Tuesday, December 19, 2023

Crowdsourcing Dust Storms in the United States Utilizing Flickr

In the past on this site we have written about how one can use social media to study the world around us. Often the focus has been on Twitter but that is not the only social media platform available.  Another is Flickr, and while in past posts have show how we can use this platform to explore bird sightings, wildfires and human migration we are now turning our attention to other phenomena. One of which is dust storms. Working with Festus Adegbola and Stuart  Evans we have just presented a poster at the 2023 American Geophysical Union Fall Meeting entitled "Crowdsourcing Dust Storms in the United States Utilizing Flickr"

In this research we compare Flickr images with National Weather Service  advisories and the VIIRS Deep Blue aerosol product data from the Suomi-NPP satellite. Our preliminary findings show that Flickr images of dust storms have a substantial co-occurrence with regions of NWS blowing dust advisories. If this sounds of interest, below you can read our abstract, see our workflow and the poster itself. 


Dust storms are natural phenomena characterized by strong winds carrying large amounts of fine particles which have significant environmental and human impacts. Previous studies have limitations due to available data, especially regarding short-lived, intense dust storms that are not captured by observing stations and satellite instruments. In recent years, the advent of social media platforms has provided a unique opportunity to access a vast amount of user-generated data. This research explores the utilization of Flickr data to study dust storm occurrences within the United States and their correlation with National Weather Service (NWS) advisories. The work ascertains the reliability of using crowdsourced data as a supplementary tool for dust storm monitoring. Our analysis of Flickr metadata indicates that the Southwest is most susceptible to dust storm events, with Arizona leading in the highest number of occurrences. On the other hand, the Great Plains show a scarcity of Flickr data related to dust storms, which can be attributed to the sparsely populated nature of the region. Furthermore, seasonal analysis reveals that dust storm events are prevalent during the Summer months, specifically from June to August, followed by Spring. These results are consistent with previous studies of dust occurrence in the US, and Flickr-identified images of dust storms show substantial co-occurrence with regions of NWS blowing dust advisories. This research highlights the potential of unconventional user-generated data sources to crowdsource environmental monitoring and research.

Data collection and workflow.
Distribution of Flickr identified dust storm occurrences and NWS dust storm advisories.

Full Reference: 

Adegbola, F., Crooks, A.T. and Evans, S. (2023), Crowdsourcing Dust Storms in the United States Utilizing Flickr, American Geophysical Union (AGU) Fall Meeting, 11th – 15th December, San Francisco, CA. (abstract, poster)

Tuesday, November 14, 2023

Massive Trajectory Data Based on Patterns of Life

Following on from the last post, we (Hossein AmiriShiyang RuanJoon-Seok KimHyunjee JinHamdi KavakDieter PfoserCarola Wenk and Andreas Zufle and myself) have a paper in the Data and Resources track at the 2023 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems entitled "Massive Trajectory Data Based on Patterns of Life".  

This Data and Resources paper introduces readers to a large sets of simulated individual-level trajectory and location-based social network data we have generated from our Urban Life Model (click here to find out more about the model). The data comprises of 4 suburban and urban regions, including 1) the George Mason University Campus area, Fairfax, Virginia, 2) the French Quarter of New Orleans, Louisiana, 3) San Francisco, California, and 4) Atlanta, Georgia. For each of the 4 study regions, we run the simulation with 1K, 3K, 5K, and 10K agents for 15 months of simulation time. We also provide simulations for 10 years and 20 years, having 1K agents for each of the 4 regions of interest. For each dataset, three items are provided: 1) Check-ins, and 2) social network links and 3) trajectory information per agent per five-minute tick. As such we argue in the paper that our datasets are orders of magnitude larger than existing real-world trajectory and location-based social network (LBSN) data sets. 

If this sounds of interest we encourage readers to check out the paper (see the bottom of this post), while the datasets, as well as additional documentation, can be found at OSF (https://osf.io/gbhm8/) and the data generator (model) can be found at https://github.com/azufle/pol.

