AAG 2025 Talks

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 and street 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. 

Keywords: Safety, Crime, Urban Space, Livable Cities, Social Media, Spatial Analysis.

Methodology

Reference: 

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. 

Keywords: Agent-Based Modeling, Synthetic Geo-Social Network, Python, Open-Source Software

Examples of Geosocial Networks Created in PySGN

Reference: 

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. 

A comparison of social surveys and social media for vaccine hesitancy

In the past we have explored various ways to explore vaccine hesitancy and keeping with this theme we have a new paper published in PLOS ONE entitled "Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media" with Kuleen Sasse, Ron Mahabir, Olga Gkountouna and Arie Croitoru. 

In the paper we use social, demographic and economic (e.g., US Census) variables to predict COVID-19 vaccine hesitancy levels in the ten most populous US metropolitan statistical areas (MSAs). By using  machine learning algorithms (e.g., linear regression, random forest regression, and XGBoost regression) we compare a set of baseline models that contain only these variables with models that incorporate survey data and social media (i.e., Twitter) data separately. 

We find that different algorithms perform differently along with variations in influential variables such as age, ethnicity, occupation, and political inclination across the five hesitancy classes (e.g., “definitely get a vaccine”, “probably get a vaccine”, “unsure”, “probably not get a vaccine”, and “definitely not get a vaccine”).   Further, we find that the application of the models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. But in summary, this paper shows social media data’s potential for understanding vaccine hesitancy, and tailoring interventions to specific communities. If this sounds of interest, below we provide the abstract to the paper along with our mixed methods matrix, data sources used and the results from the various MSAs. At the bottom of the post, you cans see the full reference and the link to the paper so you can read more if you so desire. 

Abstract:

The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data’s potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources.

Mixed methods matrix showing the data, processing, and model development steps used in our study.

Data sources used in our study.

MSA model performance (Bolded adjusted R² values represent the best performing model for each modeling technique and MSA).

Referece: 

Sasse K, Mahabir R, Gkountouna O, Crooks A, Croitoru A (2024) Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media. PLoS ONE 19(6): e0301488. https://doi.org/10.1371/journal.pone.0301488  (pdf)

Presentations at the AAG

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.

Keywords: Smellscape, Urban smells, Geosocial media, Text mining, Network analysis, Multi-sensory urban experiences.

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.

Keywords: Agent-Based Modeling, Urban Flow, GeoAI, Urban Simulation, Synthetic Populations 

References: 

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)

Spatial Data Science Symposium

The other week Yingjie Hu and myself co-organized a session entitled "Spatial Data Science for Disaster Resilience" as part for the 4th Spatial Data Science Symposium (SDSS 2023). 

Session Abstract: 

Natural disasters, such as hurricanes, floods, tornados, wildfires, earthquakes, and blizzards, pose significant threats to people and society. The availability of various geospatial data sources (e.g., drone-collected images, mobile phone location data, social media data, and sensor network data) combined with the advancement of statistical and machine learning models provide great opportunities for understanding human-environment interactions during these catastrophic events. This session aims to bring together researchers interested in using spatial data science to answer questions and address issues in any aspect related to disaster management.

Talks in the session: 

• Lei Zou (keynote): 
• Achieving a Smart and Resilient Future with Spatial Data Science.
• Qunying Huang: 
• Wildfire Burnt Area Detection with Deep Learning and Sentinel2 Imagery.
• Manzhu Yu: 
• Deciphering Wildfire Dynamics: Spatiotemporal Attention-Based Sequence-to-Sequence Models Using ConvLSTM Networks.
• Md Zakaria Salim: 
• Socio-economic Disparities of Property Damage in Hurricane Ian.
• Qingqing Chen: 
• Community Resilience to Wildfire: A Network Analysis Approach by Utilizing Human Mobility Data.
• Kai Sun: 
• GALLOC: a GeoAnnotator for Labeling LOCation Descriptions from Disaster-related Text Messages.

