CUPUM 2025

I have just gotten back from attending the 19th International Conference on Computational Urban Planning and Urban Management (CUPUM) in London and thought I would  share the two papers we presented at the conference. 

The first paper was with Qingqing Chen and Linda See and was entitled "Using New Sources of Data for Urban Climate Modeling Generated through MLLMs on Street View Imagery. "As the title might suggest, this paper was about how one can leverage multi-modal large language models (MLLMs) to extract information on building height, age and function from street level photographs. We demonstrate this using street view images from Mapillary and than ask ChatGPT to estimate the building height, age and function and compare the results to authoritative data sources. If this sounds of interest, below you can see the abstract to the paper, some if the figures (i.e., the work flow and prompts) while the results can be seen in the attached paper (see the link below).

Abstract:

Urban climate and energy balance models require data on the form and function of buildings, but high resolution spatially explicit data sets are often lacking. Here we demonstrate how multi-modal large language models (MLLMs) can be used to extract information on building height, age and function from street level photographs for New York City. A workflow is presented that illustrates the approach, with initial results indicating that the building function can be identified with good accuracy while moderate accuracies were obtained for building heights and age. Suggestions for how to improve these accuracies are also provided. 

KEYWORDS: Buildings, ChatGPT, Multi-modal Large Language Models (MLLMs), Mapillary, Street View Images (SVI).

An overview of research workflow.

The detailed description of multi-step prompting and an example of extracted building attributes information.

Full Reference:

Chen, Q., See, L. and Crooks, A.T. (2025), Using New Sources of Data for Urban Climate Modeling Generated through MLLMs on Street View Imagery. In Cramer-Greenbaum, S., Dennett, A., and Zhong, C (eds.), Proceedings of the 19th International Conference on Computational Urban Planning and Urban Management (CUPUM), London, UK. (pdf)

We then moved back to agent-based modeling with a paper with entitled "Enhancing Spatial Reasoning and Behavior in Urban ABMs with Large-Language Models and Geospatial Foundation Models" which brought back together Nick Malleson, Alison Heppenstall, Ed Manley and myself. In this paper we discuss the potential role of LLMs and geospatial foundation models in the context of agent-based modeling. If this sounds of interest, below you can read the abstract to the paper and find a link to it at the bottom of the post. Nick has also shared the slides of this presentation here. 

Abstract: 

Modeling human behavior continues to be a significant challenge for the field of agent-based modeling, and one that prohibits the development of comprehensive empirical ABMs for urban applications, such as Urban Digital Twins. However, two recent methodological advances offer the potential to transform empirical agent-based models.

Early evidence suggests that large-language models (LLMs) can be used to represent a wide range of human behaviors, with models responding in realistic ways to given prompts. Indeed there is already a flurry of activity that focusses on implementing LLM-backed agents -- i.e. agents who are controlled by LLMs. At the same time, the concept of the foundation model is also being applied in domains beyond text analysis. Of particular interest are geospatial foundation models that automatically encode spatial data in such a way as to associate different spatial objects in numerous and nuanced ways that have otherwise alluded manual classification schemes. Taken together, these two technologies offer considerable potential for a new generation of agent-based models that contain agents who can behave in response to spatial and social prompts in a way that is realistic and has so far proven impossible to replicate using manually-programmed behavioral rules.

This paper presents a discussion of the state of the art in both LLMs and geospatial foundation models in the context of their potential role in agent-based modelling. It discusses the transformational potential of these technologies and outlines the critical questions that need to be addressed before they can be used to create robust, reliable and trustworthy models for empirical policy applications that support decision-making.

KEYWORDS: Agent-based Modeling; Large language model; Geospatial foundation model; Urban Modeling.