Abstract: Individual human location trajectory and check-in data have been the driving force for human mobility research in recent years. However, existing human mobility datasets are very limited in size and representativeness. For example, one of the largest and most commonly used datasets of individual human location trajectories, GeoLife, captures fewer than two hundred individuals. To help fill this gap, this Data and Resources paper leverages an existing data generator based on fine-grained simulation of individual human patterns of life to produce large-scale trajectory, check-in, and social network data. In this simulation, individual human agents commute between their home and work locations, visit restaurants to eat, and visit recreational sites to meet friends. We provide large datasets of months of simulated trajectories for two example regions in the United States: San Francisco and New Orleans. In addition to making the datasets available, we also provide instructions on how the simulation can be used to re-generate data, thus allowing researchers to generate the data locally without downloading prohibitively large files.

Full Referece: 

Amiri, H., Ruan, S., Kim, J., Jin, H., Kavak, H., Crooks, A.T., Pfoser, D., Wenk, C. and Züfle, A. (2023), Massive Trajectory Data Generation using a Patterns of Life Simulation, Proceedings of the 2023 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Hamburg, Germany. (pdf)

Monday, November 13, 2023

Synthetic Geosocial Network Generation

In the past the blog has explored the creation of social networks for models. Keeping with this vain of research, I was fortunate to work with Ketevan GallagherTaylor Anderson and Andreas Züfle to consider the role of location of individuals when generating social networks. This work has resulted in a new paper entitled "Synthetic Geosocial Network Data Generation"  which was presented at the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec 2023). If this sounds of interest, below you can read the abstract to the paper, see some the generated geosoical networks and find the full reference and link to the paper. In addition to this, the Python code and data used to generate the networks is available at https://github.com/KetevanGallagher/Synthetic-Geosocial-Networks.

Abstract: Generating synthetic social networks is an important task for many problems that study humans, their behavior, and their interactions. Geosocial networks enrich social networks with location information. Commonly used models to generate synthetic social networks include the classical Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz models. However, these classic social network models do not consider the location of individuals. Real-world geosocial networks do exhibit a strong spatial autocorrelation, thus having a higher likelihood of a social connection between agents that are spatially close. As such, recent variants of the three classical models have been proposed to consider location information. Yet, these existing solutions assume that individuals are located on a uniform lattice and exhibit certain limitations when applied to real-world data that exhibits clusters. In this work, we discuss these limitations and propose new approaches to extend the three classic social network generation models to geosocial networks. Our experiments show that our generated synthetic geosocial networks address the shortcomings of the state-of-the-art models and generate realistic geosocial networks that exhibit high similarity to real-world geosocial networks. 
Keywords: Geosocial Networks, Network Generation, Synthetic Social Networks, Erdos-Renyi, Watts-Strogatz, Barabasi-Albert.

Real- World Geosocial Network using Facebook Social Connectedness Data between Zone Improvement Plan (ZIP) Region Centroids for the State of Virginia, USA.
Geosocial graphs using Virginia ZIP code data.
Graphs using Fairfax Census Tract data.

Full Referece:
Gallagher, K., Anderson, T., Crooks, A.T. and Züfle, A. (2023), Synthetic Geosocial Network Data Generation, Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising (LocalRec 2023), Hamburg, Germany. (pdf) (presentation)

