Monday, May 13, 2024

Using ABM to simulate Covid-19 vaccine uptake

In past blog posts we have discussed how one can use social media to study vaccine discussions and even tried to build a very simple disease model where vaccination rates were a factor in the spread of an outbreak. However, when it comes to vaccinations, especially that of Covid-19 vaccine there has been intense discussions in the physical (e.g., family), hybrid (e.g., work, school) and cyber (e.g., social media) spaces we inhabit. 

One thing that is unclear is how do these discussions in these various hybrid spaces impact our decision to get vaccinated or not? To this end, in a new paper published in the International Journal of Geographical Information Science with Fuzhen Yin, Li Yin and myself, entitled “How information propagation in hybrid spaces affects decision-making: using ABM to simulate Covid-19 vaccine uptake” we explore this. 

More specially we explore how through opinion dynamics modeling, how agents can chose to vaccinate or not and how much emphasis they place on physical, relational and cyber spaces Using Chautauqua County in New York State as a case study our model results captures the temporal dynamics of vaccination progress with small errors but we also find that different age groups demonstrate various preferences for different spaces to receive vaccine related information. 

If this sounds of interest, below you can read the abstract of the paper, see a flow chart of the model logic and some of the results. While at the bottom of the post you can find the full reference and link to the paper. Furthermore, Fuzhen has also provided a detailed Overview, Design Concepts and Details Protocol (ODD) document along with the source code and data needed to run the model at CoMSES Net 

The notion of physical space has long been central in geographical theories. However, the widespread adoption of information and communication technologies (ICTs) has freed human dynamics from purely physical to also relational and cyber spaces. While researchers increasingly recognize such shifts, rarely have studies examined how the information propagates in these hybrid spaces (i.e., physical, relational, and cyber). By exploring the vaccine opinion dynamics through agent-based modeling, this study is the first that combines all hybrid spaces and explores their distinct impacts on human dynamics from an individual’s perspective. Our model captures the temporal dynamics of vaccination progress with small errors (MAE=2.45). Our results suggest that all hybrid spaces are indispensable in vaccination decision making. However, in our model, most of the agents tend to give more emphasis to the information that is spread in the physical instead of other hybrid spaces. Our study not only sheds light on human dynamics research but also offers a new lens to identifying vaccinated individuals which has long been challenging in disease-spread models. Furthermore, our study also provides responses for practitioners to develop vaccination outreach policies and plan for future outbreaks. 

Keywords: Agent-based modeling, hybrid space, opinion dynamics, Covid-19, vaccination. 

Flowchart of the modeling process. 

Comparing predicted and observed vaccination rates of all populations by giving physical, relational, cyber spaces different weights. Mean absolute error (MAE) and root mean square error (RMSE) are reported to evaluate the quality of predictions.

Comparing predicted and observed vaccination rates among different age groups by using the weight combination 3 (physical), 1 (relational), 1 (cyber) for hybrid spaces. 

Comparing predicted and observed vaccination rates by varying weights of hybrid spaces for different age groups.

Spatial distribution of Covid-19 vaccines. (a)-(d) Point density of vaccination allocation at different time steps. (e) Predicted vaccination rates at census block group level.

Full Referece:
Yin, F., Crooks, A.T. and Yin, L. (2024), How information propagation in hybrid spaces affects decision-making: using ABM to simulate Covid-19 vaccine uptake, International Journal of Geographical Information Science, (pdf)

Tuesday, April 30, 2024

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.

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).   

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. 

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.


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 


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)

Sunday, April 14, 2024

Geosimulations for Addressing Societal Challenges @ AAG 2024

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)

Chair: Na (Richard) Jiang


Session 2 (Date: 4/16/2024; Time: 1:20 PM - 2:40 PM;  Room: 312 (Ni`ihua), Third Floor, Hawai'i Convention Center)

Chair: Suzana Dragicevic


Thursday, April 11, 2024

Addressing equifinality in agent-based modeling

In the past we have blogged about the challenges of agent-based modeling but one thing we have not written much about is the challenge of uncertainty especailly when it comes to model calibration. This uncertainty is a challenge when it when it comes to situations where various parameter sets fit observed data equally well. This is known as equifinality which is a principle or phenomenon in system theory that implies that different paths can lead to the same final state or outcome. 

In a new paper with paper with Moongi Choi, Neng Wan, Simon Brewer, Thomas Cova and Alexander Hohl entitled "Addressing Equifinality in Agent-based Modeling: A Sequential Parameter Space Search Method Based on Sensitivity Analysis" we explore this issue. More specifically we introduce an Sequential Parameter Space Search (SPS) algorithm to confront the equifinality challenge in calibrating fine-scale agent-based simulations with coarse-scale observed geospatial data, ensuring accurate model selection using a pedestrian movement simulation as a test case.  