Full Reference:

Malleson, N., Crooks, A.T., Heppenstall, A. and Manley, E. (2025), Enhancing Spatial Reasoning and Behavior in Urban ABMs with Large-Language Models and Geospatial Foundation Models. In Cramer-Greenbaum, S., Dennett, A., and Zhong, C (eds.), Proceedings of the 19th International Conference on Computational Urban Planning and Urban Management (CUPUM), London, UK. (pdf)

Talks: ABM, AI and other Thoughts

This is a slightly different post to normal, in the sense its not really about papers but my take on agent-based modeling, urban analytics and the growth of Artificial Intelligence impacting both. 

First up, while I was in Santa Fe last October for the 2024 International Conference of the Computational Social Science Society of the Americas  I was interviewed by John Cordier from Epistemix for their Flux Podcast which resulted in this "From Micro-Behaviors to Macro-Patterns: Exploring Agent-Based Models with Andrew Crooks. Rather than me trying to sum it up I will just quote from the podcast episode 

"In this episode of The Flux, host John Cordier sits down with Andrew Crooks ..... They dive into the world of agent-based modeling (ABM) - what it is, why it matters, and how it helps us simulate and better understand human behavior in complex systems. From simulating traffic jams to modeling social influence on vaccine uptake, Andrew shares how data, geography, and synthetic populations are revolutionizing our ability to forecast and inform decisions. They also explore the growing role of AI tools in democratizing modeling, the evolution of computational capabilities, and even ask: what if we had run a simulation before Brexit?"

If this sounds of interest, you can listen to the full podcast here. 

Next up, I was asked to give a talk back in late May to give a seminar talk at the Department of Geography and Spatial Sciences (GSS) at the University at Delaware hosted by Yao Hu. The title of the talk was "Monitoring and Analyzing Cities through the Lens of Urban Analytics" In this talk I reflect what urban analytics means to me and how the field is changing. If this sounds of interest, below you can read the abstract to my talk and also see the recording. However, before ending this I would really like to thank Yao for hosting me, and the others from the GSS and the universty at large for making it a great visit and being an engaged audience. 

Abstract: 

For the first time in human history, more people are living in cities than rural areas and this trend is only expected to grow in the coming decades. This growth will place unprecedented challenges on cites with respect to sustainable development especially in light of climate change and increasing populations. One way to explore and understand cities is through the lens of urban analytics, a set of methods that allow us to monitor, analyze and model urban areas. This talk will explore how urban analytics has changed over time and showcase how our understanding of cities has benefited from it. I will showcase how new sources of data can be used to monitor and analyze cities and how in turn these can be integrated into models to explore various aspects of city life from pedestrian movement to urban growth. The talk will conclude with a discussion and demonstration of how artificial intelligence can be integrated into the urban analytics toolbox and what opportunities and challenges it poses.

Also in late May, Alison Heppenstall, and myself were interviewed by Dr. Andy Collins discussing as part of the Computational Social Science Society of the Americas (CSSSA) webinar series on Agent-based modeling and simulation (ABMS). To quote from CSSSA, the purpose of these webinars is that: 

"Agent-based modeling and simulation (ABMS) has been applied far and wide to better understand our world. Each new application domain brings with it existing cultures of the domain's experts, including expectations and requirements. As such, it is foolhardy to expect agent-based modeling to be standardized across all domains. As practitioners, there is a desire to understand how these domain cultures differ, how they use agent-based modeling, and what the future of agent-based modeling is within those domains. To start to grapple with these grand questions, for the ABMS community, we are proposing to run a series of interviews with experts from different domains to try to map the world of agent-based modeling."

Readers, might not be surprised but we were asked to discuss ABM in the context of geography. So if you want to hear us discuss ABM and geography, you can see the talk below. It should also be noted the CSSSA has a whole host of other webinars on their YouTube Channel. 

Finally, at the start of May, I was invited to give one of the keynotes at the Inaugural AI and Cities: An International Forum for Innovation and Collaboration hosted by University of Florida entitled "Artificial intelligence and Urban Analytics: Opportunities and Challenges."  This talk is slightly different from the others as the focus was more on AI, so if you are wondering what my take on AI is (or my current research), you can read the abstract to the talk below and also find a link to the recording of it. 

