We are organizing sessions on "Geosimulation: Human Dynamics, AI-Enabled Agents, and Spatial Decision-Making" at the AAG Annual Meeting in New York City, February 8-12, 2027.
Building on our geosimulation sessions of the past three years, we continue to welcome a wide range of studies exploring simulation theories, data, methodologies, and frameworks. We are interested in case studies that apply geosimulation to real-world challenges, as well as research that develops and evaluates AI-enabled geosimulation models. Potential topic areas include, but are not limited to:
Geosimulation Models and Applications
Conceptual Geosimulation Models
General-Purpose Geosimulation Frameworks
AI and Geosimulation
Agent Behavior, Decision-Making, and AI Agents
Data Generation Frameworks
Validation and Verification for Geosimulation
Digital Twins
Microsimulation
Multi-Agent Systems
System Dynamics Models
Interested in presenting? Submit your abstract to AAG by October 15, 2026. Then send your title, abstract (up to 250 words), and AAG abstract code to Boyu Wang (bwang44@buffalo.edu) and Ying Zhou (yzhou59@buffalo.edu) by November 5, 2026.
At the AAG Annual meeting this year, two of my students gave talks about their ongoing research. Ying Zhou presented her work with a talk entitled "Exploring the Relationship between Urban Morphology and People’s Emotions." In this talk, Ying showed how one could mine social media posts to gain a sense of how different emotions are spatially spread around a city using New York city as a case study. If this sounds of interest, below you can see the abstract of the talk, the research methodology and a sample of the results.
Abstract:
Urban morphology records physical information about spatial patterns (e.g., streets and land use) and their evolution over time, as well as human settlement information. People who live in or visit a city gain experiences through interaction with its spatial patterns, and these experiences influence people’s emotions. Therefore, it is necessary to explore the spatial relationships between urban morphology and people’s emotions. Taking New York City as a case study, this research uses social media data to obtain and locate people's emotions in different parts of the city. To extract the emotion relating to specific space, we use the RoBERTa-based model to label texts in social media with six primary emotions (i.e., happiness, sadness, fear, anger, surprise, and disgust). We then used DBSCAN to identify spatial clustering features of these emotions. Finally, we compared the clustered emotions with urban morphology (both in terms of both its form and function) and how such emotions evolve and change over a span of five years. Such analysis reveals the relationship between people’s emotions and broader setting that they inhabit (i.e., the city). Moreover, these works offer bottom-up insights into how urban morphology shapes people’s feelings, which can serve as feedback for urban planning and management.
If this sounds of interest, below you can see the abstract of the talk, along with the Mesa-LLM architecture. While further details about Mesa-LLM can be found on Boyu's GitHub page: https://github.com/mesa/mesa-llm.
Abstract
Agent‐based models (ABMs) have long been used to examine how individual behaviors give rise to aggregated social and spatial phenomena. Mesa, an open source ABM library in Python, provides modular components and browser based visualization to create and analyze agent based models in the PyData ecosystem. Agents’ behaviours in these models are often governed by rule-based decisions. The recent advancements of large language models (LLMs) have created a new paradigm, namely generative agent-based modeling, where LLMs are integrated as decision-making engines so that agents can communicate, negotiate, and decide based on natural language. In this paper, we introduce Mesa-LLM, an LLM extension to the Mesa framework. Its modular design allows users to customize reasoning, memory and planning components and plug in different LLMs (e.g., GPT, Gemini, Llama). We demonstrate Mesa-LLM through Epstein’s civil violence model. In contrast to the classical model where agents act based on calculated probabilities and pre-defined thresholds, agents through Mesa-LLM have their decisions articulated in natural language. This demonstration shows how an archetypal ABM can be enriched by language-based decision making to explore complex social dynamics such as protest escalation. Through this simple example, we highlight how incorporating LLMs into ABMs opens new possibilities for geographers to model human behavior from the bottom up by leveraging generative artificial intelligence (GenAI).
Keywords: Agent-Based Modeling, Large Language Model, AI Agent, Python.
