is as follows: 

There is an urgent need for research that promotes sustainability in an era of societal challenges ranging from climate change, population growth, aging and wellbeing to that of pandemics. These need to be directly fed into policy. We, as a Geosimulation community, have the skills and knowledge to use the latest theory, models and evidence to make a positive and disruptive impact. These include agent-based modeling, microsimulation and increasingly, machine learning methods. However, there are several key questions that we need to address which we seek to cover in this session. For example, What do we need to be able to contribute to policy in a more direct and timely manner? What new or existing research approaches are needed? How can we make sure they are robust enough to be used in decision making? How can geosimulation be used to link across citizens, policy and practice and respond to these societal challenges? What are the cross-scale local trade-offs that will have to be negotiated as we re-configure and transform our urban and rural environments? How can spatial data (and analysis) be used to support the co-production of truly sustainable solutions, achieve social buy-in and social acceptance? And thereby co-produce solutions with citizens and policy makers.

The call generated enough interest to allow us to organize two sessions with respect to geosimulations. With the AAG ongoing we thought we would post the session details and talks. Both sessions will take place in on Thursday the 27th of March in Room 420B, Level 4, Huntington Place.

Geosimulations for Addressing Societal Challenges (2); Time: 8:30 AM - 9:50 AM

Chair: Andrew Crooks

Presentations: 

• Kejian Li, Jiaqi Ge, Nik Lomax and Gary Polhill
• Title: Agent-Based Modeling of Food Trade Disruptions: Insights from the Russia-Ukraine Conflict
• Bryan Collins and Kathleen Liang
• Title: Agent-Based Modeling to Explore Social, Economic, And Environmental Links Within the North Carolina Poultry Industry
• Dylan Munson,  Simon Mak and John Miller
• Title: Environmental policy in the context of complex systems: statistical theory and practice
• Jeon-Young Kang, Jinwoo Park, and Huiji Jeong
• Title: Agent-Based Modeling for Exploring Spatial Accessibility to Urban Green Space
• Yan Liu, University of Queensland
• Title: Urban growth simulation using cellular automata and machine learning approaches

Geosimulations for Addressing Societal Challenges (2); Time: 10:10 AM - 11:30 AM

Chair: Fuzhen Yin

Presentations: 

• Raja Sengupta and Saeed Harati Asl
• Title: Why overthink Mobility in Agent-Based Models when optimization works?
• Zhongyu Zhou
• Title: Strategic Response Dynamics: Simulating Evacuation and Engagement in Airport Security Scenarios
• Moongi Choi, Jiwoo Seo, and Alexander Hohl
• Title: Agent-Based Travel Scheduler: Derivation of Spatiotemporal Risk Areas and Travel Behaviors During Pandemic
• Na Jiang, Boyu Wang and Andrew Crooks
• Title: Large language models for conceptualizing, designing, and generating agent-based models

We hope to see you there.

New Editorial: Cities and disasters: What can urban analytics do?

In the past I have blogged about disasters, but mainly from a social media or agent-based modeling perspective. However, after the devastating wildfires that impacted parts of Los Angeles County earlier this year led me to wonder how resilient are cities to such events? Or more generally, what role could urban analytics play on the various stages of disaster management (i.e., preparation, response, recovery, and mitigation), or how can data, models, and methods at the disposal of researchers be leveraged to better prepare us for future disasters and be linked to policy?

If these questions sound of interest, I encourage you to go and read  a short editorial that I recently published in Environment and Planning B entitled "Cities and Disasters: What can Urban Analytics Do?"

Full referece: 

Crooks, A.T. (2024), Cities and Disasters: What can Urban Analytics Do?, Environment and Planning B, 52(3): 523-526. (pdf)

Call for Papers: Integrating LLMs and Geospatial Foundation Models to Enhance Spatial Reasoning in ABMs

We are delighted to announce a special track on “Integrating Large-Language Models and Geospatial Foundation Models to Enhance Spatial Reasoning in ABMs” as part of the Social Simulation Conference 2025, 25th to 29th August 2025 at Delft University of Technology, the Netherlands. Full conference details can be found at the end of this email.