Abstract: Urban areas now provide homes for more people than ever before, and with more and more people living in cities achieving sustainable cities is crucial for the betterment of all. Coinciding with the growth of the world’s population is the growth of artificial intelligence (AI) is which is becoming pervasive in all aspects of our daily lives. In this talk I will discuss how AI is offering us new opportunities when it come studying cities, specifically, through the lens of urban analytics. Urban analytics can be broadly defined a set of methods to explore, understand and predict the properties of cities. Through a series of examples, I will highlight how AI especially through the use of multimodal large language models (LLMs) is offering accessible methods for geographic information extraction and modeling of cities. I will showcase how AI can improve the granularity of urban data collection while at the same time provides more advanced GIS tools to practitioners in a more accessible and user-friendly way. However, AI alone is not the panacea when it comes to archiving urban sustainability and many challenges exist and the talk with conclude with these.

If the abstract sounds interesting click here to watch the talk.  Also the other keynotes talks are also available online here. 

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 https://www.comses.net/codebase-release/8967d4ca-9199-4ca8-be49-cab6d14db12c/. 

Abstract 

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, https://doi.org/10.1080/13658816.2024.2333930 (pdf)

ABM Online Courses

Often I get asked about how to learn about agent-based modeling (ABM). While we have a book on this with respect to GIS and ABM, the other day, Jiaqi Ge posted a question about free ABM online courses on the SIMSOC mailing list and I though it would be worth summarizing the responses here as the resources are quite useful.

Jiaqi shared some really good resources like the Santa Fe Institutes "Introduction to Agent-Based Modeling" and "Fundamentals of NetLogo" along with the University of Geneva's Coursera course "Simulation and modeling of natural processes". 

 

Others also responded to the question. For example, Wander Jager responded with online modules developed from the Action for Computational Thinking in Social Sciences (ACTiSS) team. Jen Badham responded with an extended tutorial about model design and creating models in Netlogo while Dino Carpentras responded with several general videos on YouTube on ABM which he has created. Hopefully readers will find these useful and also you might want to see our Github pages on GIS and ABM. 

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: http://cs.gmu.edu/~eclab/projects/mason/.

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. 

 

Urban life: A model of people and places

We have just wrapped up project that created a simple agent-based simulation of urban life as part of DARPA's Ground Truth Program. To this end we have just published a  new paper entitled "Urban life: a model of people and places" published in Computational and Mathematical Organization Theory, with Andreas Züfle, Carola Wenk, Dieter Pfoser, Joon-Seok Kim, Hamdi Kavak, Umar Manzoor, Hyunjee Jin  and myself. In the paper we provide an overview of the model and how it was used to test and validate human domain research. For interested readers, below you can find the abstract  to the paper along with some images that will give you a sense of our simulation model (which for interested readers was created with MASON and its GIS extension (GeoMason). While at the bottom of the post you can find the full reference and a link to the paper. 

 Abstract

We introduce the Urban Life agent-based simulation used by the Ground Truth program to capture the innate needs of a human-like population and explore how such needs shape social constructs such as friendship and wealth. Urban Life is a spatially explicit model to explore how urban form impacts agents’ daily patterns of life. By meeting up at places agents form social networks, which in turn affect the places the agents visit. In our model, location and co-location affect all levels of decision making as agents prefer to visit nearby places. Co-location is necessary (but not sufficient) to connect agents in the social network. The Urban Life model was used in the Ground Truth program as a virtual world testbed to produce data in a setting in which the underlying ground truth was explicitly known. Data was provided to research teams to test and validate Human Domain research methods to an extent previously impossible. This paper summarizes our Urban Life model’s design and simulation along with a description of how it was used to test the ability of Human Domain research teams to predict future states and to prescribe changes to the simulation to achieve desired outcomes in our simulated world.

Our generated maps colored based on different aggregation levels.