If this sounds of interest and you want to find out more, below you can read the abstract to the paper, see the logic of our simulation and some of the results. At the bottom of the page, you can find a link to the paper along with its full reference. Furthermore, Moongi has made the data and codes for indoor pedestrian movement simulation and Sequential Parameter Space search algorithm openly available at and


This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a novel sequential calibration approach. Equifinality arises when multiple models equally fit observed data, risking the selection of an inaccurate model. In the context of ABM, such a situation might arise due to limitations in data, such as aggregating observations into coarse spatial units. It can lead to situations where successfully calibrated model parameters may still result in reliability issues due to uncertainties in accurately calibrating the inner mechanisms. To tackle this, we propose a method that sequentially calibrates model parameters using diverse outcomes from multiple datasets. The method aims to identify optimal parameter combinations while mitigating computational intensity. We validate our approach through indoor pedestrian movement simulation, utilizing three distinct outcomes: (1) the count of grid cells crossed by individuals, (2) the number of people in each grid cell over time (fine grid) and (3) the number of people in each grid cell over time (coarse grid). As a result, the optimal calibrated parameter combinations were selected based on high test accuracy to avoid overfitting. This method addresses equifinality while reducing computational intensity of parameter calibration for spatially explicit models, as well as ABM in general. 

Keywords: Agent-based modeling equifinality calibration sequential calibration approach sensitivity analysis.

Detail model structures and process of the simulation.
Pedestrian simulation ((a) Position by ID, Grouped proportion – (b) 0.1, (c) 0.5, (d) 0.9).
Multiple sub-observed data ((a) # grid cells passed by each individual, (b) # individuals in 1x1 grid, (c) # individuals in 2x2 grid cells).

Validation results with train and test dataset ((a) Round 1, (b) Round 2, (c) Round 3).

Full Reference: 

Choi, M., Crooks, A.T., Wan, N., Brewer, S., Cova, T.J. and Hohl, A. (2024), Addressing Equifinality in Agent-based Modeling: A Sequential Parameter Space Search Method Based on Sensitivity Analysis, International Journal of Geographical Information Science. (pdf)

Wednesday, March 27, 2024

Community resilience to wildfires: A network analysis approach by utilizing human mobility data

Quantifying community resilience especially after a disaster is an open research challenge. However, with the growth in mobility datasets such as SafeGraph we are being given new opportunities to study how communities rebound from disaster.  

To this end, in a new paper with Qingqing Chen and Boyu Wang entitled "Community resilience to wildfires: A network analysis approach by utilizing human mobility data" which was published in Computers, Environment and Urban Systems we develop a framework to quantify resilience after a disaster using network analysis. To showcase this framework we us a human mobility data associated with two wildfires (Mendocino Complex and Camp wildfires) in California and measure the robustness and vulnerability of different communities over time. 

Our results show community resilience is closely tied to socio-economic and built environmental traits of the affected areas and as such our approach paves a way to study disasters and their long-term impacts on society. If this sounds of interest, below you can read the abstract to the paper, see some of the figures we use to explain and demonstrate our approach, while at the end of the post you can find the full reference along with a link to the paper. 

Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. 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, providing a new lens to study disasters and their long-term impacts on society.

Keywords: Wildfire, Community resilience, Network analysis, Resilience triangle, Human mobility data.   

Resilience triangle. (a) The original resilience triangle (adapted from Bruneau et al., 2003); (b) The modified resilience triangle used in this study.

An overview of the research outline.
The zoomed in study areas of the two wildfires, where the blue areas highlight the Census Block Groups; (b) The spatial distribution of wildfire density from 2005 to 2022; (c) The distribution of annual wildfires and acres in the U.S.
The distribution of degree centrality for each census block group colored by different clusters. (a) The Camp wildfire; (b) The Mendocino Complex wildfire.
The results of resilience triangles of clustered CBGs and resilience features. (a) The determined resilience triangles of clustered CBGs for Camp wildfire; (b) The determined resilience triangles of clustered CBGs for Mendocino Complex wildfire; (c) Vulnerability of CBGs within the two wildfire areas; (d) Robustness of CBGs within the two wildfires.

Full Reference: 
Chen, Q., Wang, B. and Crooks, A.T. (2024), Community Resilience to Wildfires: A Network Analysis Approach by Utilizing Human Mobility Data, Computers, Environment and Urban Systems, 110: 102110. (pdf)