If these talks sound of interest and as this was a online and distributed event, the main organizers of the Symposium have made all the talks available online.  The talks from our session can be seen below and all the other talks and seasons from the symposium at large can be found here.

 

AAG 2023 Presentations

At this years Association of American Geographers (AAG) Annual Meeting we have a number of presentations ranging from how one can leverage newspaper articles to study cities over time, to that of how people may chose to become vaccinated. These presentations build on the great work of students and postdocs here at the University at Buffalo and link to our interests in urban analytics, machine learning and agent-based modeling. Below we just give a glimpse at these topics (along with their abstracts) and if you are interested in finding out more please reach out to us.

First up is a presentation with Qingqing Chen and Boyu Wang entitled "Community resilience to wildfires: A network analysis approach utilizing human mobility data."  In this presentation we explore how we can quantify a communities resilience to wildfires utilizing human mobility through network analysis methods. 

Abstract 

Natural disasters, such as earthquakes, floods, and wildfires, have been a long-standing concern to societies at large. With growing attention being paid to sustainable and resilient communities, such concern has been brought to the forefront of resilience studies. However, the definition of disaster resilience is intricate and can vary across the diverse disciplines that study them (e.g., geography, sociology and political science), making its definition and quantification elusive. Moreover, the vast majority of studies often focus on the immediate response to an event, not the long-term recovery of the area impacted by disasters. Thus to date investigating the resilience of an area or a society over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel approach from a social perspective utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires, especially on collective human behavior. Taking the Camp and Mendocino Complex wildfires - the most deadly and the largest complex wildfires in California to date, respectively - as case studies, we capture the features of resilience, such as robustness and vulnerability, of communities based on human mobility data from 2018 to 2020. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, which can provide a new lens to study natural disasters and their long-term impacts on society.

Keywords: Community Resilience, Natural Disasters, Wildfires, Social Network Analysis, Human Mobility, Space and Time.

Full Reference

Chen, Q., Wang, B. and Crooks, A.T. (2023), Community Resilience to Wildfires: A Network Analysis Approach Utilizing Human Mobility Data, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th March, Denver, CO. (pdf)

Next up, moving from mobility to textural data, specifically that of newspapers Na (Richard) Jiang and myself have a presentation entitled "Leveraging Newspapers to Understand Urban Issues: A Longitudinal Analysis of Urban Shrinkage in Detroit". In this work we explore how can leverage Bertopic (a topic modeling technique) on newspaper articles spanning the years 1975 to 2021 to explore urban shrinkage in Detroit. 

 

Abstract 

Today we are awash with data especially when it comes to studying cities from a diverse data ecosystem ranging from demographic to that of remotely sensed imagery and social media. This has led to the growth of geographical data science and urban analytics providing new ways to conduct quantitative research within cities. One area that has seen significant growth is that of using natural language processing techniques on text data from social media to explore various issues relating to urban morphology. However, social media only provides limited insights when dealing with longer-term urban phenomena, such as the growth and shrinkage of cities. This relates to the fact that social media is a relatively recent phenomenon compared to more longer-term urban problems that take decades to emerge. With respect to the longer-term coverage, newspapers which are increasingly becoming digitized provide the possibility to overcome the limitations of social media and provide insights over a timeframe that social media does not. To demonstrate the utilization of newspapers within urban analytics and to study longer-term urban issues, we present an advanced topic modeling technique (i.e., Bertopic) on a large number of newspaper articles spanning the years 1975 to 2021 to explore urban shrinkage in Detroit. Our topic modeling results reveal the insights related to Detroit's shrinkage can be linked to the side effects of economic recessions on Detroit's automobile industry, local employment status, and the housing market. As such, this work demonstrates the potential of utilizing newspaper articles to study long-term issues

Keywords: Natural Language Processing, Topic Modeling, Newspapers, Text Data, Urban Shrinkage, Urban Analytics. 