References
Wang, B., Frisch, C., Nair, S., Kazil, J. and Crooks, A.T. (2026), Mesa-LLM: Generative Agent-Based Modeling with Large Language Models Empowered Agents, The Association of American Geographers (AAG) Annual Meeting, 17th –21th March, San Francisco, CA. (pdf)
Zhou, Y. and Crooks, A.T. (2026), Exploring the Relationship between Urban Morphology and People’s Emotions, The Association of American Geographers (AAG) Annual Meeting, 17th –21th March, San Francisco, CA. (pdf)
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
As the AAG has just wrapped up I thought I would write brief (well actually quite long) post on the talks that I was involved with at the conference. These talks would not have been possible without the many great students and colleagues who I have been collaborating with over time. Below you will find a brief summary of the talks and if any sound interesting, please reach out and we can give you more details.
First up (in order in which they were presented) was "Utilizing Streetview Images for Mapping Building Attributes with ChatGPT" with Qingqing Chen and Linda See. In this talk we discussed how multimodal Large Language Models are giving us a new way to study cities, in the sense, lowering the boundary for information extraction. Using ChatGPT andstreet view images from Mapillary as an example, we showed how one can extract building age, usage (e.g., commercial, mixed use, residential) and estimate building height which could all be used to inform urban climate models which require detailed information on buildings.
Abstract:
With increasing rates of urbanization, many challenges are emerging regarding sustainability such as the energy usage of buildings. Coinciding with this is the growing attention of urban climate models for energy demand estimation and climate adaptation strategies. However, the applicability of these models is constrained by the lack of detailed urban surface information. Therefore, creating comprehensive datasets that capture urban surface information at a granular scale is crucial for responding to our rapidly urbanizing world. Recent advancements in Large Language Models (LLMs) have opened new opportunities in urban studies, offering accessible methods for information extraction. In this talk we explore the feasibility of ChatGPT to extract building attributes from images. Taking New York City as a case study, we collect building images from Mapillary and process them through ChatGPT by posing specific questions to extract building attributes (e.g., height, functions, age). These attributes are then compared with authoritative data. The proposed method helps address the current dearth of fine-grained surface data on urban issues, therefore enhancing the accuracy and utility of urban climate models. Overall, this study demonstrates the practical applications of ChatGPT in geographic knowledge extraction, advancing the understanding of LLMs in geographic contexts, and more broadly to the discourse on Artificial Intelligence (AI) in urban modeling and climate science.
Keywords: Buildings, ChatGPT, Large Language Models (LLMs), Mapillary, Street View Images (SVI), GeoAI.
Example of Workflow.
Reference:
Chen, Q., See, L. and Crooks, A.T. (2025), Utilizing Streetview Images for Mapping Building Attributes with ChatGPT, The Association of American Geographers (AAG) Annual Meeting, 24th –28th March, Detroit, MI. (pdf)
This was followed by a talk by lead by Qingqing Chen entitled "Multi-sensory Experiences: The Connection Between the Smell and Vision in Understanding Urban Environments" where we explored to what extent can visual data from street view imagery be used as a proxy for capturing large-scale urban smell perceptions when compared to geosocial media. Such as what visual cues evoke specific smell perceptions.
Abstract:
Smell is a crucial transversal sense, which bridges the tangible aspects of urban environments, such as exhaust and garbage, with their intangible impacts on emotions, social interactions and well-being. Despite its crucial role in our everyday life, many urban studies primarily focus on the visual dimension, potentially introducing biases in our understanding of urban spaces. This research transcends this visual-centric bias by integrating the olfactory perceptions to investigate the nuanced relationship between smell and vision in urban environments. Specifically, we utilize advanced semantic segmentation to extract visual elements from street view imagery (i.e., Mapillay) and apply casual forest analysis to examine their causal effects on smell expectations recorded from human participants. These expectations, often tied to personal experiences and/or cultural associations, are compared with real-environment smell experiences derived from geosocial media (i.e., Twitter/X). The results show that visual cues can predict smells in straightforward urban settings, such as small parks or less densely populated areas. However, in complex urban environments, the predictive power of visual cues diminishes as diverse and overlapping scents obscure specific smells, even in visually distinct areas. These findings underscore the importance of a multisensory approach in urban studies, enhancing our understanding of the complex interplay between sensory experiences and informing urban design strategies that integrate multiple senses to create more engaging and inclusive environments. This is especially important for individuals with sensory impairments, such as anosmia or visual impairments, who rely on other senses to compensate for their perception of urban environments.