Friday, November 03, 2023

Geographically Synthetic Populations for ABM: A Gallery of Applications

Often we are building geographically explicit agent-based models we spend a lot of time creating the synthetic population to instantiate our artificial world. We have tired to overcome this with creating methods to generate such populations (see this old blog post). Building on this work, Na (Richard) Jiang, Fuzhen Yin, Boyu Wang and myself have a new paper entitled "Geographically-Explicit Synthetic Populations for Agent-based Models: A Gallery of Applications" which was presented at 2023 Computational Social Science Society of the Americas conference. In the paper we extend the synthetic population to the whole of New York state. While at the same time we introduce a pipeline for using the population datasets for model initialization. To show this pipeline, we present several case studies utilizing Python and Mesa. These models range from that of commuting to disease spread and vaccination uptake. If this sounds of interest, below we provide the abstract to the paper along with some of the key figures including our pipeline and example applications. At the bottom of the page we provide the full reference and a link to the paper which has links to the models and data.
Abstract: Over the last two decades, there has been a growth in the applications of geographically-explicit agent-based models. One thing such models have in common is the creation of synthetic populations to initialize the artificial worlds in which the agents inhabit. One challenge such models face is that it is often difficult to create reusable geographically-explicit synthetic populations with social networks. In this paper, we introduce a Python based method that generates a reusable geographically-explicit synthetic population dataset along with its social networks. In addition, we present a pipeline for using the population datasets for model initialization. With this pipeline, multiple spatial and temporal scales of geographically-explicit agent-based models are presented focusing on Western New York. Such models not only demonstrate the utility of our synthetic population on commuting patterns but also how social networks can impact the simulation of disease spread and vaccination uptake. By doing so, this pipeline could benefit any modeler wishing to reuse synthetic populations with realistic geographic locations and social networks. 
Keywords: Agent-Based Model, Geographically-Explicit Agent-Based Models, Synthetic Population, Python, Mesa.
Pipeline of Utilizing Synthetic Population Resulting Datasets in Agent-Based Models.

Large Scale Disease Spread Model Structure.

Disease Dynamics for Two Diseases.

Vaccination Opinion Dynamic Model.

Simulation Vaccination Rate v.s. Real Vaccination Records: (A) All Population; (B) Different Age Groups of Population.

Full Referece: 

Jiang, N., Crooks, A.T., Yin, F. and Wang B. (2023), Geographically-Explicit Synthetic Populations for Agent-based Models: A Gallery of Applications, Proceedings of the 2023 Conference of The Computational Social Science Society of the Americas, Santa Fe, NM. (pdf)

Monday, October 23, 2023

Evaluating the incentive for soil organic carbon sequestration from carinata production

Over the years we have developed several agent-based models that have explored various aspects of farming, ranging from farmers selling their land for development to that of water reuse. Keeping with this theme, we have a new paper with Kazi Ullah and Gbadebo Oladosu in the "Journal of Environmental Management" entitled "Evaluating the incentive for soil organic carbon sequestration from carinata production in the Southeast United States". 
In the paper we developed an agent-based model to evaluate what incentives might be needed for farmers to sequester soil organic carbon (SOC) when adopting a new bioenergy crop namely carinata. We simulated two carinata management scenarios: business as usual and climate-smart (no-till). The model finds that SOC sequestration incentives reduce the seed price needed to reach maximum adoption rates. While incentives lead to higher adoption rates, SOC sequestration, and profitability with no-till farming. 
If this sounds of interest, below you can read the abstract to the paper, get a sense of the agent logic and see some of the results. While at the bottom of the page, you can find the full reference and a link to the paper. The model (created in NetLogo) and data needed to run it is available on Kazi's GitHub page: https://github.com/KaziMaselUllah/Incentive_SOC_Carinata.

Abstract: Soil organic carbon (SOC) can be increased by cultivating bioenergy crops to produce low-carbon fuels, improving soil quality and agricultural productivity. This study evaluates the incentives for farmers to sequester SOC by adopting a bioenergy crop, carinata. Two agricultural management scenarios – business as usual (BaU) and a climate-smart (no-till) practice – were simulated using an agent-based modeling approach to account for farmers’ carinata adoption rates within their context of traditional crop rotations, the associated profitability, influences of neighboring farmers, as well as their individual attitudes. Using the state of Georgia, US, as a case study, the results show that farmers allocated 1056 × 103 acres (23.8%; 2.47 acres is equivalent to 1 ha) of farmlands by 2050 at a contract price of $6.5 per bushel of carinata seeds and with an incentive of $50 Mg−1 CO2e SOC sequestered under the BaU scenario. In contrast, at the same contract price and SOC incentive rate, farmers allocated 1152 × 103 acres (25.9%) of land under the no-till scenario, while the SOC sequestration was 483.83 × 103 Mg CO2e, which is nearly four times the amount under the BaU scenario. Thus, this study demonstrated combinations of seed prices and SOC incentives that encourage farmers to adopt carinata with climate-smart practices to attain higher SOC sequestration benefits.

Keywords: Agent-based model, Bioenergy, Climate-smart agriculture, Soil organic carbon, Incentives, Sustainable aviation fuel.