 Full Reference

Jiang, N., and Crooks A.T. (2023), Leveraging Newspapers to Understand Urban Issues: A Longitudinal Analysis of Urban Shrinkage in Detroit, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th March, Denver, CO. (pdf)

Switching gears slightly, we have another presentation that leverages text data, in this case Yelp reviews to help inform decision making within an agent-based model. This presentation with Boyu Wang is entitled "Do people care about others' opinions of places? Utilizing crowdsourced data and deep learning to model peoples’ review patterns."  We use a geospatial artificial intelligence (GeoAI) technique called aspect-based sentiment analysis to extract and categorize reviewers' opinion aspects on places within urban areas and then use this information to inform an agent-based model of peoples choices to which restaurants to go to.

Abstract  

People's opinions are one of the defining factors that turn spaces into meaningful places. While these opinions are subject to individual differences, they can also be influenced by the opinions from others. Online platforms such as Yelp allow users to publish their reviews on businesses. To understand reviewers' opinion formation processes and the emergent patterns of published opinions, we utilize geospatial artificial intelligence (GeoAI) techniques especially that of aspect-based sentiment analysis methods (a deep learning approach) on a geographically explicit Yelp dataset to extract and categorize reviewers' opinion aspects on places within urban areas. Such data is then used as a basis to inform an agent-based model, where reviewers' (i.e., agents') opinions are characterized by opinion dynamics. The parameters of these models are calibrated using extracted opinion aspects from the Yelp dataset. Such a method moves opinion dynamics models away from theoretical concepts to a more data-driven approach, with a specific emphasis being made on place. Focusing on 10 US metropolitan areas which are spread out across the country, we examine the calibrated influence coefficients for each opinion aspect category (e.g., location, experience, service), to compare reviewers' opinion formation processes across different categories. The results show the emergent patterns of reviewers' opinions and the influence of these opinions on others. As such this work demonstrates how using deep learning techniques on geospatial data can help advance our understanding of place and cities more generally.

Keywords: Agent-Based Modeling, Crowdsourcing, Deep Learning, GeoAI, Opinion Dynamics, Urban Analytics

Full Reference

Wang, B. and Crooks, A.T. (2023), Do People Care About Others' Opinions of Places? Utilizing Crowdsourced Data and Deep Learning to Model Peoples’ Review Patterns, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th March, Denver, CO. (pdf)

Following with the agent-based modeling theme, our final presentation with Fuzhen Yin and Li Yin is entitled "How Information Propagation in Physical, Relational and Cyber Spaces Affects Covid-19 Vaccine Uptake: Evidence from Rural Areas." In this work we explore how people may or not be influenced by others (in physical, relational and cyber spaces) with respect to vaccination uptake.

 

Abstract 

With the advent of information and communication technologies, human dynamics studied in a purely physical space increasingly shift to a cyber and relational context. While researchers increasingly recognize the shift and call for attention to the multi-dimensionality of human dynamics (e.g., Splatial framework). Rarely have studies investigated how the information propagated in hybrid spaces affects people’s decision-making process, such as Covid-19 vaccine uptake. Meanwhile, compared to the urban population, the rural population faces greater digital barriers and has been further left out in human dynamics research. To fill this gap, our study investigates Covid-19 vaccine uptake in a rural county (i.e., Chautauqua) in New York State through agent-based modeling. We first generated a synthetic population to match the demographic characteristics of the census data. Then we created home, work, school, and social media networks to represent hybrid spaces. We defined the opinion dynamics of agents based on the social influence network theory. Next, we calibrated and validated our agent-based model based on real-world vaccine update records. Our research helps to elucidate the information propagation mechanism in hybrid spaces and clarify the decision-making process in the digital age. Furthermore, our method can also shed light on how to overcome data limitations for under-represented populations such as those who live in rural areas.