Abstract for the Special Track:

Recent developments in the use of large language models (LLMs) offer exciting opportunities to control agent behaviour in potentially more realistic and nuanced ways than has previously been possible. However, an LLM-backed agent can only interface with their surroundings through text prompts, which is severely limiting. The integration of large language models (LLMs) and geospatial foundation models (GFMs) presents an exciting opportunity to use AI techniques to advance agent-based modelling for spatial applications, potentially allowing for agents with more comprehensive behavioural realism, as well as an improved perception of their environment.

This special track invites papers that explore how AI techniques, such as LLMs and GFMs, can enrich spatial agent based models, raising new questions about their feasibility in modelling human behaviour, in comparison to conventional approaches. There are huge challenges around computational efficiency, sustainability, bias, model validation, and integration frameworks, and we welcome papers that consider these issues as well.

Paper submission deadlines and details: https://ssc2025.tbm.tudelft.nl/important-dates/

Paper types: 

• Long paper (10-12 pages, excluding references – long oral presentations, will be included in the post-proceedings)
• Short paper (6-9 pages, excluding references – short oral presentations, will be included in the post-proceedings)
• Extended abstract (3-4 pages, excluding references – short oral presentations, will not be included in the post-proceedings)

Track Organizers:

• Nick Malleson, University of Leeds, UK
• Alison Heppenstall, University of Glasgow, UK
• Ed Manley, University of Leeds, UK
• Andrew Crooks, University of Buffalo, US

Please feel free to get in touch with any of us in case of questions.

From print to perspective: A mixed-method analysis of the convergence and divergence of COVID-19 topics in newspapers and interviews

In previous posts we have noted how one can explore urban issues through newspapers, while at the same time we have used social media to explore trends in vaccinations. In a recently published paper in PLOS Digital Health entitled "From print to perspective: A mixed-method analysis of the convergence and divergence of COVID-19 topics in newspapers and interviews" with Qingqing Chen, Adam Sullivan, Jennifer Surtees, Laurene Tumiel-Berhalter and myself, we thought we would explore how COVID-19 was reported in newspapers and how this varied from interviews. 

The rationale behind this was that the COVID-19 pandemic has led to diverse experiences influenced by public health measures like lockdowns and social distancing. To explore these dynamics, we introduce a novel ’big-thick’ data approach that integrates extensive U.S. newspaper data with detailed interviews. By employing natural language processing (NLP) and geoparsing techniques, we identify key topics related to the pandemic and vaccinations both in newspapers and personal narratives from interviews, and compare the (spatial) convergences and divergences between them. 

We found that both sources converge to highlight the profound impacts of the pandemic on daily life. However, newspapers provide a macro-level perspective, predominately covering policy, public health efforts and economics, while interviews reveal the nuanced impacts at the micro-level, focusing on personal experiences, emotion and concerns. An intriguing finding is the pronounced concern regarding the reliability of news information from interviews. By showcasing both convergences and divergences in identified topics, our study enhances the understanding of key issues that both disseminated to and resonate with the public, contributing to the development of more effective communication strategies for future public health crises.

If this sounds of interest, below you can read the abstract to the paper, see some of the figures which include our workflow and some of the results. At the bottom of the post you can see the full reference and a link to the actual paper. While at https://figshare.com/s/339b1c0d059c189dd6a4?file=44583661 you can find the code we used for our analysis. 