Process, overview and scheduling of the model

An example simulation output of a model run (SOC incentive = $50 Mg−1 CO2e, Carinata contract price = 6.5, Expanded diffusion, Low initial willingness scenario).

The total number of farmers who adopted carinata over the years for two farming scenarios at five levels of incentives for SOC sequestration and at the four price levels.

The mean land allocation area for four scenarios and their associated standard deviations (error bar).

Full Reference:  

Ullah, K.M., Gbadebo G.A., and Crooks, A.T. (2023), Evaluating the Incentive for Soil Organic Carbon Sequestration from Carinata Production in the Southeast United States, Journal of Environmental Management, 348: 119418. Available at https://doi.org/10.1016/j.jenvman.2023.119418 (pdf)

Wednesday, October 04, 2023

Leveraging newspapers to understand urban issues

In the past, this blog has explored several aspects of Detroit, such as how well its covered with Volunteered Street View Imagery or how through the use of agent-based models one can explore issues with urban shrinkage. Keeping up with the theme of shrinkage and Detroit but at the same time utilizing our growing interest in natural language processing (especially topic modeling) we (Na (Richard) Jiang, Hamdi Kavak, Wenjing Wang and myself) have a new paper entitled "Leveraging newspapers to understand urban issues: A longitudinal analysis of urban shrinkage in Detroit" published in Environment and Planning B

In the paper, we take 6794 English news articles published by national and local press organizations (e.g., Forbes, The New York Times, Newsweek, The Detroit News) between 1975 to 2021 using the keywords “Detroit”, “shrink” and “decline.” These keywords were selected based on the characteristics of the study area (i.e., Detroit) and the phenomenon of urban shrinkage. With these data we then use BERTopic to detect and classify all collected news articles into certain topics. We chose BERTopic because it captures the semantic relationship among words converting sentences and words to embedding and automatically generates the topic unlike other NLP topic modeling techniques (e.g., LDA). Our topic modeling results identify several insights with respect to Detroit's shrinkage. For example, we can detect the side effects of the 2007-2009 economic recession on Detroit's automobile industry, local employment status, and the housing market. If sounds of interest and you want to find out more, below we provide the abstract, some figures from the paper including the methodology workflow and an example of the resulting topics over time. Finally, at the bottom of the page you can see the full reference and s link to the paper itself.


Today we are awash with data, especially when it comes to studying cities from a diverse data ecosystem ranging from demographic to remotely sensed imagery and social media. This has led to the growth of urban analytics providing new ways to conduct quantitative research within cities. One area that has seen significant growth is using natural language processing techniques on text data from social media to explore various issues relating to urban morphology. However, we would argue that 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 longer-term urban problems that take decades to emerge. Concerning 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 utility of newspapers for urban analytics and to study longer-term urban issues, we utilize an advanced topic modeling technique (i.e., BERTopic) on a large number of newspaper articles from 1975 to 2021 to explore urban shrinkage in Detroit. Our topic modeling results reveal insights related to how Detroit shrinks. For example, side effects of 2007 to 2009 economic recessions on Detroit’s automobile industry, local employment status, and the housing market. 

Key Words: Natural Language Processing, Topic Modeling, Newspapers, Urban Shrinkage, Urban Analytics.


 Vacancy status change from 1970 to 2010 for city of Detroit and surrounding area.
Topic modeling work flow.
Topics over time (a) urban, (b) population, (c) shrinkage, (d) economy, (e) job, (f) house.

Full Reference:

Jiang, N., Crooks, A.T., Kavak, H. and Wang, W. (2023), Leveraging Newspapers to Understand Urban Issues: A Longitudinal Analysis of Urban Shrinkage in Detroit, Environment and Planning B. Available at https://doi.org/10.1177/23998083231204695. (pdf)

Monday, October 02, 2023

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.


Friday, September 29, 2023

Call for Abstracts: Geosimulations for Addressing Societal Challenges

As part of the The 10th Anniversary Symposium on Human Dynamics Research which will take place at the 2024 American Association of Geographers (AAG) Annual Meeting in Honolulu, Hawaii  between Tuesday, April 16 – Saturday, April 20, 2024 we are organizing a session(s) on Geosimulations for Addressing Societal Challenges. If the session description is of interest, please feel free to submit an abstract (details are below).