Keywords: Agent-based modeling, Covid-19, Vaccination, Opinion dynamics, Urban informatics, Rural geography

Full Reference 

Yin, F., Crooks, A.T. and Yin, L. (2023), How Information Propagation in Physical, Relational and Cyber Spaces Affects Covid-19 Vaccine Uptake: Evidence from Rural County, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th March, Denver, CO. (pdf)

Comparison between Online Social Media Discussions and Vaccination Rates

Continuing our work on social media and vaccinations,  Qingqing Chen, Arie Croitoru, and myself have a new paper entitled "A comparison between online social media discussions and vaccination rates: A tale of four vaccines" published in DIGITAL HEALTH. In the paper we explore online debates among four prominent vaccines (i.e., COVID-19, Influenza, MMR, and HPV) as captured on Twitter in the United States (US) from 2015 to 2021.

By using machine learning models (e.g., Naive Bayes, support vector machine (SVM), logistic regression, and extreme gradient boosting (XGBoost)) on over  11.7 million Twitter messages sent by approximately 2.6 million distinct users we found that while the COVID-19, it has come to dominate the vaccination discussion, there was an apparent discrepancy between the online debates and the actual vaccination rates in the US. 

If this sounds of interest and you wish to find out more, below we provide the abstract to to the paper, some figures which captures our workflow and a sample of the results such as a comparison between different vaccine discussions on Twitter and the actual vaccination rate. Finally at the bottom of the page you can find the full reference and a link to the paper.

Abstract: 

The recent COVID-19 pandemic has brought the debate around vaccinations to the forefront of public discussion. In this discussion, various social media platforms have a key role. While this has long been recognized, the way by which the public assigns attention to such topics remains largely unknown. Furthermore, the question of whether there is a discrepancy between people's opinions as expressed online and their actual decision to vaccinate remains open. To shed light on this issue, in this paper we examine the dynamics of online debates among four prominent vaccines (i.e., COVID-19, Influenza, MMR, and HPV) through the lens of public attention as captured on Twitter in the United States from 2015 to 2021. We then compare this to actual vaccination rates from governmental reports, which we argue serve as a proxy for real-world vaccination behaviors. Our results demonstrate that since the outbreak of COVID-19, it has come to dominate the vaccination discussion, which has led to a redistribution of attention from the other three vaccination themes. The results also show an apparent discrepancy between the online debates and the actual vaccination rates. These findings are in line with existing theories, that of agenda-setting and zero-sum theory. Furthermore, our approach could be extended to assess the public's attention toward other health-related issues, and provide a basis for quantifying the effectiveness of health promotion policies.

Keywords:  COVID-19, Influenza, MMR, HPV, Social media, Vaccination.

 

The workflow for comparing between online social media discussion and vaccination rates.

The quarterly distribution of percentage of users by different vaccine discussion from 2015 to 2021.

 The comparison between different vaccine discussions on Twitter and growth rate of the actual vaccination rate collected from the CDC (a) COVID-19; (b) Influenza; (c) HPV; (d) MMR.

The changes of emotion over time for different vaccines.

Full reference: 

Chen Q, Croitoru A. and Crooks A.T (2023), A Comparison between Online Social Media Discussions and Vaccination Rates: A tale of four vaccines. DIGITAL HEALTH: 9. doi:10.1177/20552076231155682. (pdf)

Analyzing the vaccination debate in social media data Pre- and Post-COVID-19 pandemic

In the past we have written about how vaccination is discussed on social media but such studies were often just focused on short study periods (i.e. a month). However, with the current COVID-19 pandemic we thought we would revisit the vaccination  debate and see if it has changed. So in a new paper with Qingqing Chen entitled "Analyzing the vaccination debate in social media data Pre- and Post-COVID-19 pandemic," we did just that. We explored approximately 11.7 million tweets posted between January 2015 to July 2021 and measured and mapped vaccine sentiments (Pro-vaccine, Anti-vaccine, and Neutral) across the US. Not to ruin the surprise of what we found but also to encourage you to read the paper we will not write about the results here. Only show the abstract of the paper, a few of the figures and a link to the paper itself.