A screenshot of the graphical user interface from a representative model run. Top-Left: The spatial network and agents. Bottom left: Simulation parameters that can be specified prior to simulation start. Top-middle: the social network. Bottom-middle: Summary statistics of the simulation during tun-time such as friendship. Right: Profiles of recreational sites.

Screenshot of the epidemic simulator depicting the French Quarter, New Orleans, LA, USA.

Full Reference:

Züfle, A., Wenk, C., Pfoser, D., Crooks, A.T., Kavak, H., Kim, J-S. and Jin, H. (2021), Urban Life: A Model of People and Places, Computational and Mathematical Organization Theory. Available at https://doi.org/10.1007/s10588-021-09348-7 (pdf)

GIS and ABM: Past, Present and Future

The other day, Alison Heppenstall and myself were invited to give a keynote at the 2021 International Conference on Geospatial Information Sciences. Its not hard to guess what we chose to be the title of our talk: "GIS and Agent-Based-Modelling: Past, Present and Future."

The talk was a synthesis of two publications (Crooks et al. 2019 and Heppenstall et al. 2021) along with some things we are currently working on. For those who are interested the conference has released not only our talk but also the other keynotes.

Kinetic Action and Radicalization

In the past we had posted on models of radicalization, but such models were rather abstract.  However in a recent paper entitled "Kinetic Action and Radicalization: A Case Study of Pakistan" which was presented at the  International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS for short) we take such work a step further. 

In the paper, Brandon Shapiro and myself develop and present a simple agent-based model informed by theory and calibrated using empirical data to explore the relationship between kinetic actions (i.e., drone strikes) and terrorist attacks in Pakistan from 2004 through 2018. The data itself came from the Bureau of Investigative Journalism data as our source for Pakistan drone strikes (i.e., kinetic actions) and the National Consortium for the Study of Terrorism and Responses to Terrorism( START)  Global Terrorism Database (GTD) as our source for terrorist incidents. 

Rather than try to pinpoint and define the motivating factors which might influence somebody down a path toward radicalization, our model that incorporated a distributed lag model to characterize the inter-dependencies between drone strikes and terrorist attacks observed in Pakistan. Based on parametric and validation tests, the model simulates a terrorist attack curve which approximates the rate and magnitude observed in Pakistan from 2007 through 2018. If this sounds of interest, below we provide the abstract to the paper, along with some images of model graphical user interface, the model logic and some of the results. The model itself was created in NetLogo and is available at: https://bit.ly/3qLJynv  (along with the data and detailed ODD of the model). At the bottom of the page you can find the full citation and a link to the paper.

Abstract. Drone strikes have been ongoing and there is a debate about their benefits. One major question is what is their role with respect to radicalization. This paper presents a data-driven approach to explore the relationship between drone strikes in Pakistan and subsequent responses, often in the form of terrorist attacks carried out by those in the communities targeted by these counter-terrorism measures. Our analysis of news reports which dis-cussed drone strikes and radicalization suggests that government-sanctioned drone strikes in Pakistan appear to drive terrorist events with a distributed lag that can be determined analytically. We then utilize these news reports to inform and calibrate an agent-based model which is ground-ed in radicalization and opinion dynamics theory. In doing so, we were able to simulate terrorist attacks that approximated the rate and magnitude ob-served in Pakistan from 2007 through 2018. We argue that this research effort advances the field of radicalization and lays the foundation for further work in the area of data-driven modeling and kinetic actions.

Keywords: Radicalization, Data-driven modeling, Drone strikes, Terrorism, Pakistan, Agent-based modeling.

Radicalization model’s graphical user interface.

The agent-based model flow diagram.

Terrorist attacks simulated by radicalization model qualitatively agree with real-world system.