Session Description:

There is an urgent need for research that promotes sustainability in an era of societal challenges ranging from climate change, population growth, aging and wellbeing to that of pandemics. These need to be directly fed into policy. We, as a Geosimulation community, have the skills and knowledge to use the latest theory, models and evidence to make a positive and disruptive impact. These include agent-based modeling, microsimulation and increasingly, machine learning methods. However, there are several key questions that we need to address which we seek to cover in this session. For example, What do we need to be able to contribute to policy in a more direct and timely manner? What new or existing research approaches are needed? How can we make sure they are robust enough to be used in decision making? How can geosimulation be used to link across citizens, policy and practice and respond to these societal challenges? What are the cross-scale local trade-offs that will have to be negotiated as we re-configure and transform our urban and rural environments? How can spatial data (and analysis) be used to support the co-production of truly sustainable solutions, achieve social buy-in and social acceptance? And thereby co-produce solutions with citizens and policy makers.

We are particularly interested in presentations that will discuss issues relating to:

  • Agent-based modeling and microsimulation techniques for responding to societal challenges; Agent-based models used for policy formation;
  • Data driven modeling;
  • Utilizing machine modeling for geosimulation;
  • Creating really big models using exascale computation;
  •  Model validation and assessment; 
  • Participatory methods for agent-based modeling;
  • Approaches to connect and share (open source) data and models;
  • Revealing, quantifying, and reducing socio-economic inequalities with Geosimulation.

Next Steps:

If this sounds of interest, please e-mail the abstract and key words with your expression of intent to Richard Jiang (njiang8@buffalo.edu) by November 9th (one week before the AAG session deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at: https://aag.secure-platform.com/aag2024/page/abstracts/abstract-guidelines

An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions. 


  • 9th November, 2023: Abstract submission deadline. E-mail Richard Jiang by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 14th November, 2023: Session finalization and author notification
  • 15th November, 2023: Final abstract submission to AAG, via https://aag.secure-platform.com/aag2024/. All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Richard Jiang. Neither the organizers nor the AAG will edit the abstracts. 
  • 16th November, 2023: AAG registration deadline. Sessions submitted to AAG for approval.
  • 16th -20th April 2024: AAG in Honolulu.


Thursday, September 07, 2023

Agent-Based Modeling of Consumer Choice

At the upcoming International Conference on Geographic Information Science (GIScience 2023) Boyu Wang and myself have a new paper entitled "Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning." In the paper we explore how through mining Yelp reviews can inform an agents choices of restaurants. The model itself was created in Mesa and uses Mesa-Geo and  more details about the model can be found at https://github.com/wang-boyu/yelp-abm.  If this sounds of interest, below you can see the abstract to the paper, some fugues including the graphical user interface of the model and a link to the paper.

Abstract: People’s opinions are one of the defining factors that turn spaces into meaningful places. 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 natural language processing (NLP) 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 consumers' (i.e., agents') choices are based on their characteristics and preferences. 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: aspect-category sentiment analysis, consumer choice, agent-based modeling, online restaurant reviews.

An overview of proposed agent-based model logic.

Average star rating vs. average sentiment by aspect category for 200 randomly selected restaurants in the City of St. Louis, MO.

The prototype agent-based model (a) with simulated (b) and actual visiting patterns (c).

Full reference:

Wang, B. and Crooks, A.T. (2023), Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning, in Beecham, R., Long, J.A., Smith, D., Zhao, Q., and Wise, S (eds), Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023), Dagstuhl Publishing, Dagstuhl, Germany., pp. 81:1-81:6. (pdf)

Wednesday, August 30, 2023

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

Wednesday, June 28, 2023

Editorial: Urban analytical approaches to combating the Covid-19 pandemic

While there has been a lot written about COVID-19 Angela  Yao, Bin Jiang, Jukka Krisp, Xintao Liu, and Haosheng Huang and myself have just recently wrapped up a special issue in Environment and Planning B and how it can be studied through the lens of urban analytics.  After a call for papers for the special issue, we published 10 papers that cover a wide spectrum of analytical methods have been used to study the pandemic. These ranged from how policies impacted pedestrian patterns to how data could on the disease could be visualized along with many things in between. Below you can see papers:

Accompanying these papers is an editorial entitled "An overview of urban analytical approaches to combating the Covid-19 pandemic," In this editorial we situate these papers in the larger literature of urban analytics and Covid-19. Also in the editorial, we explore what can be learned from the current research on Covid-19 and finally we identify gaps and future research opportunities for urban analytics in combating epidemic outbreaks.