Abstract

The COVID-19 virus has caused and continues to cause unprecedented impacts on the life trajectories of millions of people globally. Recently, to combat the transmission of the virus, vaccination campaigns around the world have become prevalent. However, while many see such campaigns as positive (e.g., protecting lives), others see them as negative (e.g., the side effects that are not fully understood scientifically), resulting in diverse sentiments towards vaccination campaigns. In addition, the diverse sentiments have seldom been systematically quantified let alone their dynamic changes over space and time. To shed light on this issue, we propose an approach to analyze vaccine sentiments in space and time by using supervised machine learning combined with word embedding techniques. Taking the United States as a test case, we utilize a Twitter dataset (approximately 11.7 million tweets) from January 2015 to July 2021 and measure and map vaccine sentiments (Pro-vaccine, Anti-vaccine, and Neutral) across the nation. In doing so, we can capture the heterogeneous public opinions within social media discussions regarding vaccination among states. Results show how positive sentiment in social media has a strong correlation with the actual vaccinated population. Furthermore, we introduce a simple ratio between Anti and Pro-vaccine as a proxy to quantify vaccine hesitancy and show how our results align with other traditional survey approaches. The proposed approach illustrates the potential to monitor the dynamics of vaccine opinion distribution online, which we hope, can be helpful to explain vaccination rates for the ongoing COVID-19 pandemic.

Keywords: COVID-19, Pandemic, Vaccination Sentiment Analysis, Time and Space, Social Media, United States.

Overview of our research pipeline.

Comparison between Twitter data and Google Trends. (a) Distribution of keywords search and tweets over time; (b) Correlation between Google Trends and Twitter data of keywords search; (c) Important news or announcements catched on Twitter activity (Note: period is the shaded area in (a)).

Correlation between Pro-vaccine users and actual vaccination records (a) Spatial distribution of odds ratio of Pro-vaccine users; (b) Spatial distribution of odds ratio of actual vaccination records; (c) Correlation between the Pro-vaccine users and the actual vaccination records.

Full Reference: 

Chen, Q. and Crooks, A.T. (2022), Analyzing the Vaccination Debate in Social Media Data Pre- and Post-COVID-19 Pandemic, International Journal of Applied Earth Observation and Geoinformation, 110: 102783.  Available at https://doi.org/10.1016/j.jag.2022.102783 (pdf)

Postdoctoral Associate Position Available

Come work with me!

The Department of Geography at the University at Buffalo (UB) invites applicants for a postdoctoral researcher position with broad interests in urban analytics with a particular emphasis on modeling and simulation. 

The postdoctoral researcher will be expected develop systems to collect and store social media data and use such data to inform and develop a portfolio of innovative agent-based models utilizing machine learning methods to study urban areas. The ideal candidate will have experience with developing agent-based models, social network analysis, GIS and machine learning.  

Key responsibilities:

• Collecting, storing and analyzing social media data for a variety of urban topics.
• Developing geographically explicit agent-based models.
• Presenting material to broader community (i.e., publications conference presentations).

Required knowledge skills and abilities: 

• PhD. in geography, computer science or a related field (or expect to have obtained their PhD. by the time the post commences).  
• Ability to work independently and in an interdisciplinary research team.
• Excellent written communication skills demonstrated by prior publications.
• Strong programming skills in Python or Java.

Preferred Qualifications

• Familiarity with GIS, machine learning and social media data.
• Experience with developing agent-based models.

This position is affiliated with the Department of Geography at the University at Buffalo (UB), a large R1 Research University on the shores of Lake Erie. 

Outstanding Benefits Package

Working at UB comes with benefits that exceed salary alone. There are personal rewards including comprehensive health and retirement plan options. We also focus on creating and sustaining a healthy mix of work, personal and academic pursuit – all in an effort to support your work-life effectiveness. Visit our benefits website to learn about our benefit packages. 