Keywords: Multi-sensory Experiences, Smell and Vision; Semantic Segmentation, Causal Effects, Geosocial Media, Street View Imagery (SVI).
Workflow
Reference:
Chen, Q. and Crooks, A.T. (2025), Multi-sensory Experiences: The Connection Between the Smell and Vision in Understanding Urban Environments, The Association of American Geographers (AAG) Annual Meeting, 24th –28th March, Detroit, MI. (pdf)
In the Geosimulation session that we organized, we had a talk entitled "Large Language Models for Conceptualizing, Designing, and Generating Agent-based Models" where Na Jiang, Boyu Wang and myself presented our work on exploring using multimodal Large Language Models (LLMs) to create age-based models. In the sense as modelers, we spend a lot of time developing and writing code and we were curious what could be done though the use of LLMs.
To give a sense of what is possible, below is an example of using ChatGPT for creating a model from a published paper.
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, Large Language Models, Geospatial Simulation
Reference:
Jiang, N., Wang, B. and Crooks, A.T. (2025), Large Language Models for Conceptualizing, Designing, and Generating Agent-based Models, The Association of American Geographers (AAG) Annual Meeting, 24th –28th March, Detroit, MI. (pdf)
Next up was Ying Zhou who presented our work entitled "Identifying Environmental Characteristics That Influence Perceived Safety in Urban Spaces." In this work we explored how using social media data can be used to study the fear and how this relates to actual crimes within New York city. Broadly speaking we find through our analysis, that fear sentiment may spread out between the neighborhoods and their surrounding areas and that neighborhoods surrounded by crime-clusters may have high sentiments of fear.
Abstract:
One goal of creating livable cities is to enhance health and safety. While previous research in spatial analysis and urban planning has focused on correlations between physical environments and crime, typically relying on police-reported crime data from sources like the Crime Open Database (CODE), safety perception is inherently subjective and cannot be fully represented by objective crime statistics alone. Also, urban planning today has gradually shifted its focus from a top-down mechanism to a bottom-up mechanism, so understanding and fostering spaces where residents feel safe is essential. This research examines factors that contribute to residents’ perceived insecurity in New York City. In addition to spatial analysis of the open crime data, the research used social media data to acquire people’s perceptions. The result indicates that the aggregations of perceived unsafe locations overlapped with aggregations of crime data's locations, such as in Manhattan’s neighborhoods, but they do not overlap with each other entirely. By adopting Latent Dirichlet Allocation (LDA), a method of topic modeling, the research filtered and summarized the posted texts and contents related to the negative descriptions of places or spaces in the city, and then it identified the related characteristics of the environments. The characteristics are investigated by the method of local Moran’s I, which indicates their spatial autocorrelation in some neighborhoods in the city of New York. This research offers “bottom’s views” about urban safety for both urban planning and decision-makers, which contributes to people-centered consideration for future development and urban resource distribution.
Zhou, Y. and Crooks, A.T. (2025), Identifying Environmental Characteristics That Influence Perceived Safety in Urban Spaces, The Association of American Geographers (AAG) Annual Meeting, 24th –28th March, Detroit, MI. (pdf)
The last day of the conference was another busy day with two talks. First was entitled "PySGN: A Python Package for Constructing Synthetic Geo-social Networks" where Boyu Wang presented our work (with Taylor Anderson and Andreas Züfle) on a Python package that can be used to generate synthetic geo-social networks. As readers of this blog might know we have a an interest in social networks and using them in modeling and this package provides a toolkit for others to easily create their own geosocial networks (e.g., Geospatial Erdős-Rényi, Barabási–Albert and Watts-Strogatz models). For interested readers, the source code available at: https://github.com/wang-boyu/pysgn.