Full Reference:

Shapiro, B. and Crooks, A.T. (2021), Kinetic Action and Radicalization: A Case Study of Pakistan, in Thomson, R., Hussain, M.N., Dancy, C.L. and Pyke, A. (eds), Proceedings of 2021 International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp 321-330. (pdf)

Learning-based Actor-Interpreter State Representation

While in previous posts we have discussed machine learning (ML) with respect to social media analysis, we have also been exploring how one can use it in agent-based modeling. One of the first examples of this is a new paper with Paul Cummings which has been accepted at the upcoming 2020 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS 2020 for short).

In the paper entitled "Development of a Hybrid Machine Learning Agent Based Model for Optimization and Interpretability" we discuss the growth of ML within agent-based models and present the design of the hybrid agent-based/ML model called the Learning-Driven Actor-Interpreter Representation (LAISR) Model. LAISR's attempts to: "a) generate an optimal decision-making strategy through its training process, including a more constrained parameter space, and b) describe its behaviors in a human-readable and interpretable approach." To demonstrate the LAISR's model we use a simple wargaming example, that of a tactical air and ground warfare as an experiment and discuss areas of further research and applications.

If this is of interest to you, below we provide the abstract to the paper, along with a high level overview of the LAISR's model, its tactical experiment diagram and some results from the wargaming experiment. This is followed by a short movie of a representative model run that Paul has created. Finally at the the bottom of the post you can see the full reference and a link to the paper itself.

Abstract.

The use of agent-based models (ABMs) has become more wide-spread over the last two decades allowing researchers to explore complex systems composed of heterogeneous and locally interacting entities. How-ever, there are several challenges that the agent-based modeling community face. These relate to developing accurate measurements, minimizing a large complex parameter space and developing parsimonious yet accurate models. Machine Learning (ML), specifically deep reinforcement learning has the potential to generate new ways to explore complex models, which can enhance traditional computational paradigms such as agent-based modeling. Recently, ML algorithms have proved an important contribution to the de-termination of semi-optimal agent behavior strategies in complex environments. What is less clear is how these advances can be used to enhance existing ABMs. This paper presents Learning-based Actor-Interpreter State Representation (LAISR), a research effort that is designed to bridge ML agents with more traditional ABMs in order to generate semi-optimal multi-agent learning strategies. The resultant model, explored within a tactical game scenario, lies at the intersection of human and automated model design. The model can be decomposed into a format that automates aspects of the agent creation process, producing a resultant agent that creates its own optimal strategy and is interpretable to the designer. Our paper, therefore, acts as a bridge between traditional agent-based modeling and machine learning practices, designed purposefully to enhance the inclusion of ML-based agents in the agent-based modeling community.

Keywords: Agent-Based Modeling, Machine Learning, Explainable Artificial Intelligence.

LAISR model

LAISR tactical experiment diagram (A). Actor finite state machine (B).

Heat map representations of actor agents.

Full Reference:

Cummings, P. and Crooks, A.T. (2020), Development of a Hybrid Machine Learning Agent Based Model for Optimization and Interpretability, in Thomson, R., Bisgin, H., Dancy, C., Hyder, A. and Hussain, M. (eds), 2020 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Washington DC., pp 151-160. (pdf)

Location-Based Social Simulation for Prescriptive Analytics of Disease Spread

Building upon our previous work on Location-Based Social Networks (LBSNs) and how agent-based modeling could provide an alternative to real world data sets, in the latest SIGSPATIAL Special Newsletter, we (Joon-Seok Kim, Hamdi Kavak, Chris Rouly, Hyunjee Jin, Dieter Pfoser, Carola Wenk, Andreas Zufle and myself) have an article entitled "Location-Based Social Simulation for Prescriptive Analytics of Disease Spread."

In this article we discuss a geographically explicit agent-based model that we have been developing that is capable not only of simulating human behavior but also able to create synthetic but realistic LBSN data based on human patterns-of-life. Furthermore, in the article we discuss how such data and models can be used to explore the parameter space of possible prescriptions to find optimal strategies (or policies) to achieve a desired system state and outcome. We refer to such a search for optimal policies as prescriptive analytics. (for readers wishing to learn more about prescriptive analytics please see the 1st ACM KDD Workshop on Prescriptive Analytics for the Physical World).