Abstract:

In the face of the unprecedented COVID-19 pandemic, various government-led initiatives and individual actions (e.g., lockdowns, social distancing, and masking) have resulted in diverse pandemic experiences. This study aims to explore these varied experiences to inform more proactive responses for future public health crises. Employing a novel “big-thick” data approach, we analyze and compare key pandemic-related topics that have been disseminated to the public through newspapers with those collected from the public via interviews. Specifically, we utilized 82,533 U.S. newspaper articles from January 2020 to December 2021 and supplemented this “big” dataset with “thick” data from interviews and focus groups for topic modeling. Identified key topics were contextualized, compared and visualized at different scales to reveal areas of convergence and divergence. We found seven key topics from the “big” newspaper dataset, providing a macro-level view that covers public health, policies and economics. Conversely, three divergent topics were derived from the “thick” interview data, offering a micro-level view that focuses more on individuals’ experiences, emotions and concerns. A notable finding is the public’s concern about the reliability of news information, suggesting the need for further investigation on the impacts of mass media in shaping the public’s perception and behavior. Overall, by exploring the convergence and divergence in identified topics, our study offers new insights into the complex impacts of the pandemic and enhances our understanding of key issues both disseminated to and resonating with the public, paving the way for further health communication and policy-making.

An overview of the research workflow.

The monthly distribution of collected articles in the United States from January 2020 to December 2021.

An example of identified entities labeled with predefined entity types.

The spatial distribution of newspaper articles by different scales.

The spatial distribution of identified newspaper topics across different regions in New York State.

Ordered rank of identified topics by percentage from interviews.

Full reference:

Chen, Q., Crooks, A.T., Sullivan, A.J., Surtees, J.A. and Tumiel-Berhalter, L. (2025). From Print to Perspective: A mixed-method analysis of the convergence and divergence of COVID-19 topics in newspapers and interviews, PLOS Digital Health. Available at https://doi.org/10.1371/journal.pdig.0000736. (pdf)

New Directions in Mapping the Earth’s Surface with Citizen Science and Generative

In previous posts, we have written how large language models (LLMs) like ChatGPT can be used in various urban analytical applications. We have kept exploring this potential especially with respect to citizen science applications. To this end we have just published a new paper in iScience, entitled "New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI". In the paper, lead by Linda See, we discuss how multi-modal LLMs (MLLMs) which are like LMMs but can take different forms of inputs (e.g., text, images, video) and output multi-modal information (e.g., take an image and output a description) could be leveraged to enhance citizen science land cover/land use mapping campaigns. If this sounds of interest, below you can read the abstract to the paper, see some of the figures we use to build our argument, while at the bottom of the post you can see the full reference and a link to the actual paper.

Abstract: 

As more satellite imagery has become openly available, efforts in mapping the Earth’s surface have accelerated. Yet the accuracy of these maps is still limited by the lack of in-situ data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for collecting in-situ data through applications like Geo-Wiki and Picture Pile, but better approaches for optimizing volunteer time are still required. Although machine learning is being used in some citizen science projects, advances in generative Artificial Intelligence (AI) are yet to be fully exploited. This paper discusses how generative AI could be harnessed for land cover/land use mapping by enhancing citizen science approaches with multi-modal large language models (MLLMs), including improvements to the spatial awareness of AI.

Visual interpretation tasks undertaken by ChatGPT for (a) a wetland/mangrove landscape in South America (b) an agricultural area in central Europe.

Visual interpretation tasks undertaken by ChatGPT for identification of natural and non-natural ecosystems where ChatGPT misclassified the images as non-natural for locations in (a) Chad and (b) Austria. In (c), the image from Colombia was classified as unsure by validators but natural by ChatGPT.

Integrating multi-modal Large Language Models (MLLMs) in a citizen science visual interpretation workflow.

Full reference : 

See, L., Chen, Q., Crooks, A., Bayas, J.C.L., Fraisl, D., Fritz, S., Georgieva, I., Hager, G., Hofer, M., and Lesiv, M., Malek, Ž., Milenković, M., Moorthy, I., Orduña-Cabrera, F., Pérez-Guzmán, K., Schepaschenko, D., Shchepashchenko, M., Steinhauser, J.and McCallum, I. (2025), New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI, iScience, doi: https://doi.org/10.1016/j.isci.2025.111919. (pdf)