A framework of the Covid-19 pandemic dynamics in urban systems.

Covid-19 research themes and topics through the lens of geography and urban analytics.

Full Reference:

Yao, X.A, Crooks, A.T., Jiang, B., Krisp, J., Liu, X. and Huang, H. (2023), An overview of urban analytical approaches to combating the Covid-19 pandemic, Environment and Planning B, 50 (5), pp. 1133–1143. (pdf)

Saturday, May 20, 2023

Simulation & Optimization Techniques for the Mitigation of Disruptions to Supply Chains

Our last paper at the Annual Modeling and Simulation Conference (ANNSIM) is entitled "Simulation and Optimization Techniques for the Mitigation of Disruptions to Supply Chains" where we (Raj Patel, Abhisekh Rana, Sean Luke, Carlotta Domeniconi, Hamdi Kavak, Jim Jones and myself) build upon our previous work which explored how the actions of criminal networks and agents might impact supply chains. 
This paper extends this research to incorporate both disruption and mitigation modeling into the same simulation.  By using evolutionary computation optimization techniques (e.g., Covariance Matrix Adaptation Evolution Strategy) we demonstrate how we can optimize both the disruption and mitigation scenarios in a pharmaceutical supply chain (which we call PharmaSIM). Our results demonstrate how  evolutionary computation techniques could be used to not only identify worst-case disruption scenarios but to also optimize the allocation of the mitigations to counter their effects.  If this sounds of interest, below we provide the abstract to the paper, some of the figures we use to support our discussion and results. While at the bottom of the post we provide the full reference to the paper along with a link to a preprint of it. 

The COVID-19 pandemic has clearly highlighted the importance of supply chains to the function of the world economy. Moreover, the global nature of most modern supply chains along with their complexity has left them vulnerable to a wide-ranging set of disruptive scenarios. This increase in complexity has also led to a corresponding increase in disruptions to supply chains from criminal networks. In this paper, we demonstrate how a generic pharmaceutical supply chain network can be successfully modeled using discrete event simulation. We outline how disruptions by criminal networks and mitigation strategies to counter them can be effectively incorporated into the same model. Finally, we show how optimization techniques, such as evolutionary computation, can be used to not only identify worst-case disruptions and find mitigations for them, but also be used to identify mitigation strategies that are effective against a diverse set of damaging disruption scenarios.

Keywords: Simulation, Optimization, Supply Chains, Disruptions, Mitigation. 

Topology of the generic pharmaceutical supply chain (PharmaSIM) model.

Fitness after evolutionary optimization of attack configurations and corresponding safety stock allocation for different budgets.

Fitness by generation for the coevolution of attack vectors and mitigation configurations.

Full Reference:

Rana, R., Patel, R., Luke, S., Domeniconi, C., Kavak, H., Jones, J. and Crooks, A.T.  (2023), Simulation And Optimization Techniques for the Mitigation of Disruptions to Supply Chains, The Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON. (pdf)

Spiral Software Development Process for ABM

Readers of this blog might gather that we are constantly developing agent-based models to study and better understand a wide range of problems but unlike in say the software industry, agent-based model development is rather ad hoc in terms of a standardized software development process. To this end  Maxim Malikov, Fahad  Aloraini, Hamdi Kavak and William Kennedy from George Mason University and myself  have a paper entitled "Developing a Large-Scale Agent-Based Model Using the Spiral Software Development Process" which we will be presenting at the upcoming Annual Modeling and Simulation Conference (ANNSIM).