To give an example of such prescriptions, in the article we make use of a simple hypothetical disease model and explore two prescribed policies to mitigate the spread of the disease. The first policy requires all agents to wear simulated Personal Protective Equipment (PPE) that reduce the chance of infection by 50%. The second policy enforces strict social distancing measures onto a fixed proportion of 50% of the population. Those who follow the social distancing order avoid recreational site visits from meeting people although they still go to restaurants. In addition to these two policies, as a baseline, we also ran a “null-prescription” in which no intervention was prescribed. We find that the social distancing prescription was extremely effective. On the other hand, our simulation results for PPE policy showed that merely wearing protective gear without any change in behavior has no significant effect (for the case of this disease).

If this type of research is of interest to you, below we provide the abstract to the paper, a movie of a representative simulation run, some of our results of the prescriptions described above and a link to the paper itself. Further information about the model and data can be found at https://geosocial.joonseok.org/p/epidemic.html and the data is available at https://osf.io/e24th/. Also as we are currently going through COVID-19, we thought a a brief write up and links to some disease models and discussions of modeling efforts related to it was also appropriate to include.

Abstract: 

Human mobility and social networks have received considerable attention from researchers in recent years. What has been sorely missing is a comprehensive data set that not only addresses geometric movement patterns derived from trajectories, but also provides social networks and causal links as to why movement happens in the first place. To some extent, this challenge is addressed by studying location-based social networks (LBSNs). However, the scope of real-world LBSN data sets is constrained by privacy concerns, a lack of authoritative ground-truth, their sparsity, and small size. To overcome these issues we have infused a novel geographically explicit agent-based simulation framework to simulate human behavior and to create synthetic but realistic LBSN data based on human patterns-of-life (i.e., a geo-social simulation). Such data not only captures the location of users over time, but also their motivation, and interactions via temporal social networks. We have open sourced our framework and released a set of large data sets for the SIGSPATIAL community. In order to showcase the versatility of our simulation framework, we added disease a model that simulates an outbreak and allows us to test different policy measures such as implementing mandatory mask use and various social distancing measures. The produced data sets are massive and allow us to capture 100% of the (simulated) population over time without any data uncertainty, privacy-related concerns, or incompleteness. It allows researchers to see the (simulated) world through the lens of an omniscient entity having perfect data.

Screenshot of the epidemic simulator depicting the French Quarter, New Orleans, LA, USA.

New cases and SEIR epidemic course.

Full Reference:

Kim, J-S., Kavak, H., Rouly, C.O., Jin, H., Crooks, A.T., Pfoser, D., Wenk, C. and Zufle, A. (2020), Location-Based Social Simulation for Prescriptive Analytics of Disease Spread, SIGSPATIAL Special, 12(1): 53-61. (pdf)

The Washington Post's Disease Model

While this post is not about COVID per se, if you are interested in disease models the Washington Post had a great article about COVID several months ago entitled "Why outbreaks like corona virus spread exponentially, and how to “flatten the curve”." This article generated a lot of discussion such as on the SIMSOC Mailing list and was citied in a paper in the Journal of Artificial Societies and Social Simulation (JASSS) entitled  "Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action." Other goods discussions on COVID related models (particularly agent-based models) can be found on Review of Artificial Societies and Social Simulation (RofASSS) website (here), the CoMSES Net Discourse Forum (along with links to past epidemic models) and the Sociology and Complexity Science Blog has some very good posts on modeling and public health.

Utilizing Agents To Explore Urban Shrinkage

While more people are living in urban areas than ever before, and this is expected to grow in the coming decades, this growth is not equal. Some cities are actually shrinking, such as Detroit in the United States. The causes of urban shrinkage have been the source of much debate but can be broadly attributed to a combination of factors relating to deindustrialization, suburbanization (i.e., urban sprawl), and demographic withdrawal. The result of shrinking cities, especially in and around the traditional downtown core of the city results in many problems, such as population loss, economic depression (due to loss in tax revenue), a growth in vacant properties, and the contraction of the land and housing markets.