About UB

The University at Buffalo is SUNY’s most comprehensive public research university, and an outstanding place to work. UB amplifies ambition for faculty and staff by offering endless possibilities to achieve more. Here, people from all backgrounds and cultures challenge and inspire each other to discover, learn and succeed. Dedicated staff and engaged faculty collaborate to further knowledge and understanding, and develop tenacious graduates who are valued for their talents and their impact on global society. Visit our website to learn more about the University at Buffalo.

As an Equal Opportunity / Affirmative Action employer, the Research Foundation will not discriminate in its employment practices due to an applicant’s race, color, religion, sex, sexual orientation, gender identity, national origin and veteran or disability status.

 

Further Details and How to Apply: https://www.ubjobs.buffalo.edu/postings/29858

Agent-Based Modeling and the City

Turning our attention back to agent-based modeling, in the recently open access edited volume by Wenzhong Shi, Michael Goodchild, Michael Batty, Mei-Po Kwan and Anshu Zhang entitled Urban Informatics, Alison Heppenstall, Nick Malleson, Ed Manley and myself have a chapter entitled "Agent-Based Modeling and the City: A Gallery of Applications." 

In the chapter we discuss cities through the lens of complex systems comprised of composed of people, places, flows, and activities. Moreover, we make the argument that as cities contain large numbers of discrete actors interacting within space and with other systems from nature, predicting what might happen in the future is a challenge. We base this argument on the fact that human behavior cannot be understood or predicted in the same way as in the physical sciences such as physics or chemistry. The actions and interactions of the inhabitants of a city, for example, cannot be easily described in a physical science theory such as that of Newton’s Laws of Motion. This notion is captured quite aptly by a quote by Nobel laureate Murray Gell-Mann: “Think how hard physics would be if particles could think.” Building on these arguments we introduce readers to agent-based modeling as it offers a way to explore the processes that lead to patterns we see in cities from the bottom up but also allows us to incorporate ideas from complex systems (e.g., feedbacks, path dependency, emergence) along with providing a gallery of applications of geographically explicit agent-based models. 

We then discuss how agent-based models can incorporate various decision-making processes within them and  how we can integrate data within such models with a specific emphasis on geographical and social information. This leads us to a discussion on how agent-based modelers are utilizing machine learning (such as genetic algorithms, artificial neural networks, Bayesian classifiers, decision trees, reinforcement learning, to name but a few) and data mining (i.e. finding patterns in the data) within their models: from the design of the model, the execution of the model to that of the evaluation of the model. Finally,  we conclude the chapter with a summary and discuss new opportunities with respect to agent-based modeling and the city. One such opportunity is dynamic data assimilation which could be transformative for the ways that some systems, for example “smart” cities, are modeled. Our argument is that agent-based models are often used to simulate the behavior of complex systems, these systems often diverge rapidly from initial starting conditions. One way to prevent a simulation from diverging from reality would be to occasionally incorporate more up-to-date data and adjust the model accordingly (i.e., data assimilation). Data, especially streaming data produced through near real time observational datasets (e.g., social media, vehicle routing counters) could be utilized in such a case. If what we have written above is of interest, below we provide the abstract to chapter along with some figures which we use to illustrate some key points or concepts (such as dynamic data assimilation). Finally at the bottom post, we provide the full reference and a link to the chapter.

Abstract:

Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which governs their interactions with each other and their environment. It is through these interactions that more macro phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the last two decades, with the growth of computational power and data, agent-based models have evolved into one of the main modeling paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter we will introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

Key Words: Agent-based Modeling, Geographical Information Systems, Machine Learning, Urban Simulation.

Using geographical information as a foundation for artificial worlds.

A selection of GeoMason models across various spatial and temporal scales.

Dynamic data assimilation and agent-based modeling.

Full Reference:

Crooks, A.T., Heppenstall, A., Malleson, N. and Manley, E. (2021), Agent-Based Modeling and the City: A Gallery of Applications, in Shi, W., Goodchild, M., Batty, M., Kwan, M.-P., Zhang, A. (eds.), Urban Informatics, Springer, New York, NY, pp. 885-910. (pdf)