Abstract:
Synthetic population has been widely used in social simulations such as traffic modeling, pedestrian movements, and the spread of infectious diseases. In recent years, much attention was focused on generating synthetic population with social networks, that captures social connections between individuals. While synthetic populations are often geographically explicit, various algorithms have been proposed to create realistic geographic social (geo-social) networks, aiming to integrate spatial information into people’s social links. We build an open-source Python package, namely PySGN, for constructing synthetic geo-social networks that incorporates position information, exhibits small-world network properties, and can be scaled to hundreds of thousands and potentially millions of nodes. We discuss different ways of parametrizing the method, by either a global average node degree, or an expected degree for each individual node. It is demonstrated through a case study with synthetic population in Buffalo, NY. By doing so, we aim to illustrate how such synthetic geo-social networks can be created, utilized, and analyzed in downstream agent-based modeling and network analysis tasks. This work is available as an open-source Python package and integrated with the PyData ecosystem (e.g., GeoPandas, NetworkX), and can be further extended with more synthetic geo-social network algorithms in the future.
Wang, B., Crooks, A.T., Anderson, T. and Züfle, A. (2025), PySGN: A Python Package for Constructing Synthetic Geo-social Networks. The Association of American Geographers (AAG) Annual Meeting, 24th –28th March, Detroit, MI. (pdf)
The final talk (well for me) was presented by Fuzin Yin who presented our work with Lucie Laurian and Emmanuel Frimpong Boamah entitled "Analysis of Online Mutual Aid Network during Buffalo Blizzard 2022: Actors and Weak Ties." In this work we explored what kind of support was offered and requested over Facebook groups along with their network structures durring and shortly after the event utilizing machine learning.
Abstract:
In December 2022, Buffalo, NY encountered a once-in-a-generation blizzard that dropped over 4 feet of snow. This four-day snow event halted emergency services and left 47 dead. In the face of the devastating blizzard, Buffalonian demonstrated resilience and solidarity by establishing Facebook (FB) groups to share information and coordinate behaviors including donations, wellness checks, and snow removals. These spontaneous behaviors created an essential layer of protection when the major infrastructure was down. This research has collected data from Buffalo blizzard FB groups to analyze community-led self-help behaviors. We have used machine learning to classify FB messages into four categories (e.g., requesting help, offering help, emotional support, and other), and social network analysis to explore users’ communication patterns. Results show that out of all messages (n=9,988), 37% of them express emotional support, which is followed by messages offering help (25%). While requests for help constitute a small proportion (8%), they stimulate more replies than other categories. Network statistics suggest that the mutual aid network is low-density but with a high clustering coefficient. This implies that most group members are strangers with weak ties, but their connections are in the right place to allow efficient communication. However, users do not equally benefit where people requesting or offering help are central in online conversation while pure emotional supporters are at the periphery. We conclude that during the Buffalo blizzard 2022, online interactions translate into offline mutual assistance by establishing weak ties among disconnected users to facilitate the flow of information and resources.
Keywords: crisis informatics, mutual aid, social network analysis, machine learning, social media
Results of Mutual Aid Network during Buffalo Blizzard 2022
Reference:
Yin, F., Laurian, L., Crooks, A.T. and Boamah, E.F. (2025), Analysis of Online Mutual Aid Network during Buffalo Blizzard 2022: Actors and Weak Ties, The Association of American Geographers (AAG) Annual Meeting, 24th –28th March, Detroit, MI. (pdf)
While this is a rather longer post than normal, we hope you found it interesting and also as noted at the top of the post, if any of these talks/topics are of interest to you please feel free to reach out.
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
Building upon last year’s successful sessions related to geosimulation, were various topics and issues from across the urban, social and environmental fields and the resulting application areas. More excitingly, we are witnessing the emergence of the integration of cutting-edge techniques (e.g., machine learning and generative AI) which is energizing the geosimulation community as they offer new approaches for advancing geosimulations.
This year, the 2025 AAG Annual Meeting will take place in Detroit, Michigan from March 24 to March 28. We are continuing to organize sessions on "Geosimulations for Addressing Societal Challenges," and we encourage you to submit abstracts if this area aligns with your research interests.
Session Description:
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.
We are particularly interested in presentations that will discuss issues relating to:
Agent-based modeling and microsimulation techniques for responding to societal challenges;
Agent-based models used for policy formation;
Data driven modeling;
Utilizing machine modeling for geosimulation;
Creating really big models using exascale computation;
Model validation and assessment;
Participatory methods for agent-based modeling;
Approaches to connect and share (open source) data and models;
Revealing, quantifying, and reducing socio-economic inequalities with Geosimulation.