To explore this phenomena, at the upcoming 2020 Spring Simulation Conference we have a paper entitled "Utilizing Agents To Explore Urban Shrinkage: A Case Study Of Detroit." The motivation for this paper is to explore the housing market in a shrinking city from the micro-level, specifically based on individuals trading interactions via an agent-based model stylized on spatially explicit data of Detroit Tri-county area. Our agent-based model demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to alleviate this issue. For readers wishing to know more about this work, below we provide the abstract to the paper, some figures sketching out some of model logic,  a sample of results and a movie of a representative model run. Similar to our other works, we have a more detailed description of the model following the Overview, Design concepts, and Details (ODD) protocol along with the source code and data needed to run the model at: http://bit.ly/UrbanShrinkage. We do this to aid replication and for others to extend if they see fit. As normal, any thoughts or comments are most welcome.

Abstract:

While the world’s total urban population continues to grow, this growth is not equal. Some cities are declining, resulting in urban shrinkage which is now a global phenomenon. Many problems emerge due to urban shrinkage including population loss, economic depression, vacant properties and the contraction of housing markets. To explore this issue, this paper presents an agent-based model stylized on spatially explicit data of Detroit Tri-county area, an area witnessing urban shrinkage. Specifically, the model examines how micro-level housing trades impact urban shrinkage by capturing interactions between sellers and buyers within different sub-housing markets. The stylized model results highlight not only how we can simulate housing transactions but the aggregate market conditions relating to urban shrinkage (i.e., the contraction of housing markets). To this end, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to alleviate this issue.

Keywords: Urban Shrinkage, Housing Markets, Detroit, Agent-based Modeling, GIS 

Agents Decision Making Process.

The sequences of all function events in the model are displayed by this UML diagram, which demonstrates the model flow, dynamic and interaction among the different components of the model.

Average Results where: (a) demand exceeds supply; (b) equal demand and supply; (c) supply exceeds demand for each different housing sub market.

Reference:

Jiang, N. and Crooks, A.T. (2020), Utilizing Agents to Explore Urban Shrinkage: A Case Study of Detroit, 2020 Spring Simulation Conference (SpringSim’20), Fairfax, VA. (pdf)

Class Model Examples

Avid readers of this blog (if there are any) might have noticed at the end of each semester I do a post pertaining to class models from the various courses I teach. This often involves a short movie of some of these models like the one below.

Often I get asked about these models are, so finally I have complied a selection of them on GitHub: https://github.com/acrooks2/ClassModels. These are only NetLogo  models (for the time being) as I use it as a way of introducing students to agent-based modeling and programing.  As noted on the readme of the repository these models come as is. What explanations there are is given in the readme file for each model (these mainly come in the form of abstracts from the papers that were submitted with the models). No further explanations, support etc. will be given and are only provided to show the range of problems agent-based models can be used to explore. I also need to acknowledge all the students who submitted the models, you know who you are! This project would not be possible without you!

Maybe one day I will also get around to containerizing some of these models. For those interested containerization and how to do this for NetLogo models, https://www.comses.net/ has a great tutorial on this (click here for further details).

Examples of the types of GIS and agent-based modeling projects.

New Working Paper: Agent-Based Models for Geographical Systems: A Review

Its been a while since we published a working paper, especially a CASA one, but this has now changed with the release of a new one entitled "Agent-Based Models for Geographical Systems: A Review." In the paper Alison Heppenstall, Nick Malleson, Ed Manley, Jiaqi Ge, Michael Batty and myself reflect back on the agent-based modeling and their use in geographical systems. 

In the paper we revisit challenges that we first explored back in 2008 (which an earlier version was another CASA working paper) and progress that has been made to address them. We then  explore new challenges within the field of agent-based models especially in light of new new (big) data along with new opportunities (such as data assimilation). If you want to find out more about the paper, below is the abstract and a ling to the paper.

