Wednesday, March 22, 2023

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. 


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. 



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.


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.

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)

Thursday, February 09, 2023

Comparison between Online Social Media Discussions and Vaccination Rates

Continuing our work on social media and vaccinationsQingqing 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.


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)

Thursday, December 08, 2022

Simulating Geographical Systems using CA and ABMs

In the chapter we discuss how thinking and studying of geographical systems like cities has changed over time from top down aggregate analysis to more bottom up approaches which captures the complex nature of such systems. We then discuss how we can model such systems from a cellular automata and agent-based perspectives. and how these styles of models have evolved and how they can be used to model future systems. If this sounds of interest below we provide the abstract to the chapter, some of the figures that accompany it and at the  bottom of the page we provide the full reference to the paper along with a link to the chapter itself.
"Abstract: How we view and understand the processes driving and shaping geographical systems is constantly evolving. This is due to the appearance of new rich data sources, increased computing power and storage, and the development of individual-level approaches. This allows us to explore geographical systems (from the bottom up) at scales not possible in the past. In this chapter, we examine the utility of two of the most commonly used individual-level modelling approaches, cellular automata and agent-based modelling. We outline their key differences and how these models are being used to further our understanding of geographical systems through simulation. We conclude with a discussion about the challenges that both approaches need to meet to continue developing into the future.
Keywords: Cellular automata; Agent-based models; Geographical systems; Machine learning

A SLEUTH like model stylized on Santa Fe, New Mexico denoting how land use charges over time from undeveloped (grey) to urban (red).

Example applications of agent-based models at different spatial and temporal scales

Full reference:

Heppenstall, A., Crooks, A.T., Manley, E. and Malleson, N. (2022) Simulating Geographical Systems using Cellular Automata and Agent-based Models, in Rey S. and Franklin, R. (eds.), Handbook of Spatial Analysis in the Social Sciences, Edward Elgar Publishing, Cheltenham, UK, pp. 142-157. (pdf)

Friday, November 11, 2022

Announcing MASON 21, Geomason 1.7 & Distributed MASON 1

Many visitors and readers to this site know that for a long time I have been involved with and developing agent-based models utilizing MASON. To this end, the other day Sean Luke posted a message to the MASON list-serve regarding new releases of MASON, GeoMASON  and the first release of Distributed MASON which is part of our NSF CI-EN: Enhancement of a Large-scale Multiagent Simulation Tool project

To quote from the email:

"MASON is a high performance open-source modeling toolkit in pure Java, designed to be fast, highly hackable and modifiable, and to guarantee repeatable results, among many other capabilities. MASON comes with extensive visualization capabilities and regularly runs on everything from laptops to back-end supercomputers".

"Distributed MASON is an open-source, massively distributed version of MASON meant for server/farm and cloud computing deployment using a combination of MPI and RMI. It runs MASON over a large number of collective machines. "

"GeoMASON is an open source set of extensions to MASON which add GIS capabilities, including reading and writing standard formats, embodying agents in GIS environments, and visualization."

"Distributed GeoMASON is an open source set of extensions to GeoMASON to enable it to run over Distributed MASON in both server/farm and cloud computing environments."

For those interested in GIS and agent-based models, we have added many more application examples (a sample of which is shown below), along with fixing a number of bugs, and adding new code for compatibility with Distributed MASON. For more details check out the MASON webpage:

Examples of some of the GeoMason Models

If you have questions regarding MASON, GeoMason, or their distributed versions, join the MASON mailing list and ask


Tuesday, November 01, 2022

Mesa-Geo: ABM and GIS in Python (A Update)

A couple of months ago we had a post about Mesa-Geo but only a short one. Now we want to go into more detail as we (Boyu Wang, Vincent Hess and myself) just presented a paper about it at the 5th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2022). The paper itself was entitled "Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in Python" in which we discuss in detail the need for a python library for creating geographically explicit agents (or GeoAgents) and introduce its architecture. 

In the paper we detail how we have designed Mesa-Geo to handle spatial data (both in terms of raster and vector via GeoSpace), how we have enabled visualization of geographical data and such models along with creating features to export geographical data from the simulations (using Rasterio and GeoPandas). To support this discussion we also provide some explicit examples on how the pieces fit together  range from rainfall flowing over a digital terrain model (DEM) to Schelling types of models using points and polygons as agents, to that of agents using road networks to navigate over an area. Boyu has also put together more details about the examples at: (which includes movies of them running).  The actual code for the models and Mesa-Geo can be found at Just to give you a sense of the paper and what Mesa-Geo can do, below we provide the abstract to the paper, some figures showing the architecture, along with some example applications. While at the bottom of the post you can see the full reference and a link to the paper itself.  


Abstract: Mesa is an open-source agent-based modeling (ABM) framework implemented in the Python programming language, allowing users to build and visualize agent-based models. It has been used in a diverse range of application areas over the years ranging from biology to workforce dynamics. However, there has been no direct support for integrating geographical data from geographical information systems (GIS) into models created with Mesa. Users have had to rely on their own implementations to meet such needs. In this paper we present Mesa-Geo, a GIS extension for Mesa, which allows users to import, manipulate, visualize and export geographical data for ABM. We introduce the main components and functionalities of Mesa-Geo, followed by example applications utilizing geographical data which demonstrates Mesa-Geo's core functionalities and features common to agent-based models. Finally, we conclude with a discussion and outlook on future directions for Mesa-Geo.

Class diagram of the Agent, GeoAgent, and Cell classes
Component diagram of GeoSpace and its related classes
Example applications using Mesa and Mesa-Geo: (a) Rainfall model, (b) Population model, (c) GeoSchelling (polygons) model, (d) GeoSchelling (points \& polygons) model, and (e) Agents and networks model.

 If you have any thoughts or comments about Mesa-Geo please let us know.

Full reference:

Wang, B., Hess, V. and Crooks A.T. (2023), Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in Python, Proceedings of the 5th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2022), Seattle, WA. pp 1-10. (PDF)