Next Steps:
If this sounds of interest, please e-mail the abstract and keywords with your expression of intent to Richard Jiang (njiang8@buffalo.edu) by October 29 (2 days before the AAG session deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and keywords and as specified at: https://aag.secure-platform.com/aag2025/page/abstracts/abstract-guidelines An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions.
Timeline:
October 29, 2024: Please send abstract and keywords with your expression of intent to Richard Jiang (njiang8@buffalo.edu)
October 30th, 2024: Session finalization and author notification
October 31, 2024: Final abstract submission to AAG, via https://aag.secure-platform.com/aag2025. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Richard Jiang. Neither the organizers nor the AAG will edit the abstracts.
February 6, 2025: Final Abstract/Session Editing and Presentation Conversion deadlines for AAG, via https://aag.secure-platform.com/aag2025.
At the recent American Association of Geographers (AAG) Annual Meeting in Honolulu, Hawaii, our group had several presentations showcasing some of the research we are doing here at the University at Buffalo with respect to agent-based modeling, social media analysis and machine learning. If these sound of interest feel free to reach out to us to find out more.
First up was Na (Richard) Jiang who presented a paper entitled "Populating Digital Twins with Humans: A Framework Utilizing Artificial Agents". In this presentation he showcase our workflow of embedding agents in models of cities using examples from simple commuting models (like that shown below) to the spread of diseases.
Abstract
Over the last few decades, considerable efforts have been placed in creating digital virtual worlds. Ranging in applications from engineering, geography, industry, and translation. More recently, with the growth of computational resources and the explosion of spatial data sources (e.g., satellite imagery, aerial photos, and 3-dimensional urban data), creating detailed virtual urban environments or urban digital twins has become more widespread. However, these works emphasize on the physical infrastructure and built environment of the urban areas instead of considering the key element acting within the urban system, which are the humans. In this paper, we would like to remedy this by introducing a framework that utilizes agent-based modeling to add humans to such urban digital twins. Specifically, this framework consists of two major components: 1) synthetic population datasets generated with 2020 Census Data; and 2) pipeline of using the population datasets for agent-based modeling applications. To demonstrate the utility of this framework, we have chosen representative applications that showcase how digital twins can be created for study various urban phenomena. These include building evacuations, traffic congestion and disease transmission. By doing so, we believe this framework will benefit any modeler wishing to build an urban digital twin to explore complex urban issues with realistic populations.
Keywords: agent-based model, geosimulation, urban digital twins
Following this talk, I presented work on behalf of Fuzhen Yin and Na (Richard) Jiang entitled "Modeling Covid Vaccination uptake in New York State: An agent-based modeling perspective". In this paper we utilize Richards workflow and add social media networks to it in order to explore vaccination uptake for the whole of New York state. We do this through the lens of opinion dynamics and agent-based modeling, in the sense how people may change their opinions about wether or not to get vaccinated based on information from different sources (e.g. family, friends or online).
Abstract
The effect of the recent COVID pandemic has been significantly curtailed with the introduction of vaccinations. However, not everyone has been vaccinated for a multitude of reasons. For example, people might be influenced by what they read online or the opinions of others. To explore the changes in people’s views on vaccination, we have developed a geographically explicit agent-based model utilizing opinion dynamics. The model captures people’s opinions on COVID vaccination and how this relates to actual vaccination trends. Using the entire state of New York with a population of over 22 million agents, we model vaccination uptake from January 1, 2021, until May 15, 2022. Agents within the model synthesize information from the other agents they are connected with either in physical or cyberspace and decide whether to vaccinate or not. We compare these vaccination statuses among different age groups with actual vaccination rates provided by New York State. Our results suggest that there is an interplay between different spaces and ages when it comes to agents making a decision to vaccinate or not. As such the model offers a novel way to explore vacation decisions from the bottom up.
Keywords: Agent-based modeling, Covid, Vaccine, Geosimulation, Social Networks
Ofter this, Qingqing Chen presented work with Ate Poorthuis and myself entitled "Mapping the Invisible: Decoding Perceived Urban Smells through Geosocial Media in New York City" in which we explore how social media can be used to map smells in large metropolitan areas.