In the paper, we review the unique aspects of agent-based models and discuss the challenges faced in the development of our own large-scale agent-based model, which simulates the impact of a disaster on the infrastructure and the population of a city. This project combines the expertise of teams with multiple disciplines, and therefore must be able to adjust to novel input from these teams over the life of the project.  Furthermore, we describe our solution to these challenges in the form of a variation of the Spiral model of software development and the ways this approach helped us address the exploratory nature of agent-based modeling.  

If this sounds of interest, below we provide the abstract to the paper, some of the figures we use to support our discussion. At the bottom of the post we provide the full reference to the paper along with a link to a preprint of it. 


As the level of complexity of agent-based models grows, so does the complexity of their development. At the time of writing, the discipline of agent-based modeling does not have an established standard for the software development process to support this increasing complexity. We hope to address this need by introducing our variation of the Spiral model of software development and demonstrating an application of this process through a simple use case. We argue that the Spiral model of software development is a flexible approach that can be tailored to fit the needs of almost any project type. Further, our agent-based modeling variation of the Spiral model is an effective approach that is capable of guiding and supporting large interdisciplinary teams participating in a project, while providing sufficient flexibility to account for the uncertainty in the requirements that may arise during the development period.

Keywords: Software development, Agent-based Modeling, Spiral Development, Disaster.


Spiral model with adjustments made to account for the specifics of complex agent-based models. Adopted from Boehm (2000).

Prototype 1 of the city infrastructure simulation. This graphical user interface shows agent and infrastructure changes after a disaster.

Full reference:

Malikov, M., Aloraini, F., Crooks, A.T., Kavak, H. and Kennedy, W.G. (2023), Developing a Large-Scale Agent-Based Model Using the Spiral Software Development Process, The Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON. (pdf)

Tuesday, May 16, 2023

Modeling Forced Migration

At the upcoming Annual Modeling and Simulation Conference (ANNSIM) we have several papers being presented. One of which is with Troy Curry and Arie Croitoru entitled "Modeling Forced Migration: A System Dynamic Approach." In this paper we study how forced migration can be modeled through a systems dynamics perspective. 
To some extent this  paper builds upon our previous work on refugees especially making use new open data sources that allow us to study forced migration. Using ideas from  systems thinking which incorporates notions  non-linearity, interconnectedness, relationships, causality and feedbacks we build a systems dynamics model of the Syrian refugee crisis from January 2012 until December 2018. The model itself  explores refugee-producing variables that have been linked as determinants of forced migration including human rights violations, political violence, generalized violence, and civil war. We use these refugee-producing variables  to simulate the flow of refugees from Syria to Greece, Turkey, Lebanon and Jordan. 
If this sounds of interest, below you can read the abstract of the model, see a high-level causal loop diagram for our forced migration model along with our validation attempts  such as comparing predicted system dynamics model refugee counts vs. reference United Nations High Commissioner for Refugees (UNHCR) refugee counts. We also have included a movie of one such model scenario however,  readers can also run the model here. Finally at the bottom of the page you can find the full reference to paper along with a link to a pre-print.


Forced migration of populations is a topic of increasingly national and international importance due to security, international relations, and humanitarian considerations. Despite its importance, there has been a dearth of quantitative research to support modeling and simulation of this topic, thus hindering our ability to better understand this phenomenon. Motivated by this gap, this research leverages the recent availability of diverse set of data related to forced migration, including regime legitimacy, violence, human rights violations, conflict, socio-political mobilization, intervening opportunities, and social media. The purpose of this article is to explore the applicability and utility of open-source data in a system dynamics model to forecast population displacement, and to illustrate the benefits of using a system dynamics approach to modeling displaced population on a national and international scale. Our results suggest that this proposed approach can be used to understand such migration processes and simulate possible scenarios.

Keywords: forced migration, refugee, system dynamics, prediction model, Middle East.

High-level causal loop diagram for forced migration.

Migration routes in simulation (i.e., Greece, Turkey, Lebanon, Jordan).

Simulation refugee counts for paths to different countries (i.e., Greece, Turkey, Lebanon, Jordan).

Model validation - comparing predicted system dynamics model refugee counts vs. reference UNHCR refugee counts.

Full reference:

Curry, T., Croitoru, A. and Crooks, A.T. (2023), Modeling Forced Migration: A System Dynamic Approach, The Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON. (pdf)