Abstract:

This paper charts the progress made since agent-based models (ABMs) of geographical systems emerged from more aggregative approaches to spatial modeling in the early 1990s. We first set the context by noting that ABM explicitly represent the spatial system by individual objects, usually people in the social science domain, with behaviors that we simulate here mainly as decisions about location and movement. Key issues pertaining to the way in which temporal dynamics characterize these models are noted and we then pick up the challenges from the review of this field conducted by Crooks, et al. (2008) some 12 years ago which was also published as a CASA working paper. We then define key issues from this past review as pertaining to a series of questions involving: the rationale for modeling; the way in which theory guides models and vice versa; how models can be compared; questions of model replication,experiment, verification and validation; how dynamics are incorporated in models; how agent behaviors can be simulated; how such ABMs are communicated and disseminated; and finally the data challenges that still dominate the field. This takes us to the current challenges emerging from this discussion. Big data, the way it is generated, and its relevance for ABM is explored with some important caveats as to the relevance of such data for these models, the way these models might be integrated with one another and with different genera of models are noted, while new ways of testing such models through ensemble forecasting and data assimilation are described. The notion about how we model human behaviors through agents learning in complex environment is presented and this then suggests that ABM still have enormous promise for effective simulations of how spatial systems evolve and change.

Full Reference:

Heppenstall, A., Crooks, A.T., Malleson, N., Manley, E., Ge J. and Batty, M. (2019), Agent-Based Models for Geographical Systems: A Review. Centre for Advanced Spatial Analysis (University College London): Working Paper 214, London, England. (pdf)

Modeling Homeowners Post-flood Reconstruction Decisions

In the past we have developed agent-based models to explore a wide variety of applications and even to explored at humanitarian assistance after a natural disaster, however we have not explored how people might decide to rebuild or not after a natural disaster. Well that was until now. In a new paper with Kim McEligot, Peggy Brouse and myself entitled "Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy" which was published as part of the 2019 Winter Simulation Conference. In the paper we use a hindcast (aka backtesting) of Hurricane Sandy’s damage to Sea Bright, NJ and explore homeowners post-flood reconstruction decisions. Below we provide the abstract to the paper, a short movie of the model running, along with a link to access the source code and data of the model, and finally a link to the full paper.

Abstract:

Coastal flooding is the most expensive type of natural disaster in the United States. Policy initiatives to mitigate the effects of these events are dependent upon understanding flood victim responses at an individual and municipal level. Agent-Based Modeling (ABM) is an effective tool for analyzing community-wide responses to natural disaster, but the quality of the ABM’s performance is often challenging to determine. This paper discusses the complexity of the Protective Action Decision Model (PADM) and Protection Motivation Theory (PMT) for human decision making regarding hazard mitigations. A combined (PADM/PMT) model is developed and integrated into the MASON modeling framework. The ABM implements a hind-cast of Hurricane Sandy’s damage to Sea Bright, NJ and homeowner post-flood reconstruction decisions. It is validated against damage assessments and post-storm surveys. The contribution of socio-economic factors and built environment on model performance is also addressed and suggests that mitigation for townhouse communities will be challenging.

The model source code (utilizing MASON Version 17) and data is available on CoMSES.net: http://bit.ly/SEABrightABM.

Our adaptation of the Protection Motivation Theory and Protective Action Decision Model.

Full Reference:

McEligot, K. Brouse, P. and Crooks A.T. (2019), Sea Bright, New Jersey Reconstructed: Agent-Based Protection Theory Model Responses to Hurricane Sandy, in Mustafee, N., Bae, K.-H.G., Lazarova-Molnar, S., Rabe, M., Szabo, C., Haas, P. and Son, Y-J. (eds.), Proceedings of the 2019 Winter Simulation Conference, National Harbor, MD, pp 251-262 (pdf)