Abstract
Smells can shape people’s perceptions of urban spaces, influencing how individuals relate themselves to the environment both physically and emotionally. Although the urban environment has long been conceived as a multisensory experience, research has mainly focused on the visual dimension, leaving smell largely understudied. This paper aims to construct a flexible and efficient bottom-up framework for capturing and classifying perceived urban smells from individuals based on geosocial media data. Thus, increasing our understanding of this relatively neglected sensory dimension in urban studies. We take New York City as a case study and decode perceived smells by teasing out specific smell-related indicator words through text mining and network analysis techniques from a historical set of geosocial media data (i.e., Twitter). The dataset consists of over 56 million data points sent by more than 3.2 million users. The results demonstrate that this approach, which combines quantitative analysis with qualitative insights, can not only reveal “hidden” places with clear spatial smell patterns, but also capture elusive smells that may otherwise be overlooked. By making perceived smells measurable and visible, we can gain a more nuanced understanding of smellscapes and people’s sensory experiences within the urban environment. Overall, we hope our study opens up new possibilities for understanding urban spaces through an olfactory lens and, more broadly, multi-sensory urban experience research.
Last but not least, Boyu Wang presented his work entitled "Simulating urban flows with geographically explicit synthetic populations". In this talk, Boyu showed how a deep learning spatial-temporal urban flow model is trained to predict the aggregated inflows and outflows within regions and feed directly into an agent-based model.
Abstract
Urban human mobility is an active research field that studies movement patterns in urban areas at both the individual and aggregated population levels. Through individual’s movement, higher level phenomena such as traffic congestion and disease outbreaks emerge. Understanding how and why people move around a city plays an important role in urban planning, traffic control, and public health. An abundance of agent-based models have been built by researchers to simulate human movements in cities and are often integrated with a GIS component to realistically represent the study area. In this work we build a geographically explicit agent-based model where agents move between their home and workplaces, to simulate people’s daily commuting patterns within a city. In order to build this model, we develop a geographically explicit synthetic population based on census data. A deep learning spatial-temporal urban flow model is trained to predict the aggregated inflows and outflows within regions of the study area, which are subsequently used to drive individual agents’ movements. To validate results from the agent-based model, agents’ movements are aggregated and evaluated along with the urban flow model. Commuting statistics are also collected and compared to existing travel surveys. As such we aim to demonstrate how urban simulation models can be complemented by recent advancements in GeoAI techniques. Conversely, the aggregated deep learning model predictions can be investigated at a fine-grained individual level. This extends traffic patterns forecasting from just looking at the patterns to the processes that lead to these patterns emerging.
Yin, F., Jiang., N. and Crooks, A.T. (2024), Modeling Covid Vaccination uptake in New York State: An Agent-based Modeling Perspective, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th April, Honolulu, HI. (pdf)
Jiang., N. Crooks, A.T., Wang, B. and Yin (2024), Populating Digital Twins with Humans: A Framework Utilizing Artificial Agents, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th April, Honolulu, HI. (pdf)
Chen, C., Poorthuis, A. and Crooks, A.T. (2024), Mapping the Invisible: Decoding Perceived Urban Smells through Geosocial Media in New York City, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th April, Honolulu, HI. (pdf)
Wang, B. and Crooks, A.T. (2024), Simulating Urban Flows with Geographically Explicit Synthetic Populations, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th April, Honolulu, HI. (pdf)
At the upcoming American Association of Geographers (AAG) Annual Meeting in Honolulu, Hawaii, we (Alison Heppenstall, Na (Richard) Jiang, Gary Polhill, Andrew Crooks, Raja Sengupta, Suzana Dragicevic, Sarah Wise, Jeon-Young Kang) have organized 3 sessions around the theme of Geosimulations for Addressing Societal Challenges. This is part of the 10th Anniversary Symposium on Human Dynamics Research. If you are at the AAG on Tuesday the 16th of April and have the time it would be great if you could stop by and see the talks. Details are below.
Sessions Abstract:
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.
Session 1 (Date: 4/16/2024;Time: 10:40 AM - 12:00 PM;Room: 312 (Ni`ihua), Third Floor, Hawai'i Convention Center)
